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Fibrinaloid Microclots and Atrial Fibrillation

Submitted:

26 January 2024

Posted:

29 January 2024

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Preprints on COVID-19 and SARS-CoV-2
Abstract
Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question as to whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known ‘risk factors’ for AF, and in particular exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots are cytotoxic by inducing hypoxia/reperfusion and other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.
Keywords: 
Atrial fibrillation
;  
fibrinaloid
;  
microclots
;  
long Covid
;  
inflammation
;  
microthromboses
Subject: 
Biology and Life Sciences  -   Biochemistry and Molecular Biology

Introduction

Atrial fibrillation (AF) is at once the commonest heart arrhythmia (Chugh et al. 2014; Corica et al. 2023a; Zulkifly et al. 2018) and a chief cause of death therefrom (Benjamin et al. 1998; Staerk et al. 2017). Its global incidence amounts to millions of individuals (Chugh et al. 2014), possibly 1% of populations, and even 20 years ago its cost represented some 1% of the NHS budget, with all these numbers on a rising trend (Camacho and Lip 2023; Dai et al. 2021; Okafor et al. 2023; Sun et al. 2023), albeit with large regional differences of incidence and/or reporting (e.g. South Asia is said to be 12x lower than North America (Joseph et al. 2021)).
AF symptoms (virtually by definition) involve (Hindricks et al. 2021) “A supraventricular tachyarrhythmia with uncoordinated atrial electrical activation and consequently ineffective atrial contraction. Electrocardiographic characteristics of AF include:
  • Irregular R-R intervals (when atrioventricular conduction is not impaired),
  • Absence of distinct repeating P waves, and
  • Irregular atrial activations.” (Hindricks et al. 2021)
Within this there are, of course, AF subclasses with different levels of severity, especially as regards the various sequelae. AF is also associated with risk factors that rarely occur in isolation, and patients with AF are commonly associated with multimorbidity, polypharmacy and frailty, with major implications for treatment and outcomes (Lip et al. 2022a; Romiti et al. 2022b; Treewaree et al. 2024; Zheng et al. 2023).
The chief fear is that AF leads to an increased likelihood of a clot forming or residing within the atrial chamber, from which it can then escape, and it is well recognised (e.g. (Tsao et al. 2023)) that those with AF are associated with mortality and morbidity from strokes (Alshehri 2019; Lip et al. 2022b; Marini et al. 2005; Ozdemir et al. 2023; Qureshi et al. 2022; Schnabel et al. 2019; Son et al. 2017; Wolf et al. 1991), coronary heart disease (Emdin et al. 2016; Kannel et al. 1983; Odutayo et al. 2016) (including myocardial infarction (Ruddox et al. 2017)) or both (Lip et al. 2023), and vice versa due to the build-up of blood-flow-occluding macro-clots in the relevant tissues. Note, however, that because AF is often asymptomatic, and until recently was rarely screened for, many/most of these studies are actually post hoc (Kalarus et al. 2023; Sposato et al. 2015; Thakur et al. 2023; Yang et al. 2019), i.e. they study the incidence of AF in people who have had cardiovascular events rather than the other way round (i.e., for our present purposes, the incidence of heart attacks and strokes in those with known, pre-existing AF).
Our purpose here is to recognise that a certain kind of ‘fibrinaloid’ microclot that we discovered (Pretorius et al. 2016c), despite being in many ways a ‘conventional’ blood clot formed by the polymerisation of fibrinogen to fibrin (albeit containing other proteins (Kruger et al. 2022; Pretorius et al. 2021)), is by virtue of its amyloid nature (Kell and Pretorius 2023, 2017), rather resistant to fibrinolysis and can persist in the circulation. The presence of these microclots, commonly in the range 2-100 mm, is known to occur in a variety of conditions that may lead to or accompany AF (see below), and some of the molecules that catalyse their production such as bacterial cell wall substances (de Waal et al. 2018; Pretorius et al. 2018a; Pretorius et al. 2016c; Pretorius et al. 2018c; Pretorius et al. 2018d) or the spike protein of SARS-CoV-2 (Grobbelaar et al. 2022; Grobbelaar et al. 2021) are also known. The widespread existence of these fibrinaloid microclots also led us to wonder whether they might thus contribute to the actual genesis of AF. Recognising this as a problem of systems biology (Alon 2006; Kell and Knowles 2006; Klipp et al. 2005; Palsson 2006), the present overview sets out what is actually the considerable evidence for this view. We summarise our review in the form of a ’mind map’ (Buzan 2002; Hull et al. 2008).
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Figure 1. A ‘mind map’ (Buzan 2002) setting out this review. Created with Biorender.com.
Figure 1. A ‘mind map’ (Buzan 2002) setting out this review. Created with Biorender.com.
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“Risk factors”, covariates, confounders, biases and coherence

