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Phenotypes of Cardiovascular Diseases: Current Status and Future Perspectives

Abstract

Cardiovascular diseases (CVDs) are a large group of diseases and have become the leading cause of morbidity and mortality worldwide. Although considerable progresses have been made in the diagnosis, treatment and prognosis of CVD, communication barriers between clinicians and researchers still exist because the phenotypes of CVD are complex and diverse in clinical practice and lack of unity. Therefore, it is particularly important to establish a standardized and unified terminology to describe CVD. In recent years, there have been several studies, such as the Human Phenotype Ontology, attempting to provide a standardized description of the disease phenotypes. In the present article, we outline recent advances in the classification of the major types of CVD to retrospectively review the current progresses of phenotypic studies in the cardiovascular field and provide a reference for future cardiovascular research.

Introduction

As the leading cause of human death, cardiovascular disease (CVD) is a major public health problem worldwide, especially in developing countries (Zhou et al. 2019). The Global Burden of Cardiovascular Disease study (Roth et al. 2017) from 1990 to 2015 showed that there were an estimated 422.7 million CVD patients and 17.92 million deaths from CVD worldwide in 2015, placing a huge disease burden on humans. Therefore, it is of great social significance for the precise prevention and treatment of CVD.

However, the inconformity in the description of CVD phenotypes poses difficulties in disease diagnosis and treatment. The development of clinical medicine has a long history, and the methods used for classifying CVD phenotypes are diverse. In addition, the terminologies used to describe the phenotype of each disease were lack of unity (Allanson et al. 2009), making it difficult to communicate among clinical doctors. Furthermore, the rapid development of genomics (Schuster 2008) has enabled the identification of individual causative genes (Ng et al. 2009). However, the lack of linkage between clinical phenotypes and genomic data affects the precise diagnosis and treatment of human diseases (Bilder et al. 2009; Freimer and Sabatti 2003), and a unified and internationally accepted terminology should be established to describe disease phenotypes (Allanson et al. 2009).

It is particularly important to construct a set of terminologies for the precise description of disease phenotypes. The accurate description of disease phenotypes provides standards for accurate diagnosis, individualized treatment and prognosis evaluation of CVD. Some studies have systematically described diseases from multiple perspectives, such as clinical manifestations, pathologies and genotypes. Human Phenotype Ontology (HPO) provides a structured term set to give a precise description of disease phenotypes, facilitating the clinical doctors and testing organizations to have the easy access to the disease databases (Fig. 1). HPO has provided great convenience for the basic data analysis, diagnosis and treatment of clinical diseases,  and it has become a paradigm for the description of disease phenotypes.

Fig. 1
figure1

Application of HPO in clinical testing, genetic testing and databases. Clinical doctors analyze and summarize the clinical phenotypes of patients using HPO to describe the typical phenotypes of patients. Testing institutions analyze the biological information of patients through genetic testing, and they evaluate the pathogenicity of genes and analyze the phenotypic similarity of patients. The diseases and biological databases are connected through phenotypes to ensure that the description of clinical and scientific phenotypes is as uniform as possible, thereby making scientific research accurate enough for clinical applications

In this review, we describe the current progresses and shortcomings of phenotypic classification in CVD underlying the precise description of CVD in the future, in which the accurate and uniform description of the disease phenotypes is conducive to the definite diagnosis, prognosis evaluation and individualized treatment of CVD.

Phenotypic Classification of Valvular Disease

Mitral Valve Prolapse (MVP)

MVP is a common disease that affects 2–3% of the world's population (Freed et al. 1999). MVP is characterized by an abnormal bulging of the mitral valve leaflets into the left atrium during ventricular systole (Guy and Hill 2012). MVP is the leading cause of primary mitral valve surgery (Delling et al. 2016).

