Relative hypercoagulopathy of the SARS-CoV-2 Beta and Delta variants when compared to the less severe Omicron variants is related to TEG parameters, the extent of fibrin amyloid microclots, and the severity of clinical illness.

doi:10.21203/rs.3.rs-1970823/v1

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Relative hypercoagulopathy of the SARS-CoV-2 Beta and Delta variants when compared to the less severe Omicron variants is related to TEG parameters, the extent of fibrin amyloid microclots, and the severity of clinical illness.

https://doi.org/10.21203/rs.3.rs-1970823/v1

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Earlier variants of SARS-CoV-2 have been associated with plasma hypercoagulability (as judged by thromboelastography) and an extensive formation of fibrin amyloid microclots, which are considered to contribute to the pathology of the coronavirus 2019 disease (COVID-19). The newer Omicron variants appear to be far more transmissible, but less virulent, even when taking immunity acquired from previous infections or vaccination into account. We here show that while the clotting parameters associated with Omicron variants are significantly raised over those of healthy, matched controls, they are only raised to levels significantly lower than those seen with more severe variants such as Beta and Delta. We also observed that individuals infected with Omicron variants manifested less extensive microclot formation in platelet poor plasma compared to those harbouring the more virulent variants. The measurement of clotting effects between the different variants acts as a kind of ‘internal control’ that demonstrates the relationship between the extent of coagulopathies and the virulence of the variant of interest. This adds to the evidence that microclots play an important role in determining the severity of symptoms observed in COVID-19.

Hematology

COVID-19

Variants

Omicron

Coagulation

Fluorescence Microscopy

Microclots

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for coronavirus disease 2019 (COVID-19), has resulted in more than 6.3 million deaths (as of 23 June 2022) worldwide ¹. Adaptive mutations in the SARS-CoV-2 viral genome can alter its pathogenic potential by effecting the ability of the virus to evade the immune system ². As of December 11, 2021, the WHO has reported five SARS-CoV-2 variants of concern (VOCs): Alpha (B.1.1.7) in December 2020; Beta (B.1.351) in December 2020; Gamma (P.1) reported early January 2021; Delta (B.1.617.2) reported in December 2020; and Omicron (B.1.1.529) reported in November 2021 ^2–9 (see Table 1).

Table 1

WHO list of SARS-CoV-2 Variants of Concern and Variants of Interest

(Modified from: ⁹).
WHO label	PANGO linage	Earliest documented samples	Date of designation
Variants of Concern (VOC)
Omicron	B.1.1.529	Multiple countries, Nov-2021 (SA 4th wave)	26-Nov-2021
Previously circulating VOC
Alpha	B.1.1.7	United Kingdom, Sep-2020 (SA 1st wave)	18-Dec-2020
Beta	B.1.351	South Africa, May-2020 (SA 2nd wave)	18-Dec-2020
Gamma	P.1	Brazil, Nov-2020	11-Jan-2021
Delta	B.1.617.2	India, Oct-2020 (SA 3rd wave)	11-May-2021
Variants of Interest (VOI)
Epsilon	B.1.427/B.1.429	United States of America, Mar-2020	5-Mar-2021
Zeta	P.2	Brazil, Apr-2020	17-Mar-2021
Eta	B.1.525	Multiple countries, Dec-2020	17-Mar-2021
Theta	P.3	Philippines, Jan-2021	24-Mar-2021
Iota	B.1.526	United States of America, Nov-2020	24-Mar-2021
Kappa	B.1.617.1	India, Oct-2020	4-Apr-2021
Lambda	C.37	Peru, Aug-2020	14-Jun-2021
Mu	B.1.621	Colombia, Jan-2021	30-Aug-2021