Much of modern analytics in medicine is concerned with identifying ‘risk factors’, most of which are really in fact covariates (Figure 2). To illustrate this point, we take a different cardiovascular disease (pre-eclampsia) (Kell and Kenny 2016; Kenny and Kell 2018), where because this is a disorder of pregnancy we do at least know the time and nature of the origin (of the pregnancy). Thus, we know that the likelihood of developing pre-eclampsia (equivalent to B in Figure 1) increases with certain pre-conditions (first pregnancy with the father, existing diabetes, maternal age, BMI, blood pressure at first visit, infection, etc) (e.g. (Allotey et al. 2020; Bartsch et al. 2016; Bilano et al. 2014; Elawad et al. 2024; Giannakou et al. 2018; Kell and Kenny 2016; Lee et al. 2022; Machano and Joho 2020; Xue et al. 2023), but a priori there is no way of knowing whether any of the above conditions are truly on a causal pathway or simply covariates (and caused by other factors such as those labelled X and Y in Figure 2). The infeasibility of establishing causality solely from measurements of variables is widely encapsulated in the mantra ‘correlation does not equal causation’, although any co-variation has the potential to contain useful information. Unravelling such relationships by causal inferencing either requires good longitudinal data and/or (better) affecting them as independent variables.
Thus the fact that the long-term use of antibiotics following a toxoplasma infection lowers the risk of pre-eclampsia by a massive 11-fold (Todros et al. 2006), along with a mass of other evidence, strongly implies an infectious origin for pre-eclampsia (Kell and Kenny 2016; Kenny and Kell 2018; Todros et al. 2007), but because these are not always even recognised they do not appear in most lists of risk factors.
Overall, we find useful the principle of coherence, which indicates that if a variety of nominally orthogonal lines of evidence point towards the same mechanism then that mechanism is more likely to be true (Thagard 2012, 2007, 1999). Similarly, we consider that an understanding of comorbidities, where the knowledge available from sets of related diseases may be brought to bear, must enhance the understanding of the disease of particular intellectual interest (Petsko 2009). As Petsko (Petsko 2009) notes, this strategy is well-established e.g. in functional genomics (where it is often called ‘guilt by association’ (e.g. (Gillis and Pavlidis 2012; Oliver 2000; Tian et al. 2008; Voy et al. 2006)), where it uses the idea that the co-expression of genes of unknown function with genes of known function implies a contributory role for the ‘unknown’ genes in the known function. Especially when the associations are between genetic polymorphisms and diseases the activity – then known as genome-wide association studies or GWAS – is seen as entirely respectable (Alsheikh et al. 2022; Baker 2010; Lappalainen et al. 2019). Nowadays, because the statistical risks of false positives are quite considerable when the number of variables (such as SNPs) is large (Benjamini and Hochberg 1995; Broadhurst and Kell 2006; Gunning and Pavlidis 2021; van den Oord 2008), the basis for any co-variation or correlation has to be very well established, and include separate validation sets.
Consequently, although our ultimate aims are mechanistic, we shall start by looking at known ‘risk factors’ and comorbidities of AF, where the term ‘guilt-by-association’ has itself been used (Al-Falahi et al. 2017). We simply note here that the terms ‘bias’ and ‘confounder’ should really just be applied in studies in which relevant and knowable covariates (e.g. gender or pre-existing pill consumption) are inadvertently distributed differently between two populations of interest (such as ‘diseased’ and ‘control’) (Broadhurst and Kell 2006). Other kinds of ‘biases’, such as a focus on particular sets of gene products leading to models that use them, are in fact a significant feature of the now-commonplace large language models (Bommasani et al. 2021) based on transformers (Vaswani et al. 2017).
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Figure 2. Risk factors, observables, causes and covariates.
Figure 2. Risk factors, observables, causes and covariates.
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It is common that observables A and B are seen to covary. There can be many reasons for this, e.g. that A causes B, that B causes A, that something else X causes them both whether sequentially or in parallel, and/or there may be a positive feedback cycle in which A causes B and B causes A. Often we can only measure variables but it is the parameters (‘causes’) that we seek. Since we cannot always influence the parameters directly, one job of systems biology or systems medicine is to seek to reconstruct the causal relations by measurements of the variables. Longitudinal methods can here be especially powerful as a later event cannot directly influence an earlier one. Created with BioRender.com
We begin by rehearsing what we know about fibrinaloid microclots.

What are fibrinaloid microclots?