Carpentier first classified MVP by the mitral value prolapse site (Carpentier 1983). According to whether MVP is accompanied by systemic diseases, MVP is further divided into two categories as follows: nonsyndromic MVP and syndromic MVP. Syndromic MVP is accompanied by a variety of systemic diseases and is considered as a systemic disease. Nonsyndromic MVP is currently considered to contain three subtypes. First, the fibroelastic deficiency (FED)-related MVP subtype mostly affects the elderly, which is considered as a kind of degeneration disease. The leaflets of the mitral valve are thin, translucent and of normal length, and the mitral annulus is mildly dilated. Mitral regurgitation occurs due to chordal rupture, and only the prolapsing or flail segment is thickened and moderately elongated (Le Tourneau et al. 2018a, b). Pathological studies have confirmed the absence of associated elastic fibers in the mitral valve of FED-related MVP patients. Second, the myxoma-related MVP (Barlow's disease) subtype is characterized by lengthy thickening of both the anterior and posterior leaflets of the mitral valve, and it is accompanied by mitral annulus disjunction (MAD) (Putnam et al. 2020). Pathological performance suggests excessive proliferation and activation of valvular interstitial cells and increased content of type III collagen. Thirdly, the FLNA-related MVP subtype is an X-associated sex chromosome inherited disease that is much severer in males than in females. This subtype is characterized by lengthy thickening of mitral valve leaflets, and it is seldom accompanied by MAD. In addition, MVP with arrhythmia has gradually attracted the attention of the scientific community in recent years. This type of patient features myxomatosis of the bileaflet and mitral annular separation, and ventricular arrhythmias and sudden death are the major symptoms of these patients. Furthermore, the pathological performance of MVP with arrhythmia is also characterized by fibrosis of the papillary muscle and basal inferior wall in the left ventricle (Del Pasqua et al. 2009).

Although the classification of MVP is currently widely recognized, there are still some shortcomings. First, different types of MVPs are not independent, and the current classifications do not solve these problems. For example, FLNA-related MVP has also been reported to have mucinous degeneration, and there are significant continuous changes from FED to Barlow's disease. Second, genetic studies of MVP are insufficient. Third, the application of imaging techniques to identify different subtypes of MVP is still immature. In future work, we should standardize the classification of MVP by improving the quality of imaging data and integrating different pathophysiological characteristics.

Tricuspid Valve Malformation

Tricuspid valve malformation (also called Ebstein’s anomaly) is a rare congenital heart disorder that accounts for approximately 1% of congenital heart diseases (Attenhofer Jost et al. 2007). There are three classification methods of Ebstein’s anomaly with different perspectives.

Carpentier classification of Ebstein’s anomaly is a straightforward classification method (Carpentier et al. 1988), and it divides Ebstein’s anomaly into four types: (1) Type A, the functional right ventricle volume is appropriate; (2) Type B, the atrialized portion of the right ventricle is large, but the anterior leaflet moves freely; (3) Type C, the anterior leaflet is severely restricted in motion and can obstruct the right ventricular outflow tract; and (4) Type D, the right ventricle is almost entirely atrialized, except for a small infundibular segment. This classification method reveals the specific pathophysiological features of different types. Celermajer classification is mainly based on the ratio of the combined area of the right atrium and atrialized right ventricle to that of the functional right ventricle and left heart in a four-chamber view at end-diastole (Celermajer et al. 1994), and it classifies this disease severity into four grades as follows: Grade 1, ratio < 0.5; Grade 2, ratio 0.5–1; Grade 3, ratio 1–1.5; and Grade 4, ratio ≥ 1.5. Both the ratio > 1.5 in adults and the ratio > 1.0 in neonates have a fatality rate of 100%. This classification method is the guideline for risk stratification of Ebstein’s anomaly patients.

In 2020, Zhou's team annotated Ebstein’s anomaly by the standard vocabulary of abnormal phenotypes in HPO (Tang et al. 2020), and they classified Ebstein’s anomaly into the three following subtypes: (1) pulmonary hypertension but without patent foramen ovale; (2) no pulmonary hypertension and rarely with Wolff-Parkinson-White syndrome or ventricular septal defect; and (3) no pulmonary hypertension or ventricular septal defect but with Wolff-Parkinson-White syndrome. Their study also analyzed the relationship between HPO-based reclassification and adverse clinical outcomes. Classification of Ebstein’s anomaly based on HPO annotation is helpful for clinical diagnosis and treatment, and it provides an essential reference for investigating the classification of other congenital heart diseases.

Bicuspid Aortic Valve (BAV)

BAV is the most common congenital heart malformation with an incidence of 1–2% in the population (Hoffman and Kaplan 2002). The normal aortic valve consists of three equal-sized semilunar leaflets, whereas patients with BAV present with two unequal-sized leaflets due to congenital leaflet dysplasia (Hoffman and Kaplan 2002). BAV leads to more than 50% death and complications of congenital heart diseases.

