THROMBOSIS ASSOCIATED BIOMARKERS IN MORBID OBESITY AND BARIATRIC SURGERY

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES MEDICAL ACADEMY

Kristina Kupčinskienė

THROMBOSIS ASSOCIATED

BIOMARKERS IN MORBID OBESITY

AND BARIATRIC SURGERY

Doctoral Dissertation Biomedical Sciences,

Medicine (06B)

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Dissertation has been prepared at the Department of Anesthesiology of Medical Academy of Lithuanian University of Health Sciences during the period of the 2014–2018 year.

Scientific Supervisor

Prof. Dr. Andrius Macas (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B).

Consultant

Prof. Dr. Almantas Maleckas (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B).

Dissertation is defended at the Medical Research Council of the Medical Academy of Lithuanian University of Health Sciences:

Chairperson

Prof. Dr. Brigita Šitkauskienė (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B).

Members:

Prof. Dr. Habil. Virgilijus Ulozas (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B);

Assoc. Prof. Dr. Tomas Jovaiša (Lithuanian University of Health Sciences, Biomedical Sciences, Medicine – 06B);

Prof. Dr. Jūratė Šipylaitė (Vilnius University, Biomedical Sciences, Medicine – 06B);

Prof. Dr. Osvaldas Pranevičius (Weil Cornell University Medical College (USA), Biomedical Sciences, Medicine – 06B).

Dissertation will be defended at the open session of the Medical Research Council of Lithuanian University of Health Sciences on the 12th_of Decem-ber, 2018 at 2 p.m. in the Great Auditorium at the Hospital of Lithuanian University of Health Sciences Kauno klinikos.

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LIETUVOS SVEIKATOS MOKSLŲ UNIVERSITETAS MEDICINOS AKADEMIJA

Kristina Kupčinskienė

POLINKIO TROMBOZEI BIOLOGINIAI

ŽYMENYS SERGANT DIDELIO LAIPSNIO

NUTUKIMU IR PO SVORIO MAŽINIMO

OPERACIJŲ

Daktaro disertacija Biomedicinos mokslai,

medicina (06B)

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Disertacija rengta 2014–2018 metais Lietuvos sveikatos mokslų universitete Medicinos akademijos Anesteziologijos klinikoje.

Mokslinis vadovas

prof. dr. Andrius Macas (Lietuvos sveikatos mokslų universitetas, biomedicinos mokslai, medicina – 06B).

Konsultantas

prof. dr. Almantas Maleckas (Lietuvos sveikatos mokslų universitetas, biomedicinos mokslai, medicina – 06B).

Disertacija ginama Lietuvos sveikatos mokslų universiteto Medicinos akademijos medicinos mokslo krypties taryboje:

Pirmininkė

prof. dr. Brigita Šitkauskienė (Lietuvos sveikatos mokslų universitetas, biomedicinos mokslai, medicina – 06B).

Nariai:

prof. habil. dr. Virgilijus Ulozas (Lietuvos sveikatos mokslų universite-tas, biomedicinos mokslai, medicina – 06B);

doc. dr. Tomas Jovaiša (Lietuvos sveikatos mokslų universitetas, biomedicinos mokslai, medicina – 06B);

prof. dr. Jūratė Šipylaitė (Vilniaus universitetas, biomedicinos mokslai, medicina – 06B);

prof. dr. Osvaldas Pranevičius (Weil Cornell universiteto Medicinos koledžas (Jungtinės Amerikos Valstijos), biomedicinos mokslai, medi-cina – 06B).

Disertacija ginama viešame Medicinos mokslo krypties tarybos posėdyje 2018 m. gruodžio 12 d. 14 val. Lietuvos sveikatos mokslų universiteto ligo-ninės Kauno klinikų Didžiojoje auditorijoje.