Clotting and clot removal happen all the time, so that the body is ‘primed’ for any desirable clotting to be initiated rapidly in response say to a wound. At a high level (Figure 3) soluble fibrinogen, typically as a 5 x 45nm complex of three different polypeptides (a2β2γ2) (MW ~ 340 kDa) (Kollman et al. 2009) https://www.rcsb.org/structure/3ghg plus internal fibrinopeptides A and B and one of the most abundant proteins in plasma (present at typically 2-4 g.L-1 (Weisel 2005; Weisel and Litvinov 2017)), is acted upon by the serine protease thrombin. This action removes two fibrinopeptides, exposing ‘knobs’ and ‘holes’ and leading to a remarkable self-assembly in which fibrin monomers polymerise to make staggered oligomers, that themselves lengthen into protofibrils that aggregate laterally to make fibres, finally branching to create a three-dimensional network which represents the clots. Typical fibre diameters are a few hundred nm (say 100-400 nm (Belcher et al. 2023; Collet et al. 2000; Ferri et al. 2011; Li et al. 2016)), with a fractal morphology (Guthold et al. 2004), meaning that a ‘unit’ of fibrin fibre contains many hundreds of fibrinogen monomers contributing to its diameter at any point. Clots are then degraded by plasmin, which itself has a variety of activators and inhibitors (Figure 3). For our purposes, we note that the clots may not be fully formed, that they form anomalous conformations (see below) and that their rate of degradation is in many cases unusually low. This means that there may be, in certain diseases, a standing crop of fibrinaloid microclots.
Much of the left-hand part is redrawn from a CC-BY article at (Kell et al. 2022). Fibrinolysis can be decreased by a variety of endogeneous inhibitors, as well (see below) as when fibrin adopts an anomalous amyloid-type form. Created with BioRender.com
Early studies in the electron microscope by one of us (EP) showed that while images of the fibrin fibres of ‘normal’ clots looked much like nicely cooked spaghetti, those in a variety of chronic, inflammatory and other conditions looked much as if such spaghetti had been parboiled and stuck together in an unholy mess (Pretorius 2011; Pretorius et al. 2009; Pretorius et al. 2010; Pretorius et al. 2011a; Pretorius et al. 2011b; Pretorius et al. 2011c), a finding referred to at the time as ‘dense matted deposits’ (see Figure 4). These anomalous fibres could be induced by the presence of free iron (Kell et al. 2020a; Kell and Pretorius 2018a; Kell and Pretorius 2015; Lipinski and Pretorius 2012; Pretorius et al. 2016a; Pretorius and Kell 2014; Pretorius et al. 2013).
Healthy whole blood was exposed to ferric chloride (raw data from (Lipinski et al. 2012)).
Many proteins can fold into a stabler, beta-sheet-rich ‘amyloid’ form, with no change in sequence. Some of these are related to a variety of more-or-less well known diseases (‘classical amyloidoses’, e.g. (Crystal et al. 2003; Martinez-Naharro et al. 2018; Palladini and Merlini 2013)) in which unfolded forms of proteins such as Ab and a-synuclein are detected, but even proteins such as insulin (Fagihi and Bhattacharjee 2022) and lysozyme (Kuo et al. 2017) can adopt amyloid forms. The apotheosis of this kind of behaviour is represented by prion proteins (e.g. (Aguzzi and De Cecco 2020; Aguzzi and Lakkaraju 2016; Frontzek et al. 2022; Kell and Pretorius 2023, 2017)), that can adopt a variety of stable, amyloid-type states (without changes in primary sequence) that can even catalyse their own (con)formation. We later showed that the ‘dense matted deposits’ were in fact amyloid in character (Kell et al. 2022; Kell and Pretorius 2017; Kruger et al. 2022; Pretorius et al. 2018a, b; Pretorius et al. 2016c; Pretorius et al. 2017b; Pretorius et al. 2017c; Pretorius et al. 2018c; Pretorius et al. 2018d; Pretorius et al. 2022; Pretorius et al. 2020; Turner et al. 2023) as they could be stained with the well-stablished amyloid stain thioflavin T, as well as the commercial ‘Amytracker’ stains (de Waal et al. 2018; Pretorius et al. 2017c; Pretorius et al. 2018c). This was confirmed by correlating images from the electron and fluorescence microscopes (de Waal et al. 2018). Such diseases (discussed in more detail below) were also accompanied by significant platelet activation. Figure 5 illustrates these phenomena.
A particular feature of such clots is that – like prion proteins – they are far more resistant than normal to proteolysis (in this case fibrinolysis) (Kell and Pretorius 2023, 2017), and their sizes can vary widely over the main approximate range 2-100 mm diameter. The addition of known amyloids to clotting systems can induce similar effects (Zamolodchikov and Strickland 2012). Also, like prion proteins, these amyloid forms are thermodynamically more stable than are the non-amyloid form(s) normally adopted, and the abnormal form can catalyse the transformation of the normal form into itself; consequently this can be triggered by minuscule amount of suitable substances, such as bacterial cell wall materials (Pretorius et al. 2016c; Pretorius et al. 2018c) or viral surface proteins (Grobbelaar et al. 2021). Noting that far more small molecules bind to proteins than was widely assumed (e.g. (Kell 2011; Li et al. 2010; Li and Snyder 2011)), it is reasonable that any number of small molecules beyond iron ions might also effect the nature of fibrinogen polymerisation, and certainly some (such as 7-b-oestradiol (Swanepoel et al. 2014; Swanepoel et al. 2016)) are known to do so. Other features known to affect the rate of fibrinolysis (Undas 2014) include fibre diameters (Carr and Alving 1995) and the presence of anti-plasmin(ogen) proteins (Bryk et al. 2020; Kruger et al. 2022; Pretorius et al. 2021). We illustrate the two types of fibrin as a cartoon in Figure 6.
The typical size range of these fibrinaloid microclots can allow them to travel widely through the vasculature, essentially blocking up capillaries of a suitable diameter, inhibiting blood flow and hence O2 transfer to tissues, and thus accounting in principle for the very wide range of symptoms in syndromes such as Long Covid, including fatigue (Kell et al. 2022), post-exertional symptom exacerbation (Kell and Pretorius 2022), and autoantibody induction (Kell and Pretorius 2023). Very recently, amyloid deposits have also been found in muscle tissues (not just plasma) of individuals with Long COVID (Appelman et al. 2024). Armed with this brief summary of fibrinaloid microclots, we now turn to risk factors for atrial fibrillation.

