Examples of non ACS-causes of elevated troponin levels:
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
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Materials",slug:"application-of-artificial-neural-networks-in-the-estimation-of-mechanical-properties-of-materials",signatures:"Seyed Hosein Sadati, Javad Alizadeh Kaklar and Rahmatollah Ghajar",authors:[{id:"1740",title:"PhD.",name:"Seyed Hosein",middleName:null,surname:"Sadati",fullName:"Seyed Hosein Sadati",slug:"seyed-hosein-sadati"},{id:"23140",title:"Prof.",name:"Javad Alizadeh",middleName:null,surname:"Kaklar",fullName:"Javad Alizadeh Kaklar",slug:"javad-alizadeh-kaklar"},{id:"23141",title:"Dr.",name:"Rahmatollah",middleName:null,surname:"Ghajar",fullName:"Rahmatollah Ghajar",slug:"rahmatollah-ghajar"}]},{id:"14732",title:"Optimum Design and Application of Nano-Micro-Composite Ceramic Tool and Die Materials with Improved Back Propagation Neural Network",slug:"optimum-design-and-application-of-nano-micro-composite-ceramic-tool-and-die-materials-with-improved-",signatures:"Chonghai Xu, Jingjie Zhang and Mingdong Yi",authors:[{id:"23577",title:"Prof.",name:"Chonghai",middleName:null,surname:"Xu",fullName:"Chonghai Xu",slug:"chonghai-xu"},{id:"23644",title:"PhD.",name:"Jingjie",middleName:null,surname:"Zhang",fullName:"Jingjie Zhang",slug:"jingjie-zhang"},{id:"23645",title:"PhD.",name:"Mingdong",middleName:null,surname:"Yi",fullName:"Mingdong Yi",slug:"mingdong-yi"}]},{id:"14733",title:"Application of Bayesian Neural Networks to Predict Strength and Grain Size of Hot Strip Low Carbon Steels",slug:"application-of-bayesian-neural-networks-to-predict-strength-and-grain-size-of-hot-strip-low-carbon-s",signatures:"Mohammad Reza Toroghinejad and Mohsen Botlani Esfahani",authors:[{id:"22485",title:"Dr.",name:"Mohammad Reza",middleName:null,surname:"Toroghinejad",fullName:"Mohammad Reza Toroghinejad",slug:"mohammad-reza-toroghinejad"},{id:"22806",title:"Mr.",name:"Mohsen",middleName:null,surname:"Botlani Esfahani",fullName:"Mohsen Botlani Esfahani",slug:"mohsen-botlani-esfahani"}]},{id:"14734",title:"Adaptive Neuro-Fuzzy Inference System Prediction of Calorific Value Based on the Analysis of U.S. Coals",slug:"adaptive-neuro-fuzzy-inference-system-prediction-of-calorific-value-based-on-the-analysis-of-u-s-coa",signatures:"F. Rafezi, E. Jorjani and Sh. Karimi",authors:[{id:"22037",title:"Associate Prof.",name:"Esmaeil",middleName:null,surname:"Jorjani",fullName:"Esmaeil Jorjani",slug:"esmaeil-jorjani"},{id:"23282",title:"Mr.",name:"Farid",middleName:null,surname:"Rafezi",fullName:"Farid Rafezi",slug:"farid-rafezi"},{id:"23283",title:"Mr.",name:"Shahab",middleName:null,surname:"Karimi",fullName:"Shahab Karimi",slug:"shahab-karimi"}]},{id:"14735",title:"Artificial Neural Network Applied for Detecting the Saturation Level in the Magnetic Core of a Welding Transformer",slug:"artificial-neural-network-applied-for-detecting-the-saturation-level-in-the-magnetic-core-of-a-weldi",signatures:"Klemen Deželak, Gorazd Štumberger, Drago Dolinar and Beno Klopčic",authors:[{id:"12968",title:"Prof.",name:"Gorazd",middleName:null,surname:"Štumberger",fullName:"Gorazd Štumberger",slug:"gorazd-stumberger"},{id:"12970",title:"Dr.",name:"Drago",middleName:null,surname:"Dolinar",fullName:"Drago Dolinar",slug:"drago-dolinar"},{id:"20832",title:"PhD.",name:"Klemen",middleName:null,surname:"Deželak",fullName:"Klemen Deželak",slug:"klemen-dezelak"},{id:"25127",title:"Dr.",name:"Beno",middleName:null,surname:"Klopčič",fullName:"Beno Klopčič",slug:"beno-klopcic"}]},{id:"14736",title:"Application of Artificial Neural Networks to Food and Fermentation Technology",slug:"application-of-artificial-neural-networks-to-food-and-fermentation-technology",signatures:"Madhukar Bhotmange and Pratima Shastri",authors:[{id:"22978",title:"Dr.",name:"Pratima",middleName:null,surname:"Shastri",fullName:"Pratima Shastri",slug:"pratima-shastri"}]},{id:"14737",title:"Application of Artificial Neural Networks in Meat Production and Technology",slug:"application-of-artificial-neural-networks-in-meat-production-and-technology",signatures:"Maja Prevolnik, Dejan Škorjanc, Marjeta Čandek-Potokar and Marjana Novic",authors:[{id:"19653",title:"Dr.",name:"Maja",middleName:null,surname:"Prevolnik",fullName:"Maja Prevolnik",slug:"maja-prevolnik"},{id:"23159",title:"Dr.",name:"Marjana",middleName:null,surname:"Novič",fullName:"Marjana Novič",slug:"marjana-novic"},{id:"23160",title:"Dr.",name:"Dejan",middleName:null,surname:"Škorjanc",fullName:"Dejan Škorjanc",slug:"dejan-skorjanc"},{id:"23161",title:"Dr.",name:"Marjeta",middleName:null,surname:"Čandek-Potokar",fullName:"Marjeta Čandek-Potokar",slug:"marjeta-candek-potokar"}]},{id:"14738",title:"State of Charge Estimation of Ni-MH battery pack by using ANN",slug:"state-of-charge-estimation-of-ni-mh-battery-pack-by-using-ann",signatures:"Chang-Hao Piao, Wen-Li Fu, Jin-Wang, Zhi-Yu Huang and Chongdu Cho",authors:[{id:"22909",title:"Prof.",name:"Chang Hao",middleName:null,surname:"Piao",fullName:"Chang Hao Piao",slug:"chang-hao-piao"}]},{id:"14739",title:"A Novel Frequency Tracking Method Based on Complex Adaptive Linear Neural Network State Vector in Power Systems",slug:"a-novel-frequency-tracking-method-based-on-complex-adaptive-linear-neural-network-state-vector-in-po",signatures:"M. Joorabian, I. Sadinejad and M. Baghdadi",authors:[{id:"22325",title:"Dr.",name:"Mahmood",middleName:null,surname:"Joorabian",fullName:"Mahmood Joorabian",slug:"mahmood-joorabian"},{id:"22326",title:"PhD.",name:"Mehdi",middleName:null,surname:"Baghdadi",fullName:"Mehdi Baghdadi",slug:"mehdi-baghdadi"},{id:"22959",title:"PhD.",name:"Iman",middleName:null,surname:"Sadinezhad",fullName:"Iman Sadinezhad",slug:"iman-sadinezhad"}]},{id:"14740",title:"Application of ANN to Real and Reactive Power Allocation Scheme",slug:"application-of-ann-to-real-and-reactive-power-allocation-scheme",signatures:"S.N. Khalid, M.W. Mustafa, H. Shareef and A. Khairuddin",authors:[{id:"10447",title:"Dr.",name:"Hussain",middleName:null,surname:"Shareef",fullName:"Hussain Shareef",slug:"hussain-shareef"},{id:"21079",title:"Dr.",name:"Saifulnizam",middleName:null,surname:"Abd. Khalid",fullName:"Saifulnizam Abd. Khalid",slug:"saifulnizam-abd.