There have been many studies on the classification of BAVs (Angelini et al. 1989; Jilaihawi et al. 2016; Roberts 1970; Sabet et al. 1999). Currently, the most widely used method is the Sievers classification (Sievers and Schmidtke 2007). In this classification, BAV is divided into the following three types: (1) Type 0, valves with no raphe; (2) Type 1, valves with one raphe; and (3) Type 2, valves with two raphes. This classification method provides a particular reference for the clinical treatment of BAVs, in which artificial valve implantation and Ross surgery can be performed for patients with type 1 variants. For patients with type 0 variants, surgery is more challenging because a round rather than fan-shaped proximal suture method is recommended. For patients with type 2 variants, the incidence of ascending aortic aneurysms is significantly increased.

Another method is the Brussels classification (de Kerchove et al. 2019). Compared to the Sievers classification, Brussels classified BAVs into the following three types according to two normally developed junctional angles of BAVs: (1) Type A, symmetrical BAV of 180°–160°; (2) Type B, asymmetrical BAV of 159°–140°; and (3) Type C, very asymmetrical BAV of 139°–120°. Different types of variants require different repair methods. For type A patients, central plication or direct closure of the non-fused portion of the raphe is needed. For type B patients, a thin raphe is also required in addition to direct closure. For type C patients, plication of the residual cusp component and generation of a functional commissure at the raphe or pericardial patch or type B repair are required. However, these two classifications only focus on single sights of BAVs, which does not meet clinical needs. Future studies should focus on proposing a comprehensive classification to guide the treatment of BAV.

According to whether the patients present other systemic diseases, BAVs can be divided into two types as follows: syndromic BAVs and nonsyndromic BAVs. The current understanding of the phenotypes and related genes of syndromic BAV is quite clear (Table 1). However, nonsyndromic BAV is a genetic disease caused by the interaction of many different genes with a complex pedigree inheritance (Siu and Silversides 2010). At the genetic level, BAV is a heterogeneous disorder inherited in an autosomal dominant pattern with incomplete penetrance and variable expressivity (Cripe et al. 2004; Prakash et al. 2014). The prevalence of BAV in first-degree family members is tenfold higher than that in the general population. Additionally, pedigree-based genome-wide linkage analysis has revealed that the risk ratio of recurrence of BAV in families with hypoplastic left heart syndrome (HLHS) is similar to that in families with BAV, providing evidence for a genetic relationship between HLHS and BAV (Hinton et al. 2009).

Table 1 Genetic abnormalities in syndromic BAV

Although many studies have been conducted on the classification and etiology of BAV during the past 20 years, there is still no established unified classification scheme covering all patients to completely illustrate the heterogeneity of clinical manifestations of BAV. To establish a perfect classification in the future, the phenotype; and genotype should be correlated to group BAVs into different subtypes. This phenotype-genotype classification will assist in the clinical diagnosis and treatment of BAVs.

Phenotypic Classification of Cardiomyopathy

Cardiomyopathy is a group of heterogeneous myocardial diseases (Dadson et al. 2017). Cardiomyopathy is caused by cardiac mechanical and electrical activity abnormalities, and it presents with inappropriate ventricular hypertrophy or dilation. Severe cardiomyopathy can cause cardiovascular death or progressive heart failure.

As early as 1980, the World Health Organization (WHO) (1980) proposed the first classification of cardiomyopathy, which classified cardiomyopathy into dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM) and hypertrophic cardiomyopathy (HCM). In 1996, the WHO/ISFC (Richardson et al. 1996) classified cardiomyopathy into primary cardiomyopathy and specific cardiomyopathy. This classification method is more systematic, incorporating the newly discovered arrhythmogenic right ventricular cardiomyopathy (ARVC) and standardizing the classification of specific cardiomyopathy simultaneously. However, there are contradictions in the classification method of primary cardiomyopathy, and the classification of cardiomyopathy remains unclear.