Risk factors for AF that are not to be seen as disease comorbidities

Many studies have examined how the prevalence of AF varies with properties such as age, gender, BMI, etc that are not per se normally seen as comorbidities, and we shall look at them first, mostly to see if they give any hints with the incidence of microclots before we move to the other factors (see e.g. (Zhang et al. 2021a), while the guidelines of Hindricks and colleagues provide a comprehensive list (Hindricks et al. 2021)). These few are summarised in table 1. The focus here is on prevalence more than outcomes, since the application of therapies is not necessarily uniform (Essien et al. 2022).
Table 1. Variation of the prevalence of AF with certain properties of individuals.
Table 1. Variation of the prevalence of AF with certain properties of individuals.
Risk factor Comments Selected references
Age Normally a significant increase in AF with age (Hindricks et al. 2021; Lowres et al. 2019; Marinigh et al. 2010; Zhang et al. 2021a)
In some cases the opposite can be true for athletes. (Ayinde et al. 2018; Goudis et al. 2015)
May involve age-dependent Na+ channel expression (Isaac et al. 2020)
BMI Obesity is sometimes a clear risk factor (Benjamin et al. 1998) (as for many metabolic diseases), but there are also (and more commonly) a variety of so-called ‘obesity paradoxes’ where the hazard ratios for acquiring or manifesting AF, and in particular suffering disease sequelae therefrom, are actually significantly greater for those with a lower BMI. (Li et al. 2022; Liu et al. 2020; Overvad et al. 2013; Panchal et al. 2019b; Proietti and Boriani 2020; Proietti et al. 2017; Rodríguez-Reyes et al. 2021; Sandhu et al. 2016; Sandhu et al. 2018; Wanahita et al. 2008; Wang et al. 2004; Wu et al. 2023b; Zhu et al. 2016)
Obesity may induce sleep apnoea, which is a known risk factor for AF (Al-Falahi et al. 2017; Benjafield et al. 2019; Khan et al. 2018; Zhang et al. 2021b)
Ethnicity More prevalent among Caucasians; not entirely clear how much is genetics, culture/lifestyle, or GxE, and as with genetics no studies really seek to deconvolve these factors (Zhang et al. 2021a)
Familial associations/ Genetics Mostly less significant than lifestyle factors and co-morbidities, apart from some particular and relatively uncommon ion channelopathies (Feghaly et al. 2018);
Monozygotic:dizygotic ratio does predict a role for genetics, so not purely cultural associations (Christophersen et al. 2013; Christophersen et al. 2009)
Highly polygenic, with genes involved in developmental, contractile, and electrophysiological functions. Necessarily convolved with GxE association that cannot be interpreted from GWAS studies alone. (Kalstø et al. 2019; Lee et al. 2023; Roselli et al. 2018; Selewa et al. 2023; Weng et al. 2017)
Gender More prevalent in males, though outcomes can be worse for females (so being female contributes to the CHA2DS2-VASc score (Friberg et al. 2012; Lip et al. 2010)). Less important than age for asymptomatic AF. (Li et al. 2019; Xiong et al. 2015; Zhang et al. 2021a)
Interestingly, with the possible exception of age, none of these is especially associated with AF (independently of disease) and neither is the prevalence of microclots (for instance age and male gender do associate with acute covid but long covid is far more prevalent in women (and often not the older ones)). That is already a useful test, because if there were very strong associations with ‘pure’ AF but not with microclots it would be harder to argue for a major role of microclots in AF or vice versa.

Risk factors for AF based on lifestyle factors

While BMI might have been placed in this category, (i) it is an effect as much as a cause, (ii) a high BMI can cover a multitude of physiques, such that (iii) a high BMI in a professional rugby player would not necessarily be seen as significant a risk factor as it might be in an office worker. However, other lifestyle choices are more obviously under the control of individuals, e.g. alcohol consumption, and we next look at these (Table 2). Although exposure to particulate matter may not be seen as a lifestyle choice, urban vs rural living is one and particulate matter exposure (especially from cars) is far worse in the former, and the effects are substantial. They also show the importance of particulate irritants, a category into which fibrinaloid microclots might be considered to fall.
Table 2 implies that within reasonable bounds these lifestyle factors have a measurable but not massive influence on the appearance of AF in the population. However, we note that they are modifiable.

Risk factors that are recognised as known disease comorbidities

Most chronic, inflammatory diseases share many properties (Kell and Pretorius 2018a), including inflammation (Kell et al. 2022), oxidative stress (Kell and Pretorius 2022), and iron dysregulation (Kell 2009; Kell 2010; Kell et al. 2020b; Kell and Pretorius 2014) (and also, as we shall see later and in Table 3, fibrinaloid microclots). This of itself might lead one to suppose that they have a broadly similar, ultimate type of cause (i.e. something labelled “Y” in Figure 1), and the evidence for this “something” points rather squarely at an infectious origin (see table 3).
Apart from multiple sclerosis and sleep apnoea, that we have not yet studied, it is striking that each of the supposedly non-communicable diseases listed in Table 3 can be seen to have an infectious origin, and we know that components of both bacteria (e.g. LPS, lipoteichoic acid) and viruses (e.g. SARS-CoV-2 spike protein) can induce the fibrinaloid microclots (see also below). However, our next task is to list some of these comorbidities on the grounds that they surely contain clues as to the origins of the syndrome of our present, prime focus, viz atrial fibrillation.
Thus Table 4 lists some of the known comorbidities of various diseases and AF. Because of the relative lack, until recently, of long-term screening, it is not normally easy to determine longitudinal trends (potentially then causal chains), but for present purposes an association is sufficient, before we move to cases in which we absolutely know that a specific infection can lead to AF. Table 4 makes clear that each of the diseases there also makes an appearance in Table 4, implying that they are related to each other, whether as effects of an earlier cause (as we consider likelier, Fig 1) or on their own causal chain in either direction, or both.