-khalid"},{id:"21187",title:"Dr.",name:"Mohd Wazir",middleName:null,surname:"Mustafa",fullName:"Mohd Wazir Mustafa",slug:"mohd-wazir-mustafa"},{id:"21188",title:"Dr.",name:"Azhar",middleName:null,surname:"Khairuddin",fullName:"Azhar Khairuddin",slug:"azhar-khairuddin"}]},{id:"14741",title:"The Applications of Artificial Neural Networks to Engines",slug:"the-applications-of-artificial-neural-networks-to-engines",signatures:"Deng, Jiamei, Stobart, Richard and Maass, Bastian",authors:[{id:"21980",title:"Dr.",name:"Jiamei",middleName:null,surname:"Deng",fullName:"Jiamei Deng",slug:"jiamei-deng"},{id:"22750",title:"Dr.",name:"Richard",middleName:null,surname:"Stobart",fullName:"Richard Stobart",slug:"richard-stobart"},{id:"22751",title:"PhD.",name:"Bastian",middleName:null,surname:"Maass",fullName:"Bastian Maass",slug:"bastian-maass"}]},{id:"14742",title:"A Comparison of Speed-Feed Fuzzy Intelligent System and ANN for Machinability Data Selection of CNC Machines",slug:"a-comparison-of-speed-feed-fuzzy-intelligent-system-and-ann-for-machinability-data-selection-of-cnc-",signatures:"Zahari Taha and Sarkawt Rostam",authors:[{id:"858",title:"Dr.",name:"Zahari",middleName:null,surname:"Taha",fullName:"Zahari Taha",slug:"zahari-taha"},{id:"24305",title:"PhD.",name:"Sarkawt Rostam",middleName:null,surname:"Hassan",fullName:"Sarkawt Rostam Hassan",slug:"sarkawt-rostam-hassan"}]},{id:"14743",title:"Artificial Neural Network – Possible Approach to Nonlinear System Control",slug:"artificial-neural-network-possible-approach-to-nonlinear-system-control",signatures:"Jan Mareš, Petr Doležel and Pavel Hrnčiřík",authors:[{id:"20382",title:"Dr.",name:"Jan",middleName:null,surname:"Mareš",fullName:"Jan Mareš",slug:"jan-mares"},{id:"22548",title:"PhD.",name:"Pavel",middleName:null,surname:"Hrncirik",fullName:"Pavel Hrncirik",slug:"pavel-hrncirik"},{id:"22608",title:"PhD.",name:"Petr",middleName:null,surname:"Dolezel",fullName:"Petr Dolezel",slug:"petr-dolezel"}]},{id:"14744",title:"Direct Neural Network Control via Inverse Modelling: Application on Induction Motors",slug:"direct-neural-network-control-via-inverse-modelling-application-on-induction-motors",signatures:"Haider A. F. Almurib, Ahmad A. Mat Isa and Hayder M.A.A. Al-Assadi",authors:[{id:"7191",title:"Dr.",name:"Hayder M. A. Ali",middleName:null,surname:"Al-Assadi",fullName:"Hayder M. A. Ali Al-Assadi",slug:"hayder-m.-a.-ali-al-assadi"},{id:"21745",title:"Dr.",name:"Ahmad Azlan",middleName:null,surname:"Mat Isa",fullName:"Ahmad Azlan Mat Isa",slug:"ahmad-azlan-mat-isa"},{id:"22702",title:"Dr.",name:"Haider A.F.",middleName:null,surname:"Almurib",fullName:"Haider A.F. Almurib",slug:"haider-a.f.-almurib"}]},{id:"14745",title:"System Identification of NN-based Model Reference Control of RUAV during Hover",slug:"system-identification-of-nn-based-model-reference-control-of-ruav-during-hover",signatures:"Bhaskar Prasad Rimal, Idris E. Putro, Agus Budiyono, Dugki Min and Eunmi Choi",authors:[{id:"20747",title:"PhD.",name:"Bhaskar Prasad",middleName:null,surname:"Rimal",fullName:"Bhaskar Prasad Rimal",slug:"bhaskar-prasad-rimal"},{id:"23043",title:"Prof.",name:"Agus",middleName:null,surname:"Budiyono",fullName:"Agus Budiyono",slug:"agus-budiyono"},{id:"23044",title:"Prof.",name:"Dugki",middleName:null,surname:"Min",fullName:"Dugki Min",slug:"dugki-min"},{id:"23045",title:"Prof.",name:"Eunmi",middleName:null,surname:"Choi",fullName:"Eunmi Choi",slug:"eunmi-choi"},{id:"23594",title:"Prof.",name:"Idris E.",middleName:null,surname:"Putro",fullName:"Idris E. Putro",slug:"idris-e.-putro"}]},{id:"14746",title:"Intelligent Vibration Signal Diagnostic System Using Artificial Neural Network",slug:"intelligent-vibration-signal-diagnostic-system-using-artificial-neural-network",signatures:"Chang-Ching Lin",authors:[{id:"20925",title:"Dr.",name:"Chang-Ching",middleName:"David",surname:"Lin",fullName:"Chang-Ching Lin",slug:"chang-ching-lin"}]},{id:"14747",title:"Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms",slug:"conditioning-monitoring-and-fault-diagnosis-for-a-servo-pneumatic-system-with-artificial-neural-netw",signatures:"Mustafa Demetgul, Sezai Taskin and Ibrahim Nur Tansel",authors:[{id:"19106",title:"Dr.",name:"Mustafa",middleName:null,surname:"Demetgul",fullName:"Mustafa Demetgul",slug:"mustafa-demetgul"},{id:"22738",title:"Dr.",name:"Ibrahim Nur",middleName:null,surname:"Tansel",fullName:"Ibrahim Nur Tansel",slug:"ibrahim-nur-tansel"},{id:"27078",title:"Dr.",name:"Sezai",middleName:null,surname:"Taskin",fullName:"Sezai Taskin",slug:"sezai-taskin"}]},{id:"14748",title:"Neural Networks’ Based Inverse Kinematics Solution for Serial Robot Manipulators Passing Through Singularities",slug:"neural-networks-based-inverse-kinematics-solution-for-serial-robot-manipulators-passing-through-sing",signatures:"Ali T. Hasan, Hayder M.A.A. Al-Assadi and Ahmad Azlan Mat Isa",authors:[{id:"21745",title:"Dr.",name:"Ahmad Azlan",middleName:null,surname:"Mat Isa",fullName:"Ahmad Azlan Mat Isa",slug:"ahmad-azlan-mat-isa"},{id:"11220",title:"Dr.",name:"Ali",middleName:"Taqi",surname:"Hasan",fullName:"Ali Hasan",slug:"ali-hasan"},{id:"21744",title:"Dr.",name:"Hayder",middleName:"M A A",surname:"Al-Assadi",fullName:"Hayder Al-Assadi",slug:"hayder-al-assadi"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"73938",title:"Biomarkers of Diseases: Their Role in Emergency Medicine",doi:"10.5772/intechopen.94509",slug:"biomarkers-of-diseases-their-role-in-emergency-medicine",body:'According to the National Institute of Health (NIH) Biomarkers Working Group, a biological marker (biomarker) is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [1].