COVID-19 has resulted in five distinct waves in South Africa ^5–8,10,11. The first wave was caused by multiple lineages and peaked in July 2020 ^12,13. The Beta VOC (501Y.V2) drove the second wave of infections that started in Nelson Mandela Bay in October 2020. This was followed by a third Delta (B.1.617.2) VOC driven wave, that was determined to have ten mutations in the spike protein. The latest VOC, Omicron (B.1.1.529) (with more than 30 changes to the spike protein), was first identified in Botswana and South Africa in November 2021, and divided into sub-linages; BA.1 (the main clade), BA.2, and BA.3 ⁵. The first wave associated with Omicron in South Africa was determined to have passed its peak by December 2021 ¹⁴. This fourth wave of COVID-19 cases in South Africa was characterized by a higher and quicker peak with fewer hospital admissions ¹⁵. In April 2022 two new sub-lineages of the Omicron VOC, known as BA.4 and BA.5 were also discovered, resulting in a fifth wave ^16,17. The WHO is closely tracking the sub-linages to determine the potential these sub-linages have for transmissibility and disease severity ⁹.

It is well-established that the earlier variants (before Omicron) caused severe disease and resulted in coagulopathies in critically ill patients ^18–36. In contrast, the heavily mutated Omicron variants have been shown to have milder symptoms than the earlier variants. The five most prevalent symptoms during Omicron infection were reported to be runny nose, rhinitis headache, fatigue (either mild or severe), sneezing, and sore throat ^37–40. Milder infections could be a result of other factors apart from Omicron variants, such as effects of previous infection and vaccination protection ⁴¹. However, multiple studies have indicated a reduced or no effect of different COVID-19 vaccines against Omicron variants ^42–44.

Using point-of-care technologies such as thromboelastography (TEG®) poses an opportunity to improve early management of severe COVID-19-associated coagulopathy ^29,45. In conjunction with this, our research group has suggested that fibrin amyloid microclot (currently only available as a research laboratory tool) might be of great importance to determine clotting pathology in individuals with acute COVID-19 infection ^29,36. Microclots are defined as fibrinogen (and other plasma proteins) that clot into an anomalous ‘amyloid’ from of fibrin (‘fibrinaloid’) with higher than normal resistance to fibrinolysis, and a size range from 1-200µm when measured on the longest axis ²⁷. Our group have previously shown amyloid fibrin deposits in numerous inflammatory conditions ^46− 53. However, the extent of the microclot presence in both acute COVID-19 and Long COVID was found to be significantly more than in other chronic systemic inflammatory conditions ^32,36.

Due to the reduction in Omicron symptoms and fatalities, we investigated the differences between new Omicron VOCs and the previously circulating SARS-CoV-2 variants using TEG® clotting profiles and an assessment of the extent of microclots in the plasma by means of fluorescence microscopy ^29,36. Because of the nature of the blood collections from patients who reported at our clinical collaborator’s practice, we did not focus on a specific sub-lineage of the Omicron variant.

Blood sample preparation

Citrated blood samples were collected and centrifuged at 3000xg for 15 minutes where after the platelet poor plasma (PPP) were stored at -80°C, until analysed later, with the haematocrit being analysed on the day the sample was received.

Participant demographics (see Table 2)

Healthy participants

Our healthy samples comprised 10 individuals who were SARS-CoV-2 negative, and with no signs of long COVID (in case they had previously been infected), or any known inflammatory or cardiovascular diseases.

Participants with COVID-19: variants pre-October 2021

Whole blood (WB) samples were collected from 10 COVID-19-positive participants between October 2020 and September 2021 before treatment was administered. This group thus include individuals with the Beta and Delta COVID-19 variant and will be denoted in this paper as β/Δ. Six of the patients were hospitalized.

Participants with COVID-19: Omicron infection (January 2022- onwards)

WB samples were collected from 10 outpatient COVID-19-positive individuals before treatment was started. Whole SARS-CoV-2 genome sequences of this subset of patients were generated from nasopharyngeal swabs using the Midnight protocol on the GridION device (Oxford Nanopore Technologies, Oxford, United Kingdom) ⁵⁴. Of the 10 samples, six were confirmed to be Omicron (2 BA.1, 3 BA.2 and 1 BA.4). Four of the samples had inconclusive results, however we expect these samples to form part of the Omicron clade as it was the prevailing variant at the time of sample collection ⁸.