Examples in which we know that infection can lead to atrial fibrillation

The conclusion from the above is that there is strong associative evidence for comorbidities of supposedly non-infectious diseases, in which fibrinaloid microclots have been demonstrated, and AF. Unfortunately, however, in most cases a causative mechanism (or a set of mechanisms or chain of causal reasoning) is not to hand.
However, infections represent a class of disease in which the temporal origin (of the infection) is usually known from early symptoms, so it is reasonable to ask the question of whether infection is known to lead to AF in populations previously known not to manifest it. Not least since the appearance of Long COVID (albeit there are many similar post-infection syndromes such as ME/CFS, Gulf War syndrome, post-Ebola syndrome, etc)), we have come to recognise that a variety of infections can cause major and sometimes debilitating symptoms for extended periods after the infection has nominally cleared. In the same way, pre-eclampsia is associated with a significantly increased risk of cardiovascular disease (Thilaganathan and Kalafat 2019; Wu et al. 2017), often for many years after the pregnancy in question.
The following facts relating infection to subsequent AF are thus highly pertinent:
  • 7.6% of cases of community acquired pneumonia led to new-onset AF (Corica et al. 2023b)
  • AF is a common occurrence following infection with SARS-CoV-2 (COVID-19) (Abbasi 2022; Al-Abbas et al. 2021; Bagnato et al. 2022; Bhatla et al. 2020; Duckheim and Schreieck 2021; García-Granja et al. 2021; Kanuri et al. 2023; Mouram et al. 2022; Niehues et al. 2022; Rav-Acha et al. 2021; Wu et al. 2023a)
  • The Odds Ratio OR for AF 365 days after COVID-19 compared to a well-established control group was 1.83 in a large study (Berman et al. 2023).
  • A previous use of DOACs is protective against AF following SARS-CoV-2 infection (Azaña Gómez et al. 2022). Note that AF increased the bleeding risk of those on anticoagulants (Rubini-Costa et al. 2022).
  • There was an increased mortality from acute COVID-19 in patients with AF (Musikantow et al. 2021; Pardo Sanz et al. 2021; Zuin et al. 2021), especially older ones.
  • The same applies to Long COVID (Huseynov et al. 2023) (which is not surprising giving its incidence following acute COVID). Incidence after covid and refs
  • Cardiac arrhythmias also seem to be caused by COVID-19 vaccination (Pari et al. 2023) (which of course includes spike protein or RNA coding for it) and spike protein is known to cause microclots (Grobbelaar et al. 2022; Grobbelaar et al. 2021).
  • New-onset AF is a common occurrence in sepsis (which of course convolves e.g. infection and inflammation), increasing as sepsis leads to severe sepsis and then septic shock, and leading to poorer outcomes (Aibar and Schulman 2021; Bashar et al. 2020; Bosch et al. 2019; Corica et al. 2022; Downes et al. 2023; Honorato et al. 2023; Induruwa et al. 2022; Klein Klouwenberg et al. 2017; Kuipers et al. 2014; Proietti and Romiti 2021; Walkey et al. 2013; Xiao et al. 2021).
  • Sepsis (we ignore subtypes (Cano-Gamez et al. 2022; van Amstel et al. 2023)) likely involves microclots (Kell and Pretorius 2018b), which can be induced experimentally in the presence of cell-surface components of infectious agents such as bacterial lipopolysaccharide (Pretorius et al. 2018a; Pretorius et al. 2016b; Pretorius et al. 2018c; Pretorius et al. 2018d) or lipoteichoic acid (Pretorius et al. 2018c), or the spike protein of SARS-CoV-2 (Grobbelaar et al. 2022; Grobbelaar et al. 2021; Pretorius and Kell 2023)
  • Coagulation, in the worst case disseminated intravascular coagulation, is a frequent accompaniment of sepsis (Allen et al. 2015; Gando 2010; Iba et al. 2013; Levi 2010; Levi et al. 1997; Li and Ma 2017; Murao and Yamakawa 2019; Yu et al. 2022)
  • Anticoagulants are significantly protective against the complications of sepsis when timed properly (Meziani et al. 2017; Scarlatescu et al. 2017) and especially in the presence of disseminated intravascular coagulation (Qi et al. 2023; Umemura et al. 2023; Umemura and Yamakawa 2018)
This raises the suspicion that coagulopathies may often precede AF, and before we look at this we shall rehearse a few of the potentially relevant biomarkers.

Infection and Stroke

We note that stroke and infection are often associated (Bustamante et al. 2017; Kazemi et al. 2021; Kishore et al. 2018; Shim and Wong 2016; Westendorp et al. 2018), and when co-occurring often lead to an unfavourable outcome (Vermeij et al. 2009; Westendorp et al. 2011) (implying some co-causality). Infections associated with stroke are usually called or referred to as a ‘post-stroke infection’ (that is following a stroke), but it is equally plausible (and in some cases demonstrable (Mélé and Turc 2018)) that the earlier stages of infection precede the stroke event that is referred to (Westendorp et al. 2011). Some of the evidence for this includes the fact that similar changes in the gut microbiome could be predictive of both stroke itself and post-stroke infection (Haak et al. 2021). However, apart from lowering urinary tract infections, prophylactic antibiotics were mostly not preventive (Kalra et al. 2015; Vermeij et al. 2018; Westendorp et al. 2021; Westendorp et al. 2015), cf. (Badve et al. 2018; Badve et al. 2019; Sluis et al. 2022); one interpretation of this is simply that the amount of bacterial cell wall product in the plasma necessary to induce microclots is absolute minuscule (Pretorius et al. 2016c; Pretorius et al. 2018c), and a tiny fraction of the total bacterial load within a person. Note, however, that certain antibiotics also increase the release of such amyloidogenic bacterial cell wall materials (Jackson and Kropp 1992; Mickiewicz et al. 2019; Opal et al. 1996; van Langevelde et al. 1998), thereby negating (or worse) the benefits of antibiosis per se.
One might also add here that the same co-association and likely causation, for which antibiotics are also not protective (Huang et al. 2018), is also true of Parkinson’s disease (Dardiotis et al. 2018; Meng et al. 2019; Smeyne et al. 2021; Wang et al. 2020).

Some biochemical changes accompanying AF

Given the above, and a variety of biomarkers that might have something useful, it is also of interest to survey biochemical markers that might also correlate with AF. Table 5 summarises some markers that have been found to be raised in AF and that might be related to the genesis or presence of coagulopathies generally, and potentially fibrinaloid microclots.