With the rapid development of cardiac molecular genetics and a thorough understanding of the pathogenesis of cardiomyopathy, the American Heart Association (AHA) (Maron et al. 2006) has divided cardiomyopathy into the following two types: primary cardiomyopathy and secondary cardiomyopathy. Primary cardiomyopathies are those solely or predominantly confined to heart muscle and are relatively few in number, and they are further divided into three types. Secondary cardiomyopathies show pathological myocardial involvement as part of a large number and variety of generalized systemic disorders. This classification expands the scope of cardiomyopathy, which states that electrophysiological abnormalities other than structural abnormalities should also be included. This classification also emphasizes genetic etiology and the importance of genetic testing. Subsequently, the European Society of Cardiology (ESC) Cardiomyopathy and Pericardial Disease Working Group issued a related statement on the 1995 WHO/ISFC (Elliott et al. 2008), which classified cardiomyopathy into five types (Fig. 2). This classification discards the initial category of secondary cardiomyopathy and rejects that pure electrical disorder is cardiomyopathy, and this classification excludes ion channel diseases and conduction system diseases from cardiomyopathy. In 2013, Elliott et al. proposed comprehensive diagnosis and described cardiomyopathy patients by the following four aspects: cardiac morphology, affected organs, genetic patterns and etiology (Elliott 2013). This classification disregards the limitation that only clinical phenotypes are used to describe cardiomyopathy and provides a multiangle classification for cardiomyopathy patients combing with clinical phenotypes and genotypes. However, the clinical applicability of this classification is still not clear.

Fig. 2
figure2

Cardiomyopathy classification proposed by the ESC. The ESC divides cardiomyopathy into the following five types: HCM, DCM, ARVC, RCM and indeterminate cardiomyopathy. The five types are further divided into familial/hereditary cardiomyopathy and nonfamilial/nonhereditary cardiomyopathy according to whether the disease is hereditary or not

Many studies have intensively investigated and classified the subclasses of cardiomyopathy. In 1997, Maron classified HCM into the following four types according to the location of ventricular hypertrophy (Maron 1997): (1) Type I, anterior ventricular septal hypertrophy; (2) Type II, anterior and posterior septal hypertrophy; (3) Type III, hypertrophy of both the interventricular septum and the anterolateral wall of the left ventricle; and (4) Type IV, hypertrophy involving the posterior septum and/or lateral wall of the left ventricle or may only involve the apex without thickening of the anterior septum and the inferior (posterior) wall of the left ventricle. According to Maron’s classification, type III, accounting for 52% HCM, is the most common subtype, while type IV is the least common. This classification method classifies HCM from anatomical abnormalities but does not involve cardiac function. Subsequently, in 2014, ACC/ESC clinical expert consensus classified patients with HCM into obstructive HCM (left ventricular outflow tract pressure gradient, LVOGT ≥ 30 mmHg at rest), nonobstructive HCM (LVOGT < 30 mmHg at rest or during stress exercise) and occult obstructive HCM (normal LVOTG at rest and LVOGT ≥ 30 mmHg during stress exercise) according to the peak pressure gradient between the left ventricular outflow tract gradient and aorta (LVOTG) measured by echocardiography (Elliott et al. 2014). This classification highlights the cardiac function of HCM patients and has guiding significance for clinical diagnosis and treatment. It is found that the presence of multiple rare variants in sarcomeric genes is a risk factor for the malignant outcome of HCM, which may be considered as a criterion for risk stratification of HCM patients (Wang et al. 2014), providing novel ideas for predicting the prognosis of HCM.

In recent years, genotypic and hemodynamic studies have enriched HCM phenotypes. Myofilament mutations (MYBP3, MYH7, TNNI3, TNNT2, TPM1 and MYL2), Z-disc mutations (LBD3, ACTN2 and ANKRD1), and calcium-handing mutations (JPH2 and PLN) have been suggested to be associated with HCM (Geske et al. 2018). Regarding hemodynamic studies, Martinez-Legazpi et al. used diastolic vortex analysis to elucidate the mechanism of insufficient cardiac output in HCM patients and found that the ventricles of HCM patients were lack of vortex, resulting in poor filling (Martinez-Legazpi et al. 2014). In addition, HCM is often accompanied by systolic anterior motion (SAM). The subvalvular pressure gradient has been suggested to be used as a reliable diagnostic indicator of SAM (Meschini et al. 2021). When this quantity attains the value of 30 mmHg, SAM of the mitral leaflets is observed, and when this threshold is exceeded, the SAM becomes obstructive. This study provides a novel idea for the diagnosis of patients with clinically obstructive HCM.

In 2020, Verdonschot et al. reclassified DCM into four categories by integrating features, such as etiology, clinical comorbidities, cardiac function and transcriptomics (Verdonschot et al. 2020), which revealed the molecule-specific differences and corresponding pathogenesis of DCM patients. Subsequently, these researchers found that different DCM subtypes have different risks of clinical outcomes. Patients with type 1 disease have the best prognosis, while patients with type 3 disease who carry multiple arrhythmia-related gene variants, such as LMNA and TNN, have the worst prognosis. These findings lay a foundation for precise individualized treatment of DCM.