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ABBREVATIONS

aOR – adjusted odds ratio

APC – activated protein C

aPTT – activated partial thromboplastin time ATIII – antithrombin III

AUC – area under the curve BMI – body mass index

C – concentration

CHOL – cholesterol

CI – confidence interval CRP – C-reactive protein DVT – deep vein thrombosis

EDTA – Ethylenediaminetetraacetic acid ESR – erythrocyte sedimentation rate F5 – coagulation factor V gene FGG – fibrinogen gamma chain gene G – clot strength

gDNA – genomic DNA

GPB – gastric bypass

GWAS – genome-wide association studies HDL – high-density lipoprotein

hs-CRP – high sensitivity C-reactive protein INR – international normalized ratio k-time – clotting time

LDL – low-density lipoprotein

LMWH – low-molecular weight heparins MA – maximum amplitude

MCF – maximal clot firmness

MTHFR – methylenetetrahydrofolate reductase gene NTC – no template controls

OR – odds ratio

PC – protein C

PCR – polymerase chain reaction PE – pulmonary embolism PLT – platelet count

POD1 – postoperative day 1 POD2 – postoperative day 2

PS – protein S

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ROC – receiver operating characteristic r-time – reaction time

RT-PCR – real-time polymerase chain reaction sdLDL – small dense low-density lipoprotein SNP – single nucleotide polymorphism TEG – Thromboelastography

TF – tissue factor

(tPA) – tissue plasminogen activator TRIG – triglycerides

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1. INTRODUCTION

Obesity has become one of the major health care challenges affecting nearly 700 million individuals worldwide [1]. Furthermore, obesity in child-hood and adolescence are steadily increasing and associated with adverse health consequences throughout the life-course [2]. Severe obesity is associated multiple medical conditions, significantly affecting longevity and quality of life [3, 4]. Obesity is a well-known risk factor for developing thrombotic events [5]. To date, bariatric surgery is the most effective modality for treatment of morbid obesity. Although the risk of postoperative deep vein thrombosis (DVT) following bariatric surgery does not exceed 2% in most of the studies; however, the strategies to identify individuals who have the highest risk for DVT are not available [6]. Despite improved understanding of thrombosis pathogenesis in obesity, risk stratification strategies for prevention of thrombosis in obese individuals need to be improved [7]. Therefore, identification of different biomarkers that could be used to identify the patients with increased risk of thrombosis in the perioperative setting of surgery as well as in the long run need to be established.

Epidemiological studies show a clear link between body mass index (BMI) and the risk of DVT or related conditions [8–10]. Thrombosis and obesity are complex epidemiologically associated diseases, but the mecha-nism of this association is not yet completely understood [11]. Recent studies outlined the importance of genetic factors for development of thrombosis with more than 60% of the variation in susceptibility that might be related to genetic factors [7, 12]. Recent studies have shown that BMI and thrombosis or pulmonary embolism (PE) are genetically linked [13, 14]. Several large scale genome wide association studies (GWAS) have identified several genetic loci including ABO, F5, MTHFR and FGG gene polymorphisms that are associated with development of different thrombotic events [15–18]. To date, the frequencies of these genetic variations have not been assessed in morbidly obese patients undergoing bariatric surgery as well as in Lithuanian population.

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Furthermore, TEG allows to identify the effect of prophylactic anticoagu-lation in perioperative setting, which is still large debated in bariatric surgery. As of 2018, only few studies address the role of hypercoagulability monitoring in perioperative period of bariatric surgery [21, 22] and this field remains highly unexplored. A recent systematic review about hypercoagul-able states and thromboelastography indicated that further studies are needed to determine the ultimate role of this method for prediction of hypercoagulability and postoperative thrombosis. In addition, there is few data available on the patterns of the major coagulation cascade factors after the bariatric surgery in relation to weight loss.

Taking into account the complex pathogenesis of thrombosis in morbid obesity described above, we performed the present study that was composed of two parts. In the first part of the study we looked at genetic risk factors that may contribute to the development of morbid obesity. In the second part of the study we examined coagulation, inflammatory and lipid biomarkers in the short and long term follow up after bariatric surgery.

1.1. The aim of the study

To investigate the association of thrombosis related genetic variations with morbid obesity and assess coagulation, inflammatory and lipid biomar-kers in patients undergoing bariatric surgery.

1.2. The objectives of the study

1. To determine the potential association of thrombosis related ABO, F5, MTHFR, and FGG gene single nucleotide polymorphisms and morbid obesity.

2. To determine preoperative values of clot firmness (maximum amplitude and clot strength) and to assess dynamics of coagulation parameters using thromboelastography in the perioperative setting and long term follow up after bariatric surgery.