Table 2

TEG® parameters for whole blood^29,56.
Thromboelastography®
TEG® parameters	Explanation
Reaction time (R)	Time of latency from start of test to initial fibrin formation (amplitude of 2mm); i.e., initiation time.
Kinetics (K)	Time taken to achieve a certain level of clot strength (amplitude of 20mm); i.e., amplification.
Alpha angle (A)	The angle measures the speed at which fibrin build up and cross linking takes place, hence assesses the rate of clot formation, i.e., thrombin burst.
Maximum amplitude (MA)	Reflects the ultimate strength of the contracted platelet-fibrin clot.

WHO Clinical Progression Scale assessment of COVD-19 patients

All participants in the study diagnosed with COVID-19 were scored by our clinical collaborators using the WHO Clinical Progression Scale. This scale serves as a minimum set of common outcome measures for clinical research on COVID-19 ⁵⁵. The scale ranges from 0 (uninfected) − 10 (dead). Scores 1–3 represent ambulatory mild disease ranging from asymptomatic to symptomatic needing assistance. Scores 4–5 are given to hospitalized patients with moderate disease: no oxygen support (score 4), and oxygen by mask or nasal prongs (score 5). Scores 6–9 categorize hospitalized patients with severe disease: oxygen by high flow or non-invasive ventilation (score 6), intubation and mechanical ventilation with partial pressure oxygen (pO₂)/ fraction of inspired oxygen (FiO₂) ≥ 150 or oxygen saturation (SpO₂)/ FiO₂ ≥ 200 (score 7), intubation and mechanical ventilation with pO₂/FiO₂ mm Hg < 150 or SpO₂/FiO₂ mm Hg < 200 (score 8), or extracorporeal membrane oxygenation (ECMO) (score 9) ⁵⁵.

Thromboelastography (TEG®) of Whole blood (WB)

TEG® is a viscoelastic technique that allows for the quantitative measurement of the efficiency of blood coagulation. Table 2 summarizes the various parameters measured in the present study using this method.

WB TEG® was performed to assess the clot kinetics and viscoelastic properties of naïve WB samples from healthy individuals (n = 10), individuals diagnosed with β/Δ (n = 10), and individuals diagnosed with Omicron (n = 10). Sample preparation required the addition of 20µL 0.2M calcium chloride (CaCl₂) (7003, Haemonetics®, Niles, IL, USA) to a disposable TEG® cup (HAEM 6211, Haemonetics®, Niles, IL, USA), followed by the addition of 340µL citrated WB. CaCl₂ is responsible for the reversal of the anticoagulant action of sodium citrate and, consequently, activation of the coagulation cascade ⁴⁶. Samples were loaded into the measuring channels of a TEG® 5000 Hemostasis Analyzer System (07–033, Haemonetics®, Boston, MA, USA) and allowed to run until the maximum amplitude (MA) was reached. All analyses were performed at 37°C.

Quantitative assessment of anomalous platelet and fibrin amyloid clotting by fluorescent microscopy of platelet poor plasma (PPP)

In order to detect microclot presence in PPP, Thioflavin T was added to PPP of healthy individuals (n = 10), β/Δ (n = 10), and Omicron participants (n = 10). This method was previously described ^47,57. Thioflavin T (ThT) is a fluorescent probe frequently used to detect amyloid fibrils at roughly 482nm with an excitation of 450nm ^58,59. The ThT molecule comprises a pair of benzothiazole and benzaminic rings that freely rotates around a shared C-C bond and if the rotation is disturbed, the molecule will exhibit strong fluorescence (at 482nm) which appears green to the observer ⁶⁰. The PPP was thawed from − 80°C to room temperature, whereafter the samples were exposed to ThT (Sigma-Aldrich, St. Louis, MO, USA), at a final exposure concentration of 5 µM and for a period of 30 minutes. Following incubation, 3µL PPP of each exposed sample was placed on a glass slide and covered with a coverslip. A Zeiss Axio Observer 7 inverted fluorescence microscope equipped with a Colibri 7 LED light source, and a Plan-Apochromat 63x/1.4 Oil DIC M27 objective (Carl Zeiss Microscopy, Munich, Germany) was used to view the prepared samples. The excitation wavelength for ThT was set at 450nm to 488nm and the emission at 499nm to 529nm.