In terms of the classification of ARVC, Hu’s team used an unsupervised core clustering algorithm that included clinical features, histopathology and gene mutation characteristics to classify ARVC into four types as follows (Chen et al. 2019): Cluster 1 is characterized by the variation of DSG2, PKP2 and DSC2; Cluster 2 is characterized by the variation of PLN, LMNA, DES, TMEM43 and CTNNA3; Cluster 3 has mutations in DSP, PLN, CTNNA3 and other unknown mutations; Cluster 4 has unknown gene mutations. These researchers also predicted and validated the prognosis of patients using the new classification, and they suggested that targeted treatment should be performed for different types of patients. In addition, the precordial QRS voltage predicts the residual myocardium in the right ventricle, which effectively predicts death and the need for heart transplantation. Thus, the accurate classification of ARVC has been established internationally for the first time. The relationship between genotypes and phenotypes is analyzed based on a complete description of the ARVC phenotypes, which also provides a reference for classifying other types of cardiomyopathies in the future.

Although some meaningful genetic mutations are associated with RCM (Muchtar et al. 2017), no classification method clearly defines the disease due to its diverse etiology. Further investigations should be considered to combine gene mutations, clinical phenotypes and pathology to classify cardiomyopathy in detail. Moreover, the differences in the prognosis of different types should also be explored through long-term follow-up. Together, these studies may provide a reference for clinical diagnosis, treatment and prognosis towards precision medicine.

Phenotypic Classification of Heart Failure (HF)

HF is a common clinical syndrome with high morbidity and mortality. According to the 2016 ESC guidelines (Ponikowski et al. 2016), HF patients were divided into three types according to the left ventricular ejection fraction (LVEF) as follows: HF with reduced ejection fraction (HFrEF, LVEF ≤ 40%), HF with mid-range ejection fraction (HFmrEF, 40% < LVEF < 50%) and HF with preserved ejection fraction (HFpEF, LVEF ≥ 50%). In 2021, an update for the classification of HF was proposed with improved ejection fraction (HFimpEF, symptomatic HF with a baseline LVEF ≤ 40%, a ≥ 10-point increase from baseline of LVEF and a second measurement of LVEF > 40%) as a new type of HF (Bozkurt et al. 2021). Furthermore, the researchers revised the stages of HF into the following four stages: at risk for HF (Stage A), pre-heart failure (Stage B), HF (Stage C) and advanced HF (Stage D). These studies provide clear criteria for the classification and staging of HF. Nevertheless, the measurement of LVEF is limited by methods, operators and calculation (Fonarow 2017), making it challenging to use LVEF to accurately define patients with four types of HF.

Compared to classifying HF into different types, it has been proposed that HF is a progressive, multifactorial and complex syndrome with significant heterogeneity, and HF is considered a syndrome across a spectrum of phenotypes (Triposkiadis et al. 2019). Each HF phenotype is a result of a specific trajectory of patients. The trajectory path depends on the risk factors, combining diseases, sex, age and other characteristics of patients. Stratifying HF patients with different biological characteristics guides the accurate application of existing treatment technologies and the development of novel therapies for HF.

The treatment of HFrEF has progressed rapidly. However, due to the complex phenotypes and pathophysiological mechanisms of HFpEF, the same treatment regimen is unlikely to meet the needs of all kinds of HF patients, and no significant progress has been made in clinical trials. Therefore, it is essential to classify HFpEF patients accurately. In 2012, Shah and Pfeffer suggested to perform phenotypic analysis of HFpEF and conduct targeted treatments for different phenotypic subgroups (Shah and Pfeffer 2012). However, the proposed classification of HFpEF is complicated, limiting its clinical promotion (Shah et al. 2014a, b). In 2020, Ge proposed to divide HFpEF into five categories in which the patients of each category present similar pathophysiological changes (Ge 2020). Compared to the previous classifications, this classification is helpful to understand the risk factors, etiology, pathophysiology and clinical course of HFpEF, and it also facilitates the targeted and individualized treatment of patients with different etiologies in clinical work. It is expected that randomized clinical trials will be conducted in HFpEF patients of the same or main etiology to test the therapeutic effects of different therapeutic principles on HFpEF subtypes. In addition, considering the difficulties in obtaining the pathological tissues, the clinical characteristics, imaging findings and genotype changes can be synthesized to achieve the accurate classification of HF patients in the future.