Worldwide, biomarkers have been primarily studied specifically in the setting of diseases. Even so, it is imperative to understand the concept that a biomarker in the human milieu can be present as a result of normal physiological functioning and need not be the result of a pathological process always. Analysis of various biomarkers, both quantitatively and qualitatively have resulted in better understanding of physiological as well as pathological processes of the human body. An ideal biomarker should aid in early diagnosis, help in risk stratification, be able to monitor response to treatment modalities and predict outcomes better than the clinical processes and investigations existing in medical practice at that point of time [2].
In medical practice, biomarkers can be of diagnostic or prognostic value. In the setting of practicing Emergency medicine, diagnostic biomarkers assume greater significance. In the Emergency department, where time is of the essence, diagnostic biomarkers help in quick clinical decision making as well as patient disposition. A biomarker that is able to make a clinical differentiation between two similar disease conditions is highly valuable for an emergency physician. This helps in rapid diagnosis, which leads to faster treatment initiation. Faster the treatment can be initiated, faster the patient movement. A faster TAT (turnaround time) for patients in the ED translates to lesser waiting times in the ED. An ideal biomarker in the Emergency department should have the following characteristics—High diagnostic accuracy which involves a high degree of sensitivity with reasonable specificity, should be reproducible across platforms and should be cost effective to the patient and the clinician.
Numerous biomarkers have been extensively studied for the purpose of clinical utility, but only a few handfuls have proved their mettle in clinical practice. The abundance of biomarker tests available, pose a not so friendly dilemma to the clinician of the present. Understandably, there is still a long way to traverse from the laboratory table to the patient bedside. Numerous trials and research activities are underway across the world in the field of biomarkers. Most of them are still in various phases of preclinical trials and are expected to be available by the patient’s bedside in a few years’ time.
In modern clinical practice, biomarkers are being used more and more for clinical decision making. The current gamut of biomarkers available in the clinical realm have changed the way we practice medicine. The increase in the use of biomarkers for clinical decision making has expedited the patient disposition to a great extent. Having said that, there is also the other side of the coin which is the increase in the health care cost. The over dependence of clinicians on biomarkers should be viewed with caution as this escalates the overall cost of treatment and the patients will have to bear that burden. The decision to make use of biomarkers in the clinical context should be individualised and targeted to prevent its overuse or abuse [3]. This will put a strain on the already scarce resources in the health care sector. It is always pertinent to remember that—A biomarker should always be evaluated in the clinical context and should never be used as a standalone tool for clinical decision making.
In Emergency medical practice, majority of the biomarker work has been focussed in the field of cardiology, renal failure and sepsis, as early detection and prompt interventions in the early phases of the diseases can significantly alter the natural course of the disease and improve the patient morbidity and mortality. Other areas of focus are hepatic diseases, traumatic brain injury, venous thromboembolism etc., where biomarkers are increasingly being tested for their clinical utility. Therefore, the chapter focuses in detail on these clinically significant biomarkers.
Acute coronary syndromes have been in the forefront of novel biomarker evaluation research due to its widespread prevalence as well as the need for detection in a time sensitive manner. Almost 20 million patients with symptoms of acute coronary syndromes present to emergency departments in North America and Europe annually [4, 5]. Numerous studies have been performed on various biomarkers, the conventional markers as well as high sensitive variants, and also with respect to different time frames. In the Emergency department, making a rapid diagnosis of acute myocardial infarction is of utmost importance, as
Historically, biomarkers like LDH (lactate dehydrogenase) and AST (aspartate aminotransferase) were tried in the detection of an acute coronary syndrome especially towards the end of 20th century. Their clinical significance slowly began to decline with the advent of better alternatives as well as lack of specificity. The next in line were the markers with better specificity and sensitivity, namely—the troponins and creatine kinase. As the 21st century is taking its foothold, the scientific community is focussing its attention on these biomarkers for detection of acute coronary events, a leading cause of death worldwide. The research was primarily focussed on Creatine Kinase – MB fraction, which used to be the gold standard of evaluation of ACS. But, that has given way to the newer biomarkers—Troponin I and Troponin T – both conventional and high sensitive, which are being studied extensively across the globe.
Creatine Kinase is an intracellular enzyme with a dimeric molecule which has 3 isoforms—CK-MM (muscle), CK-BB (brain) and CK-MB (myocardium), based on the organ of origin. CK-MB is the isoenzyme fraction which is predominantly seen in cardiac muscle and hence the utility in detecting cardiac muscle damage. This marker is leaked into the systemic circulation from the cellular cytosol due to disruption of the cell membrane as a result of myocardial injury. This marker can be assayed by a clinician to help in the diagnosis of myocardial infarction. CK-MB isoenzyme can be detected in the bloodstream about 4–6 hours after the onset of chest pain. It peaks by 12–24 hrs and returns to baseline by 12–48 hours. This short time window of rise and fall of CK MB is especially useful in detecting reinfarction or infarct extension in a patient in whom the troponin values might be already elevated as a result of an infarct. It is also helpful in identifying complications in patients who have undergone revascularization procedures in the cardiac care unit. The reference values for CK-MB are as follows—males: ≤ 7.7 ng/mL and females: ≤ 4.3 ng/mL. CK MB assay should always be viewed with a pinch of salt since it is a subunit of the total CK in the system. Abnormal elevations of CK MB can be detected along with an increased level of total CK in cases of traumatic muscle injuries, rhabdomyolysis, myopathies etc. It is worthwhile to note that the normally CK-MB fraction accounts for only 3–5% of the total CK in the body and any increase beyond 30–50% of the total CK should prompt suspicion of abnormal beta subunit synthesis. But, over the past few years, the burden of diagnosis of acute myocardial injury has been shifted on to the shoulders of troponins [6].
Troponin is a complex protein molecule comprising of three regulatory proteins playing an integral role in the contraction of cardiac and skeletal muscle. These three subunits are namely—Troponin I (TnI), Troponin T (TnT) and Troponin C (TnC). Each subunit has a unique function. Troponin T binds to the troponin components of Tropomyosin, troponin I inhibits the interaction of myosin with actin and troponin C has the sites for binding of calcium ions to initiate muscle contraction.
Similar to creatine kinase, any cellular injury leads to leakage of the troponins into the systemic circulation thereby providing a window for diagnosis of acute myocardial infarction. Troponins have much higher specificity and sensitivity than creatine kinase. The utility of cardiac troponins especially – Troponin T and Troponin I has been validated in various studies across the world and hence has been incorporated into the diagnostic guidelines of acute myocardial infarction.