Microclot images were assessed by calculating the area of fluorescent microclots (identified by ThT). A total of five micrographs per sample were subjected to two separate thresholding scripts/algorithms. A Fiji® (Java 1.8.0_172) and a Python® (Python 3.9.5) script were the quantitative methods used. The Fiji® methodology was adapted from as described in ⁶¹. A second Python® image processing script was developed and applied to the same five micrographs per sample to calculate the area of fluorescent microclots (identified by ThT). The Fiji® script was set to analyse particles of a size of 0.5µm – 200,000µm (to represent infinity). A minimum of 0.5µm was set to include smaller particles but also account for background fluorescence. The Python® script converts the images to grayscale and performs simple binary thresholding to eliminate low-level background fluorescence. A threshold value of 30 (pixel value) is used for this step. For a grayscale images, the pixel value is a single number that represents the brightness of the pixel. The most common pixel format is the byte image, where this number is stored as an 8-bit integer giving a range of possible values from 0 to 255. Typically, zero is taken to be black, and 255 is taken to be white. After grayscaling the images, Otsu’s thresholding method is used to binarize the image and separate the image into foreground and background pixels, with the foreground pixels being the fluorescent clot areas. The individual clot areas are then identified, labelled, and characterised using the skimage measure library (https://scikit-image.org/). The now labelled clot areas are used to calculate the total fluorescent area. The average fluorescent area measurement calculated by both the Fiji® and Python® scripts are converted to percentages through considering the total area per image. This allowed for direct comparison of the two methodologies (scripts are available on request).

Statistical analysis

GraphPad Prism 9 (version 9.3.1, San Diego, CA) was used to determine statistical differences of quantitative parameters. An unpaired student’s t-test was performed for parametric data (as determined by the Shapiro–Wilk normality test), while the Mann–Whitney U test was performed for nonparametric data. A Kruskal-Wallis test (nonparametric distribution) was performed on the age parameter to determine the statistical difference between the three representative groups. Parametric data were expressed as mean ± standard deviation, and nonparametric data were expressed as median and [Q1 – Q3 interquartile range].

Demographic features and clinical characteristics of all participants are presented in Table 3 alongside WHO Clinical Progression scores for Omicron and β/Δ patients. Based on this prognostic scale, all 10 Omicron patients had a WHO score of 2. Likewise, using the same algorithm, the 10 β/Δ (which included in - and outpatients) had a mean WHO score of 4.9. Four of the β/Δ patients were outpatients, of which three had WHO scores of 2 and one patient who required assistance had a WHO score of 3. Two patients were on nasal cannula oxygen for WHO scores of 5, and one patient required high-flow nasal cannula for a WHO score of 6. Three β/Δ patients received mechanical ventilation, (pO₂/FiO₂ mm Hg < 150 or SpO₂/FiO₂ mm Hg < 200) for WHO scores of 8. This prognostic tool indicated that the disease severity of β/Δ patients were significantly higher than that of Omicron patients.

Table 3

Participant demographics and COVID-19 positive WHO clinical progression scores. Statistical significance was established at p < 0.05 (* = p < 0, 05; ** = p < 0,01; *** = p < 0,001; **** = p < 0,0001). Parametric data are expressed as mean ± standard deviation, and nonparametric data are expressed as median and [Q1 – Q3 interquartile range].
Demographics	Healthy Participants (n = 10)		Omicron (n = 10)		β/Δ (n = 10)		P-value
Age	49.5 [29.8–53.0]		32.5 [27.0–41.0]		57.0 [52.5–62.5]		0.003 ()**
	Frequency	Percentage	Frequency	Percentage	Frequency	Percentage
Gender Female Male	6 4	60 40	7 3	70 30	7 3	70 30
Comorbidities
Obesity					3	30
Dyslipidaemia					4	40
Hypertension			1	10	4	40
Type II Diabetes Miletus					3	30
Ischemic Heart disease					1	10
Familial Hypercholesterolaemia			1	10
Anxiety/Depression			3	30
Hypothyroidism			1	10
Previous cancer diagnosis			1	10	1	10
WHO Clinical Progression Score			2 (± 0)		4.9 (± 2.56)		0.002 ()**