Phenotypic Classification of Vascular Diseases

Acute Coronary Syndrome (ACS)

ACS is a group of clinical syndromes caused by acute myocardial ischemia. Based on the characteristics of ST-segment elevation in the electrocardiogram, ACS is classified into ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA). Although this approach is still applicable, it does not fully elucidate the pathophysiological mechanism of ACS.

In 2017, it was proposed that ACS can be classified into the following four categories through the pathophysiological characteristics of atherosclerotic plaques: plaque rupture with an inflammatory response, plaque rupture without an inflammatory response, plaque rupture caused by plaque erosion and ACS without thrombosis (Crea and Libby 2017). Different therapeutic strategies have been suggested for the subtypes of ACS. For example, for plaque rupture with an inflammatory response, treatment should be focused on reducing the production of inflammatory factors and regulating the function of immune cells (Morton et al. 2015; Shah et al. 2014a, b). Inhibiting the formation of cholesterol crystals should be considered unless plaque rupture is associated with an inflammatory response (Abela et al. 2011; Meuwese et al. 2009; Zimmer et al. 2016). When a plaque rupture caused by plaque erosion is encountered, treatments, including reduction of blood triglyceride levels, anticoagulants and antiplatelet therapy, should be considered (Jia et al. 2017; Libby 2015; Mangold et al. 2015). Furthermore, using vasodilators to reduce coronary spasms is an effective treatment to cure ACS patients without thrombosis. This classification method allows clinicians to apply proper treatments to different ACS patients. In addition to inflammation, plaque composition is also proposed to be associated with myocardial ischemia (Gaur et al. 2016). With simple stenosis assessment, the addition of plaque assessment is more conducive to the assessment of myocardial ischemia, which may further facilitate the accurate classification of coronary heart disease.

Although there have been remarkable progresses in the classification of ACS, the clinical manifestations and pathophysiological mechanisms of ACS patients still need to be combined. In subsequent work, it is necessary to develop biological and imaging markers that reflect the mechanism of ACS and verify them in clinical practice. Thus, a strict clinical evaluation for the targeted therapy of ACS with different classifications can be performed, which is beneficial to achieve precision medicine.

Aortic Dissection (AD)

AD is a relatively rare disease with a poor prognosis. AD has a high mortality rate, especially when treatment is delayed. If AD occurs in the ascending aorta, 40% of patients die immediately, and the mortality rate increases by 1–2% per hour. The mortality rate is approximately 50% at 48 h after the occurrence of AD (Hirst et al. 1958).

According to the onset time, AD can be divided into three categories as follows: acute (< 14 days), subacute (15–90 days) and chronic (> 90 days). This classification method has guiding significance for predicting the prognosis of patients. The mortality of untreated acute AD increases with time. In chronic AD, there is a strong scar formation between the outer wall of the dissection and the surrounding tissue, and the outer wall of the dissection can mostly withstand aortic pressure. However, the classification method lacks a pathological description. DeBakey and colleagues proposed classifying AD into three types according to the extent of the aorta involved in dissection (Debakey et al. 1965). Reul et al. were the first to subdivide the DeBakey classification, distinguishing it from thoracoabdominal dissections (Reul et al. 1975). Daily et al. from Stanford University proposed another classification method mainly based on the location of the intimal cleft (Daily et al. 1970). Daily and colleagues divided AD into two categories, namely, proximal AD (Stanford A dissection) and distal AD (Stanford B dissection), bounded by the origin of the left subclavian artery and further subdivided it into different subcategories according to aortic arch lesions. This classification method is valuable for guiding treatment options. Most patients with acute AD die from pericardial tamponade due to rupture of the ascending aorta. Therefore, emergency surgery should be performed to replace the ascending aorta and/or aortic arch with the greatest risk of rupture for acute AD. For chronic AD, surgical treatment is also recommended. In contrast, endograft therapy or medical treatment is the preferred treatment for acute distal AD, and surgical treatment is considered only in patients with failed endograft therapy, medical treatment or complications. In addition, this classification has guiding significance for distinguishing acute and chronic AD; Stanford A dissection rarely converts to chronic AD, while Stanford B dissection often presents with arterial dilatation, fibrosis and other states with drug treatment (Peterss et al. 2016). In 2016, Urbanski and Wagner proposed non-A non-B AD (Urbanski and Wagner 2016). The dissection is limited to the aortic arch or can be described as a retrograde dissection arising from the descending aorta that extends into the arch and stops before the ascending aorta. These researchers proposed that compared to conservative treatment, emergency surgery may offer improved clinical outcomes through the follow-up of patients. This classification complements the traditional AD classification and guides the clinical treatment of non-A non-B AD.