Normally troponins are not detectable in the bloodstream due to the minute quantities in open circulation, which is <0.01 ng/mL for Troponin T and ≤ 0.04 ng/mL for Troponin I. After a myocardial injury, elevated troponin levels in the bloodstream can be detected within a period of 4–6 hours, by conventional methods. The reason for this delay in detection has been attributed to the molecular weight and size (21–37 kDa). This can cause clinically significant delay in the diagnosis of myocardial infarction especially in the setting of nonspecific ECG changes. This has led to the advent of high sensitive assays which can detect troponins at much lower levels (at the levels of ng/L) and that too, much earlier than conventional methods. Troponins can be detected as early as 2 hours after the ischemic event by high sensitive troponin assays currently in clinical practice. This also has a caveat, that is, troponin levels can be detected in the circulation even without myocardial injury [4, 5]. Hence a troponin value above the 99th percentile is taken as a diagnostic cut-off for detection of myocardial ischemia. A 20% rise or fall from the baseline within a period of 3–6 hours can confirm the diagnosis of an acute myocardial infarction according to the National Academy of Clinical Biochemistry [6]. It has been recommended by the American college of Cardiology (ACC) that serial values of troponin be considered at 6–9 hr. intervals to rule out NSTEMI [7]. European Society of Cardiology has also reiterated the importance of doing serial assessments of troponins rather than making a clinical decision based on a single value [8]. In a recent large multicentre evaluation in patients with suspected ACS who presented within 8 hours of symptom onset, it was found that it was possible to diagnose ACS with 3-hour marker samples rather than the conventional method of doing serial markers at 6 hour intervals, without losing out on the diagnostic accuracy [9]. Along with clinical evidence of MI, an elevation of troponin level more than 5 times the upper limit compared to the baseline is needed to diagnose a PCI-related MI and more than 10 times the upper limit to diagnose a CABG-related MI [10]. Multiple studies have shown that there is correlation between the levels of troponins and development of adverse cardiac events [11, 12].
Elevated troponin levels may not always indicate myocardial injury [13, 14]. It can also be elevated in non-ischemic conditions as well. A rise/fall in troponin levels are needed to detect acute MI in patients in whom troponins will be elevated otherwise, like renal failure. Even though sensitivity is increased, specificity has come down which may indicate an underlying disease than an acute coronary event. Various causes of nonischemic elevation of troponins are detailed in the below table (Table 1). The troponin levels have to be interpreted only in the appropriate clinical setting, failing which the physician may be misled to an alternate diagnosis [15].
Cardiac | Non Cardiac |
---|---|
• Congestive cardiac failure | • Pulmonary Embolism |
• Myocarditis | • Renal failure |
• Pericarditis | • Sepsis |
• Infiltrative diseases | • Stroke |
• Blunt chest trauma |
Examples of non ACS-causes of elevated troponin levels:
Numerous other biomarkers have piqued the interest of the scientific community to identify acute coronary events more early as well as more precisely. Very few have actually stood on their own when compared to troponin studies. The most common drawback being the cost of the investigation as well as availability. Some of the examples are discussed below.
Myoglobin (Mb) peaks within minutes of cardiac ischemia. With the recent advancements for detection of hs-troponin levels, the utility of Mb has come down in the diagnostic algorithm [16]. A few examples of other novel biomarkers of myocardial ischemia/injury that have undergone clinical trials are given below in Table 2. They include cardiac intracellular proteins, markers of neurohormonal activation, markers for haemostatic activity, vascular inflammation markers etc. [17]. In this study, the assessment of H-FABP within the first 4 h of symptoms was found to be superior to cTnT for detection of MI. But the reduced specificity of H-FABP is presently limiting its usefulness in clinical practice. Soluble CD40 ligand and choline which are biomarkers signalling the instability of atherosclerotic plaque formation, have been studied, but did not show add any prognostic or diagnostic value to the existing ones in practice. But the other biomarkers they studied along with this, did not show any favourable clinical significance.
• Myoglobin (Mb) P |
• Heart Fatty Acid Binding Protein (H-FABP) |
• Glycogen Phosphorylase-BB (GP-BB) |
• NT-Pro-Brain Natriuretic Peptide (NT-ProBNP) |
• D-Dimer |
• High Sensitivity C-Reactive Protein (HsCRP) |
• Myeloperoxidase (MPO) |
• Matrix Metalloproteinase-9 (MMP-9) |
• Pregnancy Associated Plasma Protein-A (PAPP-A) |
• Soluble CD40 Ligand (SCD40L) |
Newer biomarkers for cardiac ischemia.
Cardiac failure is a complex process involving a multitude of pathophysiological processes. As a result of this, various biomarkers have been identified which correlate with specific aspects of heart failure. The marker which has made its mark in a clinically significant manner are the natriuretic peptides-B type natriuretic peptide (BNP) and NT pro BNP.
BNP is secreted from the ventricles as a result of neurohormonal activation due to volume overload and resultant stretching of the myocardial muscle fibres. In patients with left ventricular dysfunction/failure, high plasma levels of BNP and NT pro BNP are specific for elevated filling pressures in the cardiac chambers. This can be used in the clinical context for the diagnosis as well as prognostication of cardiac failure. ProBNP is a 108-amino acid polypeptide precursor which is stored in secretory granules in both ventricles and, to a lesser extent, in the atria. After proBNP is secreted, it is cleaved to the 76-peptide, biologically inert N-terminal fragment NT-proBNP and the 32-peptide, biologically active hormone BNP. BNP is rapidly cleared from the circulation; the plasma half-life being approximately 20 min. No receptor-mediated clearance of NT-proBNP is known to occur, because of which NT-proBNP has a prolonged half-life of 60–120 min. The reference values for NT-proBNP varies widely with age and gender, which can be tricky for the clinician while assessing patients, especially in the elderly population (Table 3).
Age | Males | Females |
---|---|---|
≤45 yrs | 10–51 pg/mL | 10–140 pg/mL |
45–70 yrs | 10–100 pg/mL | 10–206 pg/mL |
≥70 yrs | 10–138 pg/mL | 10–1263 pg/mL |
Reference values for NT-ProBNP:
In the multicentre Breathing Not Properly Study [18], using plasma BNP level of 100 pg/mL as cut off, gave a sensitivity of 90%, specificity of 76% and a diagnostic accuracy of 81% which was superior to clinical assessment alone in a series of 1586 patients who presented to the ED with acute dyspnoea. A BNP level < 100 pg/ml or an NT-proBNP level < 300 pg/ml can essentially rule out Acute HF in most cases. When using N-terminal proBNP for the diagnosis of acute CHF, a value of 900 pg/mL has high specificity and sensitivity.
Apart from left ventricular failure, these biomarkers can be elevated in numerous other conditions which can cause myocardial stretch. Patients with right ventricular failure secondary to pulmonary embolism or pulmonary hypertension, valvular heart disease, arrhythmias such as atrial fibrillation, renal failure and advanced age may also have elevated levels of BNP or NT-proBNP [19]. In severe renal failure, the NT-pro BNP value of >1200 pg/mL is needed to make a diagnosis of cardiac failure. A common clinical scenario in which the patient is obese, the pro-BNP values can be falsely lower which can mask cardiac failure and lead to misdiagnosis.