Thromboelastography (TEG^®) of Whole blood (WB)

TEG® was performed on WB samples to assess coagulation sufficiency of our three groups. Four WB clot parameters were assessed by TEG® in this study: reaction time (R), kinetics (K), alpha angle (A) and maximum amplitude (MA) (see Table 3). Significant differences in all four TEG® parameters were found when comparing data from healthy individuals and individuals diagnosed with β/Δ COVID-19, with all parameters of β/Δ indicating to a hypercoagulable state. Overall significances were also established between healthy individuals and individuals with Omicron. The MA of the β/Δ group was significantly higher than the Omicron group. Despite the fact that significance was not established in three of the parameters when directly comparing Omicron and β/Δ, majority of the p values in the β/Δ to healthy individuals comparison were lower than the p values in the Omicron to healthy individuals comparison. The distribution of WB TEG® parameters between all groups are shown in Table 4.

Table 4

Results of four viscoelastic TEG® parameters assessing coagulability of WB samples from healthy individuals, β/Δ COVID-19, and individuals diagnosed with Omicron COVID-19, respectively. Statistical significance was established at p < 0.05 (* = p < 0, 05; ** = p < 0,01; *** = p < 0,001; **** = p < 0,0001). Parametric data are represented as the mean ± standard deviation and nonparametric data as the median [Q1 – Q3 interquartile range].
Healthy samples vs β/Δ samples
Parameter	Control (n = 10)	β/Δ (n = 10)	P-value
R (min)	10.5 (± 4.08)	5.7 (± 1.8)	0.003 ()**
K (min)	3.38 (± 2.31)	1.63 (± 0.48)	*0.03 ()**
A (°)	55.72 (± 13.26)	67.56 (± 5.88)	*0.02()**
MA (mm)	52.3 [42.45–58.75]	67.75 [61.68–73.93]	0.007()**
Healthy samples vs Omicron samples
Parameter	Control (n = 10)	Omicron (n = 10)	P value
R (min)	8.65 [6.78–14.4]	4.6 [4.1–5.2]	< 0.0001()
K (min)	3.15 [1.65–4.23]	1.4 [1.28–1.85]	*0.04 ()**
A (°)	54.55 [43.95– 66.43]	69.85 [65–71.43]	*0.01 ()**
MA (mm)	52.12 (± 11.4)	60.77 (± 3.96)	*0.04 ()**
β/Δ samples vs Omicron samples
Parameter	β/Δ (n = 10)	Omicron (n = 10)	P value
R (min)	6.1 [4.63–7.1]	4.6 [4.1–5.2]	0.11
K (min)	1.55 [1.2–1.95]	1.4 [1.23–1.85]	0.87
A (°)	68.45 [62.05–72.9]	69.85 [65–71.43]	0.81
MA (mm)	67.75 [61.68–73.93]	61.45 [57.83–63.9]	*0.05 ()**

Fluorescence microscopy to detect aberrant fibrin amyloids in platelet poor plasma (PPP) stained with Thioflavin T (ThT)

Plasma from both β/Δ COVID-19 and Omicron COVID-19 samples demonstrated a significantly higher percentage area of amyloid when compared to control samples. In addition, here we could also differentiate between the microclotting seen in acute Omicron COVID-19 samples and acute β/Δ COVID-19 samples. Following the % microclot area analysis using Fiji®, we subjected the same set of micrographs to a similar image processing script we developed using Python®. The results of the analysis were in agreement between the two scripts. Table 5 shows the statistical analysis of the area of fluorescent particle results obtained from the two different scripts. Figure 1 shows a cartoon on how the PPP samples were prepared for the detection of microclots and representative examples of microclots in PPP. We observed less extensive microclot formation in Omicron PPP compared to the more virulent β/Δ variants.