3. To determine initial values and dynamics of the following biomarkers in the long term follow up after bariatric surgery:

• Coagulation biomarkers (protein C, protein S, coagulation factor VII and antithrombin III);

•_{Inflammatory biomarkers (C-reactive protein, high sensitivity} C-reac-tive protein);

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1.3. Novelty of the study

In the last years our knowledge about risk factors associated with thrombotic events in obesity and bariatric surgery has expanded rapidly [23]. It has been shown that thrombogenic effects in overweight patients undergoing bariatric surgery are mediated by multiple factors, including comorbiddity, smoking, physical activity, concomitant medication and other [23, 24]. Nevertheless, biomarkers that could help to stratify patients with respect to thrombotic risks in obesity and patients undergoing bariatric surgery are highly lacking.

In first part of our study we tried to identify thrombosis associated genetic variations that might be linked with morbid obesity in patients undergoing bariatric surgery. Different research groups over the last decade have attempted to identify crucial co-factors that contribute to the development of thrombosis in obesity [24]. Growing number of studies show that apart from the main underlying causative agents in thrombosis, the process may be reinforced by confounding factors such as smoking, physical activity etc. [25]. Inter-individual variation of thrombotic events suggests the potential influence of genetic factors in this process. Advances in genotyping techniques allowed to identify coexisting genetic alterations associated with thrombosis in different clinical settings [26]. To date, seventeen gene variations have been linked with the thrombotic states [26].

The aim of the first part our present study was to determine the frequencies of thrombosis related ABO, F5, MTHFR and FGG gene polymorphisms in morbidly obese patients undergoing bariatric surgery and compare them with the group of non-obese individuals. Frequencies of ABO C>T (rs505922), F5 C>G (rs6427196), MTHFR C>T (rs1801133) and FGG C>T (rs6536024) gene polymorphisms have not been previously evaluated in patients with significantly increased BMI. Here, in this study performed ABO, F5, MTHFR and FGG SNP genotyping analysis in 320 morbidly obese patients (BMI > 40 kg/m2) and 303 control non-obese individuals (BMI < 30 kg/m2) of European descent. Determination of genetic factors that could be linked with thrombotic complications in obese individuals might provide additional insights in to the pathogenesis of these disorders. Furthermore, this is the first study that evaluated the distribution of ABO C>T (rs505922), F5 C>G (rs6427196), MTHFR C>T (rs1801133) and FGG C>T (rs6536024) genotypes in Lithuanian population.

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2. REVIEW OF LITERATURE

2.1. Obesity related morbidity and thrombosis

Obesity related morbidity and mortality are among the major health care problems [29]. Accumulating evidence clearly shows that obesity is linked with a variety of poor health outcomes. Obesity in adults has been shown to be associated with a significant reduction in life expectancy for both genders. The association between BMI and cause specific mortality was assessed in the Prospective Studies Collaboration analysis. The study found that, in individuals with a BMI between 25 and 50 kg/m2_{, every 5} kg/m2_{increase in BMI was associated with an increased mortality from} diabetes mellitus, chronic kidney disease, ischemic heart disease, stroke, respiratory disease, and neoplasms [30].

Many disease entities occur more frequently in the obese and many of them might be even clinically unsuspected or undiagnosed. Gabriel et al. retrospectively reviewed the autopsies of 311 patients, 125 of whom were obese. The authors found that this population is 1.65 times more likely to have major clinically significant unsuspected diagnoses on postmortem examination compared with underweight and normal weight populations [31]. Calle et al. followed more than 900,000 adults in the United States for 16 years and found that obese men and women were more likely to die from cancer than subjects of normal weight [32]. The cancers that occurred with a higher frequency in the obese population included cancers of the esophagus, liver, colorectal, pancreas, gallbladder, kidney, non-Hodgkin lymphoma, and multiple myeloma [32].

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weight women, with class III obesity, in particular [36]. Furthermore, PE was the most frequently missed clinically significant diagnosis in obese individuals based on autopsy findings [31].

2.2. Genetic predisposition for thrombosis

As mentioned above thrombosis is a common cause of morbidity and mortality in developed countries [9, 37]. Both venous and arterial forms of thrombosis are of great importance for public-health [38]. The widely perceived causes of thrombosis include both environmental and host factors [39, 40]. High prevalence of thrombosis and widely investigated environ-mental influences, such as smoking and oral contraceptive use, suggest that multiple genes with varying effects might be involved in determining susceptibility to thrombosis [41]. Gene to gene as well gene-environment interactions may trigger different prothrombotic pathways [42]. As of 2018, accumulating evidence suggests underlying genetic predisposition for thrombotic events, but this field needs further research efforts [26].