Virchow’s triad: coagulopathies and thrombogenic potential as predictors of AF

Although, as mentioned, AF is widely recognised as predictive of thrombus formation, our proposal is that the converse is also true (if not even more so). Is there further evidence for this? While our preferred metric is the presence of fibrinaloid microclots (measures such as D-dimer reflecting those of clot breakdown rather than clot presence (Kell et al. 2022)), these have not been done, so we need to look to more traditional measures of clotting potential, where similar suspicions have been raised (Danese et al. 2014; Ding et al. 2020; Ding et al. 2022b; Downes et al. 2023; Marín et al. 2003), and some limited longitudinal evidence brought forward (Tilly et al. 2023).
“Virchow’s triad” reflects or consists of ’abnormal blood constituents’, ’vessel wall abnormalities’ (endothelialitis) and ’abnormal blood flow’ (Bagot and Arya 2008), a set of coagulopathies leading to venous thrombosis, that also occur in AF (Ali et al. 2022; Arvanitis et al. 2022; Chung and Lip 2003; Darlington and McCauley 2020; Ding et al. 2020; Freestone et al. 2008a; Freestone and Lip 2008; Khan and Lip 2019; Qureshi et al. 2022; Schnabel et al. 2019; Watson et al. 2009; Yamashita 2016) and indeed are common in both acute (Ahmed et al. 2020; Carbillon et al. 2021; Conway et al. 2022; Flaumenhaft et al. 2022; Gonzalez-Gonzalez et al. 2021; Mehta et al. 2020; Wadowski et al. 2023; Zheng et al. 2020) and Long (Invernizzi et al. 2021; Jing et al. 2022; Zanini et al. 2024) COVID (which of course are also characterised by fibrinaloid microclots). Note again that the special feature of these fibrinaloid microclots which not only makes them easy to see but makes them significantly more resistant to the normal means of fibrinolysis. This again points strongly to the potential for microclots as being causative in AF and not merely a consequence. Similarly, it is easy to suppose that microclots are potentially able to aggregate into the better known microclots; it seems highly desirable to test these as to whether or not they are amyloid in character.

Clinical risk scores, e.g. CHA2DS2-VASc

From the point of view of risk factor analysis, the main present assessment for predicting the risk of stroke in individuals known to have AF is known as (and leads to) clinical risk scores, such as the CHA2DS2-VASc score (Lip et al. 2010), a backronym related to Congestive Heart Failure, Hypertension, Age (>75y) (2 points), Diabetes Mellitus, prior Stroke or TIA or thromboembolism (2 points), Vascular disease, Age (again, 65-74y) and Sex category (female). The elements not marked as two points score one point, and age is either one or two points, giving a maximum score of 9. These are now in wide common use in guidelines globally (Chao et al. 2022; Writing Committee Members et al. 2024).
However, notwithstanding that strokes are clearly caused by microclots, as phrased by Qureshi et al. (Qureshi et al. 2022) these scores “rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad.” In view of the above arguments, it does seem very timely to revisit these, and indeed to develop new methods of assessment based on markers of thrombotic problems including biochemical markers such as those in Table 5, more physiological methods of endothelial dysfunction such as flow-mediated dilatation (Freestone et al. 2008a), and in particular fibrinaloid microclots. Given the tendency of unfolded/amyloid proteins to nucleate and increase in size (e.g. (Camino et al. 2021; Cawood et al. 2021; Chi et al. 2010; De and Klenerman 2019; Michaels et al. 2017; Michaels et al. 2023; Seuma et al. 2021; Seuma et al. 2022; Taylor et al. 2018)), it is highly plausible that microclots may serve as precursors to macroclots. Figure 7 provides a summary and overview of the kinds of evidence that we have brought together here.

Machine learning in AF

As we move towards a post-genomic, data-driven, biology (Kell and Oliver 2004), it is increasingly recognised that hypothesis-free methods can play a valuable role in deconstructing complex phenomena. Thus, within the AF field, machine learning has been applied to the predictive risk of many inputs (Sekelj et al. 2021; Tseng and Noseworthy 2021), including more narrowly on ECGs (Rizwan et al. 2021; Siontis et al. 2020) and in COVID-19 (Lip et al. 2021). In one study, 15% of AF patients assigned to the AF cohort by the algorithm had a secondary care diagnosis with no record of AF in primary care (Pollock et al. 2020). Implementing such algorithms would be highly cost effective (Szymanski et al. 2022).

Discussion, conclusions, and a forward look

Although we have focused here on AF, we recognise that many other cardiac and (more generally) cardiovascular disorders are also accompanied by thromboses of various kinds. These include type 2 diabetes (Grant 2007; Konieczynska et al. 2014), heart failure (Karaban et al. 2024), myocarditis (Antoniak et al. 2008), peripheral arterial disease (Bennett et al. 2009), various disorders of pregnancy (Staff et al. 2016), and others that are not always (but should be) seen to be associated with vascular problems, such as Alzheimer’s and Parkinson’s (Roy et al. 2022). Cardiac amyloidosis (Martinez-Naharro et al. 2020; Martinez-Naharro et al. 2019) are of especial interest here. The multifaceted clinical complexity of patients with AF has led to the current overall holistic or integrated care management approach to AF care (Romiti et al. 2023; Romiti et al. 2022a), as is recommended in guidelines (Chao et al. 2022).
Learning from this collective of syndromes allows one to see common factors, at least one of which involves fibrinaloid microclots. Up to now these have not been studied as an independent risk factor for AF. This clearly needs to change.