The European Society of Cardiology Task Force has recommended that the algorithm for HF diagnosis should include an NP assay as the first step along with electrocardiography (ECG) and chest X-ray [20]. Biomarkers are not just useful in the diagnostic algorithm, but also in guiding treatment. In a meta-analysis [21] of 2686 patients in 12 randomised trials, the researchers found that the use of cardiac peptides to guide pharmacologic therapy significantly reduces mortality and HF related hospitalisation in patients with chronic HF.
As discussed in the previous section, Troponin I (TnI) also plays an important role in the pathophysiological profile of cardiac failure. Newer markers that have potential to be significance in the future for diagnosis and prognosis in heart failure include high-sensitivity C-reactive protein (hsCRP), uric acid and myeloperoxidase (MPO), soluble toll-like receptor-2 (ST2) and soluble fms-like tyrosine kinase receptor-1 (sFlt-1). Recently, a study in which the amount of exhaled acetone is measured has shown promise as a newer non-invasive modality for cardiac failure assessment [22]. A recent study attempted to evaluate the predictive utility of these biomarkers with a multimarker score which included BNP, troponin I and creatinine apart from the above markers. They concluded that a multimarker score significantly improves prediction of adverse events in ambulatory patients with chronic heart failure [23]. But, NACB’s practice guidelines on cardiac biomarker testing specifically recommends against routine use of biomarker testing only for risk stratification [24]. The newer entries into this field include galectin-3 [25], MR-proANP (midregion pro–atrial natriuretic peptide) [26], MR-proADM (mid regional pro-adrenomedullin), co-peptin, adiponectin, pentraxin-3, soluble ICAM-1(intercellular adhesion molecule-1), PAPP-A (pregnancy associated plasma protein A) etc.
In a case of suspected pulmonary embolism, laboratory evaluation by biomarker levels is primarily helpful in ruling out the diagnosis in low probability scenarios, rather than ruling in a confirmation of a diagnosis. D-dimer has been in clinical use extensively since the past few decades and the other markers which are increasingly used are troponins, BNP and Ischemia modified albumin (IMA).
D-dimer is a degradation product produced by plasmin during fibrinolysis. The reference value of D-dimer is ≤500 ng/mL Fibrinogen equivalent Units (FEU). It has very low specificity, but a high sensitivity. Due to the low specificity, a clinical diagnosis of pulmonary embolism requires a strong clinical suspicion. In order to help the clinician in this regard, various scoring systems to assess the probability of making a diagnosis of PE have been devised. Well’s criteria and its modified version are among the most commonly used. These scoring systems assist the clinician in assessing the probability of a diagnosis of PE along with the blood levels of the biomarker used. In patients with a low pretest probability of PE as assessed by well’s criteria and a negative d-dimer value, the diagnosis of pulmonary embolism can be essentially ruled out without any probability of adverse events happening later [27, 28].
Ischemia modified albumin (IMA) is a newer marker that has shown potential as a substitute for D-dimer as it has been found to be better than the latter in a few studies due to its better positive predictive value [29]. The reference value for IMA is ≤0.540 ABSU. In patients with pulmonary embolism, more so in those who develop RV dysfunction, other biomarkers like troponins and BNP are also found to be elevated. This occurs due to the increased pulmonary vascular resistance, pulmonary artery pressure and resultant RV afterload. The elevated troponin levels can pose a dilemma for a clinician who wants to rule out ACS as well in the clinical setting as the symptoms of both the conditions may overlap significantly. The elevated levels of BNP/NT pro-BNP in patients with pulmonary embolism have been found to be associated with increase in risk for complications and 30-day mortality [30].
Sepsis is a complex process that stems from a combination of features of a systemic inflammatory response to a known or presumed infection. It is associated with a very high mortality rate around 30% not to mention the significant economic impact on the healthcare system [31]. Sepsis can be viewed as a chain of events in the body as a response to an inciting agent through an inflammatory pathway. This provides clinicians the opportunity to diagnose sepsis early by either picking up the inciting agent or the inflammatory response to the agent. More than 170 biomarkers have been identified as useful for evaluating sepsis [32], which itself points to the fact that none of them can be used as a single marker for accurate diagnosis or prognosis. C-reactive protein, procalcitonin and serum lactate are among the prominent ones used extensively worldwide at present.
C-Reactive protein, one of the most commonly used markers for sepsis, is synthesised in the liver as an acute phase reactant. The normal levels in a healthy adult individual tends to be below 10 mg/L. Depending on the severity, any stress or stimulus can cause an elevation in the CRP levels, even manifold up to 500 mg/L. The levels peak around 36–48 hrs and the plasma half-life is approx. 19 hrs. Although very commonly used as an inflammatory marker, it lacks specificity as it is found to be elevated in numerous conditions like post-operative patients, burns, myocardial infarction and inflammatory/rheumatic diseases as well [33]. It can be elevated even in normal individuals especially in elderly as well as pregnancy. Moreover, even a viral infection can cause a mild increase in the serum levels of CRP, contrary to popular belief. The sensitivity and specificity of CRP as a marker for bacterial infections are 68–92% and 40–67%, respectively [34, 35]. CRP plasma levels have shown to correlate with the severity of infection [36] which makes it a useful marker to assess the response to pharmacological treatment.
It is a 116-amino acid polypeptide which is the prohormone of calcitonin. It has a short half-life (25–30 hours), and is encoded by the
Lactate is produced in the body even normally, which gets cleared off rapidly in healthy individuals. But, in cases of sepsis and resultant hypoperfusion, the levels of lactic acid increase when anaerobic metabolism increases in the body. Lactate clearance has been shown in a prominent light in the ‘Early goal directed therapy’ of septic patients. This indicates that, more than a diagnostic marker, lactate has prognostic significance in patients with sepsis. Recent studies have shown that patients with even a milder increase in serum levels in the range of 2–4 mmol/L were at an increased risk of morbidity and mortality [42]. In a study conducted in an urban academic centre which included 1278 patients with infections, those with lactate levels above 4 mmol/L had higher in-hospital mortality rates than patients with lactate levels less than 2.5 mmol/L (28.4% vs. 4.9%) [43]. The bottom line is, the better the lactate clearance, better the outcome of the patient [44].
Adrenomedullin (ADM) is a 52-amino acid ringed peptide produced from endothelial cells in cardiovascular, renal, pulmonary, cerebrovascular and endocrine tissues. It is a potent endogenous vasodilator in the human body. ADM is not easily measurable due to its very short half-life of 22 minutes in the circulation, its rapid degradation by proteases, and the formation of complexes with circulating complement factor H [45]. The prohormone of ADM - ProADM can be used as a surrogate marker for this purpose as it is more easily quantifiable, and the tools required for this are available commercially. The mid-regional fragment of proadrenomedullin (MR-proADM) is a marker of endothelial dysfunction/inflammation and therefore can be seen in elevated levels in numerous disease conditions. Pro-ADM has been found to be an independent predictor for adverse outcomes in patients with COPD [46]. It has also been studied in the context of burns, in which it was found to have utility in early recognition of onset of sepsis in burns victims [47]. It is still early days for MR-proADM in routine clinical practice as many studies [48] have failed to demonstrate any added utility with respect to other less expensive parameters presently available. For healthy individuals, the reference values for MR pro ADM is <0.5 nmol/L.