For chronic distal AD, thoracoabdominal AD is divided into five types according to the involvement range of dissection (Crawford et al. 1978). After the initial classification of thoracoabdominal AD, type V (only renal artery involvement) was removed, changing the classification to four types (Crawford et al. 1986). The main classification is determined by the relationship between AD and the anatomical location of the left subclavian artery, diaphragm and visceral artery. This classification is helpful to guide the prevention and treatment of complications, such as paraplegia during chronic distal AD, because most of the main blood vessels supplying the spinal cord originate from the distal descending thoracic aorta and the proximal descending abdominal aorta. However, the Crawford classification does not clearly define the “distal descending aorta”. Safi used T6 as an anatomical marker for the upper and lower portions of the descending aorta according to the Crawford classification, adding a V-type thoracoabdominal AD (Safi 1999), further refining the content of the Crawford classification.

Previous studies have only considered the phenotypes of AD that has already occurred,  however, the premonitory phenotypes of AD, such as intramural hemorrhagic hematoma and transmural aortic ulcer, are not described in these studies. Considering the factors above, the ESC proposed a new classification criterion that classifies AD into five categories (Erbel et al. 2001). This classification emphasizes the precancerous lesions of AD and provides new ideas for early clinical detection and intervention.

At present, the classification of AD is still based on the site of dissection, and the classification criteria for different sites are not consistent. In subsequent investigations, the phenotypic and genotypic abnormalities of AD should be considered in the classification. A comprehensive and unified classification will provide a reference for the clinical diagnosis and treatment of AD.

Summary and Perspectives

Over the past two centuries, great progresses in CVD treatment have been achieved by the intervention of risk factors and the development of evidence-based medicine (Leopold and Loscalzo 2018). However, the prevention and treatment of CVD are still suboptimal due to the wide heterogeneity of clinical manifestations in CAD patients. The description of phenotypes among individuals with the same disease is imprecise, leading to the lack of individualized treatment for patients (Antman and Loscalzo 2016). Therefore, there is an urgent need for precise phenotyping of CVD. Several studies have disregarded the traditional situation of classifying CVD with a single clinical phenotype. Researchers have tried to describe CVD from multiple perspectives, such as clinical manifestations, pathology and imaging, further revealing the features of the disease and interindividual heterogeneity. However, there are still many improvements that need to be achieved in the description of CVD phenotypes (Table 2). In addition, most of the classification methods currently proposed are based on single-center studies and limited by the sample size. Multicenter validation is required before these concepts can be generalized.

Table 2 Current progress and suggested improvements in the descriptions of CVD phenotypes

With significant progresses in genetic research, the molecular etiology of CVD has gradually become clear. Genotypes are an indispensable part of the phenotypes of CVD. These advances provide novel insights into CVD manifestations and help to define the molecular pathophysiological basis for disease development. Combining the genotypes and clinical phenotypes can precisely group affected patients according to the risk stratification, which is helpful to identify the individuals who are at high risk for developing CVD but still present no obvious evidence of diseases. Although the current understanding of CVD genotypes is imperfect, the development of new genomics technologies provides unparalleled opportunities to fully explore the genetic architectures of different CVDs. The continuous improvement of genomics research on CVD is essential to the disease phenome providing the support for precision medicine.

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Funding

This study was supported by Shanghai Municipal Science and Technology Major Project (2017SHZDZX01).

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Correspondence to Jiangping Song.

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HZ performed the literature survey and wrote the manuscript, XH corrected the manuscript, JS provided the theme and guided the manuscript writing.

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Zhang, H., Hua, X. & Song, J. Phenotypes of Cardiovascular Diseases: Current Status and Future Perspectives. Phenomics (2021). https://doi.org/10.1007/s43657-021-00022-1

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Keywords

  • Cardiovascular disease
  • Phenotype
  • Classification
  • Therapy