Funding

EP: Funding was provided by NRF of South Africa (grant number 142142) and SA MRC (self-initiated research (SIR) grant), and Balvi Foundation. DBK thanks the Balvi Foundation (grant 18) and the Novo Nordisk Foundation for funding (grant NNF10CC1016517). The content and findings reported and illustrated are the sole deduction, view and responsibility of the researchers and do not reflect the official position and sentiments of the funders.

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Figure 3. A high-level illustration of fibrinogen polymerisation into fibrin as a part of blood clotting.
Figure 3. A high-level illustration of fibrinogen polymerisation into fibrin as a part of blood clotting.
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Figure 4. Scanning electron microscopy figure of dense matted deposits entrapping a red blood cell.
Figure 4. Scanning electron microscopy figure of dense matted deposits entrapping a red blood cell.
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Figure 5. Microclots and platelets in healthy individuals and in individuals with Long COVID (raw data from (Lipinski et al. 2012).
Figure 5. Microclots and platelets in healthy individuals and in individuals with Long COVID (raw data from (Lipinski et al. 2012).
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Figure 6. Cartoon illustrating the ability of fibrinogen to polymerise either to its a-helix-rich normal form or its crossed-b-sheet amyloid ‘fibrinaloid’ form depending on the presence of various small-molecule triggers. Glyphs taken from the CC-BY Open Access publication (Laubscher et al. 2021). Created with BioRender.com.
Figure 6. Cartoon illustrating the ability of fibrinogen to polymerise either to its a-helix-rich normal form or its crossed-b-sheet amyloid ‘fibrinaloid’ form depending on the presence of various small-molecule triggers. Glyphs taken from the CC-BY Open Access publication (Laubscher et al. 2021). Created with BioRender.com.
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Figure 7. Some of the features on which we have focused in this review, indicating those (indicated by thick arrows) for which there is well established evidence, plus others that we consider likely but for which further evidence needs to be sought. Created with BioRender.com.
Figure 7. Some of the features on which we have focused in this review, indicating those (indicated by thick arrows) for which there is well established evidence, plus others that we consider likely but for which further evidence needs to be sought. Created with BioRender.com.
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Table 2. Variation of the prevalence of AF with certain ‘lifestyle’ risk factors.
Table 2. Variation of the prevalence of AF with certain ‘lifestyle’ risk factors.
Risk factor Comments Selected references
Alcohol consumption Some increase in AF risk as a function of alcohol intake; greater in men; studies mainly not controlled for BMI. Not a huge effect for moderate levels of consumption. (Djoussé et al. 2004; Mukamal et al. 2005; Panchal et al. 2019b)
Exercise As with BMI, the relationship is nonlinear, with moderate exercise and general cardio-respiratory fitness being beneficial but excess exercise (which could cause oxidative stress (Powers et al. 2020; Thirupathi et al. 2020), inflammation (Cerqueira et al. 2019; da Rocha et al. 2019), hypoxaemia (Durand and Raberin 2021), etc) having negative effects. (Aizer et al. 2009; Panchal et al. 2019b; Qureshi et al. 2015)
Particulate matter exposure New-onset AF can follow exposure to particulate matter.This can be an acute occurenceIncreases with known genetic risk factors Meta-analyses.Particulate matter is also amyloidogenic (Blaszczyk et al. 2023; Link et al. 2013; Mandaglio-Collados et al. 2023; Zhang et al. 2023)(Ma et al. 2023) (Shao et al. 2016; Wang et al. 2021; Yue et al. 2021) (Calderón-Garcidueñas et al. 2008; Iaccarino et al. 2021; Jang et al. 2018; Sahu et al. 2021)
Psychosocial Stress As estimated by surrogates reflecting anger and hostility, can be a minor risk factor in men but not women, even after controlling for hypertension. (Eaker et al. 2004; Panchal et al. 2019b)
Smoking Although important to other cardiovascular diseases, for AF seemingly a marginal risk, and probably dwarfed by other risks of smoking such as lung cancer (Panchal et al. 2019a)
Table 3. Some supposedly non-communicable diseases for which there is in fact substantial evidence of an infectious element and/or evidence of fibrinaloid microclots.
Table 3. Some supposedly non-communicable diseases for which there is in fact substantial evidence of an infectious element and/or evidence of fibrinaloid microclots.
Disease or syndrome Comments References regarding an infectious origin References illustrating anomalous clotting/ microclots
Alzheimer’s Many references, not least from Ruth Itzhaki focusing on HSV, imply this strongly. Other organisms have also been implicated. (Grobler et al. 2023; Itzhaki 2018; Itzhaki et al. 2008; Itzhaki and Lathe 2018; Itzhaki et al. 2016) (de Waal et al. 2018; Pretorius et al. 2016a; Pretorius et al. 2018a)
Diabetes, type 2 Originally asked by Gundersen in 1927. Even greater evidence for type 1 (Jean-Baptiste et al. 2017), not covered here. (Craighead 1975; Gundersen 1927; Notkins 1977; Rajsfus et al. 2023; Yoon and Notkins 1983) (Pretorius et al. 2011a) (de Waal et al. 2018; Pretorius et al. 2015; Pretorius et al. 2017c; Pretorius et al. 2020) (Pretorius et al. 2022; Soma and Pretorius 2015)