Table 5

Percentage average amyloid area in platelet poor plasma (PPP) of healthy individuals versus participants with β/Δ COVID-19 and those participants with Omicron COVID-19 using different image thresholding algorithms (Fiji^® and Python^® script). Statistical significance was established at p < 0.05 (* = p < 0, 05; ** = p < 0,01; *** = p < 0,001; **** = p < 0,0001). Parametric data are represented as the mean ± standard deviation and nonparametric data as the median [Q1 – Q3 interquartile range].
Healthy samples (n = 10) vs β/Δ samples (n = 10)
Representative values	Fiji^® script	Python^® script
P value	< 0.0001 ()	< 0.0001 ()
Median of healthy samples	0.29% [0.19% − 0.4%]	0.25% [0.18% – 0.43%]
Median of β/Δ samples	3.85% [1.09% − 6.08%]	3.15% [1.08% − 5%]
Healthy samples (n = 10) vs Omicron samples (n = 10)
P value	0.005 ()**	0.009 ()**
Mean of healthy samples	0.32% (± 0.16%)
Mean of Omicron samples	1.02% (± 0.67%)
Median of healthy samples		0.25% [0.18% – 0.43%]
Mean of Omicron samples		0.6% [0.38% – 1.4%]
β/Δ samples (n = 10) vs Omicron samples (n = 10)
P value	0.007 ()**	0.002 ()**
Median of β/Δ samples	3.85% [1.09% − 6.08%]	3.15% [1.08% − 5%]
Median of Omicron samples	0.93% [0.38% − 1.68%]	0.6% [0.38% − 1.4%]

Mutations to the SARS-CoV-2 viral genome, resulting in the rise of new variants, have been associated with increased risk of hospitalization, ICU admission, morbidity, and mortality ^62,63. The Beta and Delta variants posed a greater risk in terms of the above mentioned factors when compared to the less virulent Alpha and Gamma variants ⁶³. Interestingly, estimates of the severity of the newest Omicron VOCs propose a lower risk of serious infection (per person infected) requiring hospitalization when compared to the previous dominant variant, Delta ^64,65. The impact of SARS-CoV-2 genome mutations on COVID-19 associated coagulopathy is not well documented. In this study, we compared WB TEG® blood clotting parameters, and prevalence of microclots of healthy individuals to COVID-19 participants that have been infected by different SARS-CoV-2 variants.

A spectrum of hypercoagulability is predicted to present among different COVID-19 variants. The early Alpha variants, and successive Beta and Delta variants, caused more hypercoagulability as documented by the incidence of venous thromboembolism, and reflected by hypercoagulopathic parameters of TEG®s including fibrinolytic shutdown ^28,66 than what is presumed to be seen in subsequent Omicron variants. In our patient population, the majority of the β/Δ variants were sicker and included recruitment from the inpatient population, whereas all the Omicron variants were less ill and were recruited only from the outpatient population. Using the WHO Clinical Progression Scale, we determined that our Omicron population experienced significantly less severe disease states compared to our β/Δ population. This study did not calculate APACHE II & IV, or SOFA scores for the COVID-19 positive population, yet it is clear that these scores were much lower for the non-hospitalized Omicron patients. Thus, there has been significant literature published regarding the validity of these prediction tools as well as precision-based proteomics algorithms which rely on machine-learning ^{55,67− 70}. In this study, we demonstrated TEG® results and their ability to distinguish differing degrees of hypercoagulability among the variants. A large proportion of critically ill COVID-19 patients will present with hypercoagulable TEG® profiles ^45,71,72. Omicron patients are met with reduced odds of developing severe disease ⁷³, leading to the assumption of less hypercoagulable TEG® profiles. The results presented here do indeed indicate lesser hypercoagulability in our Omicron population when compared to healthy individuals versus our β/Δ population when compared to the same set of healthy individuals. A direct comparison between the Omicron population and β/Δ population indicated a significantly higher maximum amplitude in the β/Δ population.