Cytokines like TNF, IL-1β and IL-6 are the predominant inflammatory mediators responsible for the initial inflammatory response and the levels correlate with the organ damage and mortality [49]. Similarly, High-mobility group box 1 protein (HMGB1) and Macrophage migration inhibitory factor (MIF) are also found to increase in patients with severe sepsis and septic shock and is correlated with the degree of organ failure [50, 51]. Lipopolysaccharide-binding protein (LBS) is an acute phase protein which increases in sepsis and makes it useful as a diagnostic tool as well as a marker for severity of the disease [52, 53]. Other biomarkers like serum amyloid A, eosinophil count, mannan and antimannan, and IFN-γ-inducible protein 10 also show potential to be of use in the future.
In patients who are hospitalised with burns, sepsis is considered as one of the most important causes for mortality. Biomarkers which can help pick up the onset of sepsis in burn patients in the early phase itself will be useful in the proactive management of complications. Procalcitonin, Tumour necrosis factor-alpha (TNF-alpha), MR Pro-ADM, Interleukins 6, 8 & 10, Presepsin are among the major ones studied in this context in addition to assessment of single-nucleotide polymorphisms (SNPs) and leukocyte transcriptomes [54].
PCT has been extensively studied in the context of sepsis, but literature regarding studies in burns are much lesser in comparison to other critical conditions. Serum levels of PCT were found to be elevated in patients who developed infections after burns in one of the initial studies done in 1993 which had 9 burns patients included among the 79 general patients enrolled in the study [55]. In a recent meta-analysis of around 12 studies in burns patients led the investigators to believe that PCT has a strong ability to differentiate between patients with sepsis and without sepsis [56]. The study proposed that a PCT value >1.47 ng/mL can prompt the clinicians to initiate early antibiotic therapy to counter the development of sepsis and improve patient outcomes.
It is a proinflammatory cytokine and has been researched worldwide in various disease conditions among the host of numerous inflammatory mediators. It is produced ubiquitously in the body in response to various stimuli which can be infectious or ischemic in nature. They include endotoxins, complement system activation, hypoxia, ischemia as well as reperfusion [57]. TNF-alpha has been found to be elevated in burns and the values are seen to be higher in patients found to be in sepsis [58]. It has also been shown to have a prognostic value in burns victims. In burns patients who were treated with GM-CSF, the values of TNF alpha were shown to come down gradually as the treatment progressed [59], hence proving its role as a prognostic indicator. Reference value: ≤ 2.8 pg/mL.
The interleukins (ILs) are a large class of cytokines that promote cell-to-cell interactions and the stimulation of humoral or cell-mediated immune responses. They were initially thought to be produced only by the leukocytes, but have been found to be produced from numerous sources since then. The IL family consists of a huge number of members of which IL-6, IL-8 and IL-10 have been shown to be associated with evaluation of sepsis in burns patients. IL-6 has been found to be elevated in burns patients with sepsis [60]. It not only helps in the early diagnosis, but also has prognostic significance regarding the mortality as the levels have been found to be correlating with the size of the burns [61]. Recently, a meta-analysis of studies done on critically ill patients regarding markers of sepsis found IL-6 to have a high specificity, hence making it a suitable marker to confirm an infectious process [62]. Similarly, studies have found that IL-8 levels in burns patients correlate with the development of sepsis and multi-organ failure resulting in mortality [63]. The authors of the study opined that it can be used as a biomarker for monitoring the morbidity and mortality of burn patients developing sepsis. IL-10, similar to its counterparts, have been shown to have a correlation to development of sepsis in burn patients. Normally, the serum levels increase after the injury and decline later. But a failure to decline over time and being persistently elevated should point towards the development of an infective process and may increase the chances of mortality [64], hence making it a prognostic indicator in burn patients.
Some of the other notable markers pertaining to burns patients are -
Conventionally, renal function tests which include serum levels of creatinine, urea and assessment of glomerular filtration rate are the methods used to quantify renal diseases. But, these conventional methods have a huge drawback. There is an unacceptable high time lag between the onset of tissue injury and derangement of these biochemical values. This hinders any active reno-protective interventions that may be initiated promptly. Moreover, the serum levels of these markers vary widely even in healthy individuals as it depends on various physical factors like age, gender, muscle mass etc. This has led the medical community to look for alternatives. Human neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), interleukin-18 (IL-18), cystatin C, clusterin, fatty acid binding protein, and osteopontin are the prominent ones that have been studied and NGAL has been the most prominent one of the lot.
Other markers that are being studied for their utility in kidney injury are Interleukin-18, Kidney injury molecule 1 (KIM-1), Cystatin – C, Sodium/Hydrogen Exchanger Isoform 3(NHE3) and Liver-type fatty acid binding protein (L-FABP). Kidney injury molecule 1 (KIM-1) and Interleukin-18 are found to be elevated in case of ischemic Acute Tubular Necrosis [72, 73]. Cystatin-C has been found to be better at estimating the GFR than the conventional method using creatinine [74]. Sodium/Hydrogen Exchanger Isoform 3(NHE3), which is found in the urine following tubular injury, has been found to be better than fractional excretion of sodium in differentiating between pre renal and intrinsic renal causes for renal failure [75]. Liver-type fatty acid binding protein (L-FABP) has also shown promise in animal studies for early detection of AKI and is being adapted as a possible biomarker for AKI [76].
Traumatic brain injury is a major cause of morbidity and mortality across the world [77]. The reasons are several, but the lion’s share of the incidents can be attributed to the high speed motor vehicle collisions. In addition to that, there are other contributing factors like falls as well as injuries due to contact sports. The acute injuries can be devastating and even fatal, but a huge number of those patients also develop chronic neurological sequelae which can be debilitating [78]. Given the complexity of the situation, the understanding of the mechanisms and pathophysiology of these chronic conditions are much less understood compared to their acute counterparts [79].
Traumatic Brain Injury (TBI) can be classified into acute or chronic based on the acuity of the event. Acute traumatic events as a result of MVA (motor vehicle accidents), falls or sporting events mostly result in immediate clinical symptoms or signs that are often diagnosed with the help of neuroimaging immediately by the clinician. A concussion as a result of contact sports or accidents can result in clinical features which can range from mild dizziness to complete unconsciousness. But, it will exhibit no discernible defects in neuroimaging of the patient [80]. Therefore it becomes a clinical diagnosis rather than a radiological diagnosis. Many patients, especially sportspersons who are part of contact sports like boxing have exhibited persistent symptoms for hours to days to months after the insult [81]. The shearing forces in an acceleration/deceleration injury causes axonal damage which is often responsible for the clinical manifestations. The acute event leads to a primary insult to the central nervous system which can manifest as cerebral oedema or even intracranial haemorrhage. This in turn leads to increase in the intracranial pressure which in turn causes cerebral hypoperfusion and resultant tissue hypoxia [82].