Many reviews (also those with T2D are more susceptible to infections; this direction is not discussed here). (B 2022; Nobs et al. 2023; Sohail et al. 2022)
Increased diabetes prevalence following COVID-19 infection (Naveed et al. 2023)
Multiple sclerosis Now recognised as being caused by Epstein-Barr virus (Rousseau and Bhaduri-McIntosh 2023; Schönrich et al. 2022; Wekerle 2022) Not yet studied
Myalgic encephalitis/ chronic fatigue syndrome (ME/CFS) Clear infectious origin, likely viral and most likely a herpes virus (Nunes et al. 2023) (Nunes et al. 2023; Nunes et al. 2022)
Parkinson’s Induction of disease progression by bacterial LPS and by viruses (Cannon and Gruenheid 2022; Leta et al. 2022) (de Waal et al. 2018)
Reviews (Caggiu et al. 2019; Limphaibool et al. 2019)
Rheumatoid arthritis Absolutely clear evidence for Proteus spp. as the infectious agent (Ebringer 2012; Ebringer and Rashid 2014, 2009; Ebringer et al. 2010) (Bezuidenhout et al. 2020; Kell and Pretorius 2022; Pretorius et al. 2017a; Pretorius et al. 2012)
Sleep Apnoea Obstructive Sleep Apnoea is a strong risk factor or comorbidity of AF, also associated with obesity (Al-Falahi et al. 2017; Goudis and Ketikoglou 2017; Staerk et al. 2017; Zhang et al. 2021b) and both acute (Miller and Cappuccio 2021) and Long COVID (Mandel et al. 2023) (Chiner et al. 2016; Hariyanto and Kurniawan 2021; Su et al. 2014) Not yet studied
Table 4. Some diseases or comorbidities known to be associated with AF.
Table 4. Some diseases or comorbidities known to be associated with AF.
Disease Comments Selected references
Alzheimer’s AF is of course related to age, as is AD. Stroke is also related to vascular dementia. Strong comorbidities between cardiovascular disease and AD, (Ihara and Washida 2018)(Benenati et al. 2021)
Some indications that AF is associated with exacerbation of the onset of AD and related dementias, but not causally (Falsetti et al. 2018; Manemann et al. 2023; Nakase et al. 2023; Pan et al. 2020; Papanastasiou et al. 2021; Proietti et al. 2020)
Diabetes, type 2 Very strong association of AF with diabetic complications, and of diabetes increasing the risk of AF (Alijla et al. 2021; Bohne et al. 2019; Choi et al. 2023; Ding et al. 2022a; Domek et al. 2020; Kwon et al. 2023; Lorenzo-Almorós et al. 2023; Wang et al. 2019; Xiong et al. 2018)
Parkinson’s Some evidence of an association of AF with early PD, much less so if PD diagnosed later (i.e. evidence either way there is relatively weak) (Alves et al. 2021; Cereja et al. 2023; Han et al. 2021; Hong et al. 2019)
Rheumatoid arthritis Small, significant association,but confounded with use of small molecule drugsAlso associated with greater risk of cardiovascular disease (Rong et al. 2023)(Kim et al. 2023)(Kerola et al. 2021; Semb et al. 2022)
Table 5. Some markers that are raised in AF and that have been related to the genesis or presence of fibrinaloid microclots and coagulopathies generally.
Table 5. Some markers that are raised in AF and that have been related to the genesis or presence of fibrinaloid microclots and coagulopathies generally.
Biochemical marker Comments Selected references
Ferritin Serum ferritin is a marker of cell death (Kell and Pretorius 2014), whose accompanying release of free iron can cause microclots and may itself be induced by them or other traumas. It is significantly raised in AF (Altieri et al. 2022; Mikkelsen et al. 2019)
Fibrinogen Fibrinogen (including g’ (Farrell 2012)) fibrinogen levels are commonly raised in inflammatory diseases (Luyendyk et al. 2019). Fibrinogen levels are higher in individuals with AF (and in those having a higher CHA2DS2-VASc score and likelihood of stroke), consistent with a role of microclots in the onset of AF (Bao et al. 2023; Di Lecce et al. 2003; Li-Saw-Hee et al. 2001; Lip et al. 1996b, 1995; Mukamal et al. 2006; Rafaqat et al. 2022; Semczuk-Kaczmarek et al. 2019; Tilly et al. 2023; Weymann et al. 2017)
Fibrin clot properties also relate to stroke likelihood/severity in AF, though no amyloid measurements were yet made (Drabik et al. 2020; Drabik et al. 2017)
Inflammation Occurs (by definition) in all kinds of chronic, inflammatory disease (Kell and Pretorius 2018a), but is certainly associated with AF. An accompaniment to all syndromes involving microclots. (Korantzopoulos et al. 2018; Paquissi 2016; Rafaqat et al. 2022)
Plasminogen Activator Inhibitor-1 (PAI-1) Significantly raised in AF, potentially reducing the rate at which fibrinaloid microclots might be removed (Li et al. 2021; Marín et al. 1999 ; Pretorius et al. 2007 )
Platelet Factor-4 and platelet activation Platelet activation is another key feature of chronic, inflammatory diseases accompanied by microclots (Drabik et al. 2015; Sohara et al. 1997; Yamauchi et al. 1986)
b-Thromboglobulin Raised in AF (Kamath et al. 2002; Lip et al. 1996a; Yamauchi et al. 1986 )
Troponin (cardiac isoforms) Probably more a metric of severity of cardiac events, but as a measure of cell death (like ferritin) may have predictive value (Bai et al. 2018; Cortés et al. 2022; Costabel et al. 2017; Kaura et al. 2020; Lucrecia Maria et al. 2020; Sepehri Shamloo et al. 2021)
Von Willebrand Factor Note that it is unlikely to be a simple function, as too much or too little can be bad (Grobler et al. 2020) (Freestone et al. 2008b; Weymann et al. 2017; Zhong et al. 2018)
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