Fibrinolytic abnormalities can occur in COVID-19 with fibrin deposits previously seen in the lungs ⁷⁴ and hearts ⁷⁵ of positive patients. The present study found a significant amount of fibrin amyloid microclots in the PPP of β/Δ samples and to a lesser extent Omicron samples. Microclots have previously been proven to be highly resistant to fibrinolysis and it was also shown that inflammatory molecules and plasmin inhibitors can become entrapped in them ³². Similar to this, Wygrecka et. al. found that abnormal fibrin structure and dysregulated fibrinolysis collectively contribute to a high incidence of thrombotic events in COVID-19 ⁷⁶. Abnormal fibrinogen levels are a prominent factor associated with COVID-19 induced coagulopathy ^77− 79. This study did not measure fibrinogen levels; however, previous studies show that elevated fibrinogen correlates with excessive inflammation, disease severity and ICU admission in COVID-19 patients ⁸⁰. Our results show statically significant tapering in amount of microclots from β/Δ to Omicron to healthy individuals. The TEG® parameters did indicate a spectrum of hypercoagulability among the different COVID-19 variants, however to a lesser extent than the microclot results since direct comparison of the Omicron population to β/Δ population indicated significance.

We suggest that TEG/ROTEM and plasma microclot analysis, as personalized point-of-care medicine tools, may fill the gaps in these evidence-based recommendations for safely and effectively titrating thromboprophylaxis or anticoagulation. Although we did not directly show a lower risk of clinically significant macro-thrombosis for Omicron variants, this study is the first of its kind to directly study differing degrees of hypercoagulability among the less virulent Omicron and more virulent β/Δ variants. The severity of COVID-19 associated coagulopathy is largely due to severity of illness and correlates well with disposition; thrombotic and hemorrhagic events correlate positively to intensive-level care ⁸¹. Since Omicron less frequently causes severe illness and hospitalization, it was thought also to confer lesser hypercoagulability along the spectrum of COVID-associated coagulopathy. This study supports that hypothesis. Beyond the hospitalized COVID-19 patient, these results also have implications for the surgical patient undergoing elective surgery while afflicted with acute or convalescent COVID-19 ⁸². The surgeon may use TEG/ROTEM and plasma microclot analysis to contextualize the patient’s coagulopathy and thrombohemorrhagic risk in the perioperative period. TEG/ROTEM has established operative use in cardiac surgery, liver transplantation, and trauma resuscitation ^83,84. We propose that acute or convalescent COVID-19 should be a relative indication for adjunctive TEG/ROTEM use for the perioperative patient undergoing an emergent or elective procedure.

In the present study we did not focus on platelet activity. The role of platelets in COVID-19 associated coagulopathy is complex. Platelet hyperactivation fuels the thrombo-inflammatory milieu associated with disease severity in moderate to severe COVD-19 ^85–89. Similarly, SARS-CoV-2 can directly bind to platelet angiotensin-converting enzyme 2 (ACE2) via its spike glycoprotein to enhance thrombotic activity ⁹⁰. Thrombocytopenia is another important platelet associated complication of COVID-19 ^91–93. The pathophysiology of thrombocytopenia in COVID-19 is not fully understood, but has been proposed to involve several mechanisms ⁹⁴. As the mechanism may differ, the strategies to remedy this thrombocytopenia might be different, rendering the need for serious caution when approaching treatment ⁹⁵. Traditional platelet functional assays are reliable, but show very limited potential in clarifying platelet phenotypic heterogeneity and interactions ⁹⁶. Considering this in combination with the lack of pre-existing knowledge distinguishing the impact of differing SARS-CoV-2 variants on coagulation, and the multifaceted role platelets may play in COVID-19, highlights the need for alertness when approaching research in this uncharted territory. Advancements in techniques such as flow cytometry, electron microscopy, mass spectrometry, and ‘omics’ have started to open up new avenues in platelet research ⁹⁶, paving the way for follow up studies to expand and add to the personalized-based medicine tools presented here.