Traumatic head injury is not an area which usually requires a biomarker evaluation from the Neurosurgeon’s point of view for the purpose of acute care. Clinical decision making is often dependent on neuroimaging, which is a CT scan usually. But, recently, the role of biomarkers has become important in the decision making process of getting a neuroimaging. This is done with a view of reducing the radiation exposure to patients with mild head injury. These biomarkers are not usually present in the circulation and their presence generally indicates a breach of Blood–brain barrier. Some of the important biomarkers described in the recent literature are Glial fibrillary acidic protein (GFAP), calcium binding protein S100B, and tau protein [83]. The most important one in the horizon is S100 calcium binding protein B (S100B), which is a glial-specific protein which is primarily expressed by a subtype of mature astrocytes. It is elevated in neuronal damage which makes it a potential marker for CNS insults. In a study done on 512 adult patients with mild head injury (GCS 14–15, loss of consciousness and/or amnesia and no additional risk factors), the researchers used protein S100B levels as a clinical tool to determine whether the patient needed a CT scan [84]. They found that adult patients with mild head injury, without additional risk factors and with S100B levels of <0.10 mcg/L within 3 hours of injury, can safely be discharged from the hospital without neuroimaging. A recent study done in Sweden tries to shed light on the utility of biomarkers like total tau, protein S100B and neuron-specific enolase in assessing concussion injuries in sports persons [85].
Once the primary insult is over, the brain can suffer from secondary injury as a result of sequelae from the initial insult. This can occur after days, weeks, months or years after the initial event. This results from biochemical cascades that are triggered by the primary event. These secondary events are mediated by free radicals and reactive oxygen species that are generated as a result of tissue hypoxia, reperfusion injury and neuroinflammation [86]. The change in membrane permeability which results from the initial injury causes increase in the calcium uptake or activation of NMDA and AMPA receptors by glutamate can cause mitochondrial dysfunction [87]. Thus the inflammatory response leads to further cellular disruption and the vicious cycle continues to damage the nervous homeostasis. These inflammatory insults in the nervous system give rise to various inflammatory markers which can be detected in the system. These biomarkers have been extensively studied and their usefulness in the clinical environment hotly debated. Even though many of these markers many of them show enough promise within the confines of the laboratory, they are yet to come to the bedside to be used by the clinician in daily practice.
Many animal studies have demonstrated an increase in biochemical markers even after a single day after the insult and these can persist even after a month [88]. Acrolein, a post-traumatic neurotoxin can be quantified in brain tissue and can be elevated depending on the insult. A sustained upregulation has been demonstrated after brain injury, which suggests that it is a potential marker for neuronal injury and inflammation [89].
Proton Magnetic Resonance Spectroscopy (1H-MRS) is a technique which is able to measure the neurochemicals in the nervous system. This helps in detecting the neurotransmitters and metabolites, thereby quantifying the markers in various clinical conditions. Using this method, animal studies have found that endogenous antioxidants glutathione and ascorbic acid may be decreased up to 2 weeks following the insult [90]. F2-isoprostane, a lipid peroxidation by-product, has been found to be elevated on chronic brain injury or Chronic Traumatic Encephalopathy (CTE) which manifests years after the insult(s).
As detected by Proton Magnetic Resonance Spectroscopy (1H-MRS), there have been consistent results in detection of the following neurotransmitters in children with Autism Spectrum Disorder(ASD). There has been reduced levels of N-acetylaspartate (NAA), Creatine and phosphocreatine (Cr + PCr), Glutamine, Myo-Inositol and Choline containing compounds in the subcortical areas as well as cortical white matter and grey matter in varying degrees in children with ASD [91].
Neurodegenerative diseases can be extremely debilitating and distressing to not only the patient, but also the caregivers. Once diagnosed, the pathophysiological mechanisms can seldom be reversed and hence it becomes the source of social, financial and economic drain for not only families, but for the governmental health machinery itself. The social impact is huge, but the economic impact of these conditions cannot be ignored by any means.
Hence, it becomes imperative that the diagnosis can be made as early as possible thereby mitigating the scenario. An earlier diagnosis will help the clinician as well as the caregivers to come to a plan for further care of the patient. It is also essential to look at the therapeutic interventions which can arrest or at least slow down the progression of the disease. This is where the role of biomarkers come into picture. A biomarker can help in diagnosing a particular condition early. Even if the diagnosis cannot be confirmed, at least the possibility of the condition can be ascertained and hence be prepared against. This is where a biomarker helps in fighting the diseases which for all practical purposes, have no definitive curative measures available by the bedside.
Biomarkers in neurodegenerative conditions can be classified as fluid and radiological markers. Since radiology is an integral and essential part of assessment of the neurological system in modern medicine, many techniques have been developed which can detect the presence of markers within the brain tissue which can point towards the presence or likelihood of a particular disease condition. On the other hand, there are fluid biomarkers that can be detected in the fluid that flows all over the brain - the cerebrospinal fluid (CSF). Before we look into those markers, it needs to be kept in mind that quite a few markers have gone to the lab in the hunt for that perfect biomarker. But, none of them has been successful enough to be brought to the bedside for daily clinical practice. Among the many neurodegenerative conditions, Alzheimer’s disease has been the most extensively studied, because of its prevalence and impact on the society. But the biomarkers used in Alzheimer’s disease do overlap with many other conditions as well due to similarities in pathophysiology.
Biomarkers in neurodegenerative conditions can be detected in blood and cerebrospinal fluid (CSF) and are clubbed together to be called as fluid markers. The predominant ones are Amyloid β peptides/oligomers and Tau peptides. Neurofilament light chain (NfL), which is found in myelinated axons, is an important marker which indicates white matter damage and points towards neurodegeneration [92]. Serial NFL sampling in patients at risk of developing Alzheimer’s Disease, can be used to predict brain atrophy rates, cognitive impairment and disease progression [93]. These can be estimated by assays in blood as well as CSF. The major disadvantage these markers face is the low specificity and that hinders its utility in clinical practice. Another set of markers or radiological distinction characteristics can be detected in MRI and PET scans and are termed as radiological biomarkers. MRI utilises the various imaging modalities available to detect white matter lesions that are present in many of the neurodegenerative diseases. These include reduction in the volume, thickness, presence of microbleeds, myelin, iron, neuromelanin within the brain tissue in specific regions. PET targets various Tau lesions and Amyloid β aggregates in various regions within the neuronal system to detect the possibility of neurodegeneration. Apart from these, there are genetic biomarkers or specific genes that can predict the possibility of a neurodegenerative disease condition (Table 4).
Gene(s) | Disease syndrome associated |
---|---|
Familial - Early-onset Alzheimer’s disease | |
AD- Risk factors | |
Behavioural-variant frontotemporal dementia | |
Amyotrophic Lateral Sclerosis | |
Huntington’s disease | |
Parkinson’s disease | |
Prion Disease |
Genetic biomarkers for neurodegenerative diseases.