Omicron variants present with less severe symptoms and a lower likelihood of hospitalization when compared to earlier variants such as Beta and Delta. Less is known about the impact of SARS-CoV-2 genome mutations on COVID-19 associated coagulopathy. The results presented here show differing degrees of hypercoagulability among SARS-CoV-2 variants determined by a combined approach of TEG® and fluorescent PPP microclot analysis. This study does not of itself infer a direct lower risk of clinically significant macro-thrombosis for Omicron variants, but it is the first of its kind to focus on studying and indicating differing levels of hypercoagulability among the less virulent Omicron and more virulent Beta and Delta variants. The use of TEG/ROTEM and plasma microclot analysis are suggested as personalized-based medicine tools, that may have potential in successfully facilitating safe and effective titrating thromboprophylaxis or anticoagulation. Future research should focus on establishing how SARS-CoV-2 infection from newer variants influence platelet behavior in the diseased state, compared to older variants. Additional development of easy-to-use screening tools should continue with an overlapping clinical and translational approach. Access to a variety of relevant screening tools will assist clinicians in choosing optimal treatment during COVID-19 associated coagulopathy.

FUNDING

DBK: Novo Nordisk Foundation for support (grant NNF20CC0035580). MJK and TGM: Research reported this article was supported by the South African Medical Research Council with funds received from the Department of Science and Innovation (Project Code 96825). EP: Laboratory research supported by NRF of South Africa (grant number 142142) and SA MRC (self-initiated research (SIR) grant). 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.

ETHICS STATEMENT

Ethical approval for blood collection and microclot analysis of blood samples from participants with COVID-19 and healthy individuals was given by the Health Research Ethics Committee (HREC) of Stellenbosch University (reference N19/03/043, project ID 9521; renewal 2021 and 2022). This laboratory study was carried out in strict adherence to the International Declaration of Helsinki, South African Guidelines for Good Clinical Practice, and the South African Medical Research Council (SAMRC) Ethical Guidelines for research. Consent was obtained from all participants. A positive COVID-19 test was confirmed before blood collection. Genomic sequencing to confirm SARS-CoV-2 is covered under Health Research Ethics Committee (HREC) of Stellenbosch University (reference #N20/04/008_COVID-19) as part of the national genomics surveillance program.

CONFLICT OF INTEREST STATEMENT

MJK is a non-executive director and shareholder of Gknowmix (Pty) Ltd. EP is the managing director of BioCODE Technologies. E.E.M., H.B.M., M.D.N. have received research grants from Haemonetics Corporation outside the submitted work. M.D.N. has received an honorarium from Haemonetics Corporation for speaking engagements, as well as research support from Janssen Pharmaceuticals (Beerse, Belgium) and Noveome Biotherapeutics (Pittsburgh, PA, USA) outside the submitted work. He has served as a consultant to Janssen and CSL Behring (King of Prussia, PA, USA) and serves on the Scientific Advisory Board of Haima Therapeutics (Cleveland, OH, USA). M.M.W. has received honoraria from Alexion Pharmaceuticals (Boston, MA, USA).

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Relative hypercoagulopathy of the SARS-CoV-2 Beta and Delta variants when compared to the less severe Omicron variants is related to TEG parameters, the extent of fibrin amyloid microclots, and the severity of clinical illness.

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Introduction

Materials And Methods

Blood sample preparation

Participant demographics (see Table 2)

Healthy participants

Participants with COVID-19: variants pre-October 2021

Participants with COVID-19: Omicron infection (January 2022- onwards)

WHO Clinical Progression Scale assessment of COVD-19 patients

Thromboelastography (TEG®) of Whole blood (WB)

Statistical analysis

Results

Thromboelastography (TEG^®) of Whole blood (WB)

Discussion

Conclusion

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