It is a proteinopathy which is characterised by accumulation of Tau neurofibrillary tangles and extracellular Amyloid β plaques. These can be assessed by radiological methods as well as by looking at the fluid biomarkers. It has a prolonged pre-clinical phase where there Tau lesions can be found in the subcortical regions even before profound clinical symptoms appear [94]. Similarly amyloid β aggregates are initially found in the neocortical regions and later subcortical and cerebellar regions [95]. Plasma levels of Aβ42 has been found to be decreased compared to controls in a study [96]. The National Institute on Ageing and Alzheimer’s Association Research Framework has defined AD by its underlying pathologic processes that can be documented by post-mortem examination or in vivo by biomarkers [97]. The biomarkers are classified into the 3 major groups- β amyloid deposition, pathologic tau, and neurodegeneration - AT(N). As and when newer biomarkers are discovered, they may be added into these categories. When it comes to therapeutics targeted based on these markers, there is still a long way to go to get these implemented in clinical practice. This may be due to the lack of direct correlation between the marker load with the clinical deterioration [98] as well as lack of specificity [99].
It is the most common presentation of synucleinopathy. The presentation can be similar to other similar conditions like Dementia with Lewy bodies. The typical feature can be aggregation of α-synuclein in the form of Lewy bodies which starts in the subcortical regions and later spread into the other regions [100]. Apart from the mutations in the genes, causality has been attributed to pesticide exposure as well as traumatic brain injury [101].
This encompasses a spectrum of disease conditions characterised by vacuolation, gliosis and neuronal loss in the cortical regions of the frontal and temporal lobes. Features of Tau protein accumulation, TDP-43 or fused in sarcoma can be seen in this condition [102]. Since they share a similar pathophysiology, Amyotrophic lateral sclerosis (ALS) and other similar motor neuron diseases are considered to be part of the same spectrum.
Huntington’s disease is characterised by progressive neuronal loss and astrogliosis in the striatum along with prominent degeneration of the other cortical regions [103]. Similar to other disease conditions, there is limited data available to look at the therapeutic use of markers in HD also. Plasma levels of IL-8, TNF-α [104], and NfL may become useful in the coming years in this regard. A decrease in the uptake of phosphodiesterase-10 PET tracer in the strial region may become an important marker with regard to therapeutics in HD [105]. Diagnosis of Prion diseases like sporadic Creutzfeldt-Jakob disease (sCJD) is done by EEG, MRI or CSF based biomarkers with the use of real-time quaking-induced conversion (RT-QuIC) which is preferred over 14-3-3 protein detection [106].
Since many of the neurodegenerative conditions present late in clinical practice, it is imperative that in order to tackle this menace, the scientific community will have to bring forth tools that can detect these early as well us much ahead of the phase of clinical presentation, even decades. It is in this regard, the biomarkers have a major role to play, whether they are radiological or fluid markers. Genetic markers are useful when we are dealing with hereditary conditions or familial variants of neurodegenerative conditions. Confirmation of the diagnosis is essential in determining the treatment and initiating it at the earliest for the best possible response.
Even though the field of biomarker evaluation is not very old, it is a fast changing world and newer techniques are being added to the mix quite often. A recent technique is the Multimer Detection System-Oligomeric Aβ, which looks at the tendency of plasma proteins to oligomerize [107]. Immune-infrared sensor assay to measure blood vessels for the propensity of the amyloid protein to form β-sheets has also been tried with some success as a potential biomarker [108]. Measurement of locus coeruleus, an early affected region, using special MRI techniques is also being explored as a potential target [109].
POCT refers to diagnostic evaluation at or near the site of patient care. A POC lab is not within the institutional central laboratory, but nearer to the patient care setting like ED or ICU. POC testing of biomarkers is increasingly becoming the norm at the moment. This has been touted as the next revolutionary step in faster healthcare delivery in the Emergency department. But, the challenge lies in transferring the resultant advantage to improvement in patient care and disposition. The benefit demonstrated on paper should be translated to better patient care by the bedside. If the results of the POC testing do not alter the course of management of the patient, it defeats the whole purpose of POCT.
Biomarkers are being increasingly used in the Emergency departments for faster patient disposition. Recently efforts are being undertaken to include an array of biomarkers in the triage scoring itself as a method of risk stratification of patients presenting to the ED [110]. In this study, the researchers included biomarkers from 3 distinct biological pathways for risk stratification of general medical patients presenting to the ED. The study included biomarkers of inflammation (pro-adrenomedullin [ProADM]), stress (copeptin) and infection (procalcitonin). They used a multi-marker approach to stratify patients and came to a conclusion that all the markers strongly predicted the risk of death, ICU admission and high initial triage priority, especially ProADM. It is a possibility that these methods may get introduced in clinical practice in the not so distant future.
Biomarkers are among the best tools in the hand of the clinicians at present. Each and every clinical condition has been tagged with a quantifiable biomarker which helps in faster clinical diagnosis as well as prognostication. Overall this would lead towards better healthcare delivery to the patient. But, the sheer vast numbers and volumes of various biomarkers in the research pipeline points towards a glaring fact. There is no single marker that can give a complete picture of the patient’s clinical condition. There is no ideal biomarker. A scoring system based on multiple markers would give a better picture than a single one. This accuracy always comes at a higher cost, which translates to more expensive healthcare delivery. There is dire requirement for better clinical validation among the various contenders in each disease process A biomarker based evaluation system, though more accurate, may not suit each and every healthcare facility, but needs to be tailored based on the adaptability and cost effectiveness suited to the society it caters to. Given the vast array of biomarker assays, clinicians should keep in mind that these should always be used as tools that compliment your clinical decision making process rather than replacing the process itself.
I would like to acknowledge the role of my wife, Aparna, who has been a pillar of support throughout the writing of this chapter just like she has been throughout our life together. I would also like to acknowledge the support given by my parents, friends as well as colleagues in my department, who made me what I am.
I have no conflict of interest to declare.
ACC | American College of Cardiology |
ACS | Acute Coronary Syndrome |
AST | Aspartate aminotransferase |
BNP | B type natriuretic peptide |
CABG | Coronary Artery Bypass Grafting |
CHF | Congestive Heart Failure |
CNS | Central Nervous System |
COPD | Chronic Obstructive Pulmonary Disease |
CT | Computed Tomography |
ECG | Electrocardiogram |
ED | Emergency Department |
H-FABP | Heart Fatty Acid Binding Protein |
ICU | Intensive Care Unit |
IL | Interleukin |
IMA | Ischemia modified albumin |
LDH | Lactate dehydrogenase |
LVF | Left Ventricular Failure |
MI | Myocardial Infarction |
MR-proADM | Mid-regional Proadrenomedullin |
NACB | National Academy of Clinical Biochemistry |
NGAL | Neutrophil gelatinase-associated lipocalin |
NSTEMI | Non ST Elevation Myocardial Infarction |
NT pro BNP | N-Terminal pro B type natriuretic peptide |
PCI | Percutaneous Coronary Intervention |
POCT | Point of Care Testing |
QC | Quality Control |
STEMI | ST Elevation Myocardial Infarction |
TNF | Tumour Necrosis Factor |
TNF-alpha | Tumour necrosis factor-alpha |
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