The physiological cascade that underlies the normal formation of thrombin and the pathological endpoint of thrombosis is complex, with many components involved in the coagulation and fibrinolytic pathways. The identification of objective risk factors for thrombosis has gained signifi-cant velocity in the recent years. Numerous hemostatic factors – including fibrinogen, factor VII, factor VIII, von Willebrand factor, homocysteine and many others have been implicated as possible risk factors of both venous [43–45] and arterial thrombosis [45–47].

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Most significant advances in understanding genetic predisposition for DVT come from genome-wide association (GWAS) studies. New variants and candidate genes have been identified using this next-generation sequen-cing based approach [12, 18]. Advances of these modern genetic sequences techniques has allowed to identify specific genetic variants that increase the risk of thrombotic events. The large case-control studies employing next generation sequencing techniques have found different genetic loci that may influence the development of both venous and arterial thrombotic events. To date, at least seventeen genes have been associated with VT risk including ABO, F2, F5, F9, F11, FGG, GP6, KNG1, PROC, PROCR, PROS1, SERPINC1, SLC44A2, STXBP5, THBD, TSPAN15 and VWF [26, 41, 57].

2.2.1. ABO gene and thrombosis

ABO gene is located on chromosome 9q34.2 and its inheritance is explained by the classical Mendel theory. The association between ABO blood groups and diseases resulting in coagulation disorders and venous thrombosis was first described by Jick et al. back in 1969 [58]. A number of studies have uncovered that the ABO genetic variations have a profound influence on haemostasis, as it is a major determinant of plasma von Wille-brand factor (vWF) levels [59]. Levels of vWF, mostly genetically determi-ned, are strongly associated with VTE. It mediates platelet aggregation and stabilizes FVIII in plasma. In a healthy state, family and twin studies showed 75% of the variation in plasma vWF/factor VIII levels result from genetic determinants [60], 30% of which are associated with ABO blood type [61]. Notably, vWF levels are approximately 25% higher in individuals who have a blood group other than O [62].

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F5 is a plasma glycoprotein coded by a gene located in chromosome region 1q24.2 [18]. F5 is an important component within the coagulation system, that does not possess enzymatic active but rather functions as a co-factor [11]. In its activated form F5a participates in the coagulation cascade as an essential co-factor to factor Xa in thrombin genesis. F5 is also a proteolytic target for activated protein C (APC), which expresses its anti-coagulant function by deactivating it. The deactivated F5 and protein S act as co-factors of APC, deactivating the factor VIII (FVIII) molecule [66].

Deficiency of F5 is associated with increased risk of bleeding, while several genetic mutations of this gene predispose thrombosis events [67]. Genetic variations in F5 region have been clearly linked with the risk of clot formation. Leiden mutation (rs6025) within the F5 gene is the most well-known and already serves as a biomarker in clinical practice when investi-gating otherwise unexplained severe thrombotic events like Budd-Chiari syndrome and other [57]. In order to identify additional novel genetic deter-minants of VTE, Tang et al. (2013) conducted a large GWAS study among individuals of European ancestry [18]. This study identified two loci at the F5 region with an intronic variant rs2420370 and a coding variant rs6427196 that are associated with the risk of thrombosis.

2.2.3. MTHFR gene and thrombosis

MTHFR gene encodes methylenetetrahydrofolate reductase which cata-lyses the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahyd-rofolate [17]. It is a co-substrate for homocysteine remethylation to methio-nine by methiomethio-nine synthetase [17]. Homocysteine is a non-protein-forming sulfur amino acid produced during the catabolism of methionine. Its concentration is tightly regulated and kept at low levels through either remethylation or transsulfuration [17]. Plasma homocysteine levels are influenced by genetic as well as environmental factors (age, sex, smoking status, intake of folate and intake of B vitamins). When the plasma homo-cysteine levels increase, oxidative stress is enhanced in parallel, leading to inflammation of the vascular cells and thrombosis due to endothelial cell dysfunction [68].

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The MTHFR rs1801133 polymorphism has a homozygous wild-type genotype (CC), a heterozygous genotype (CT), and a homozygous mutant genotype (TT). Several studies have reported that the TT genotype is asso-ciated with a marked increase in plasma homocysteine concentrations [11, 12, 37] and homozygous mutated subjects for rs1801133 have a higher risk for various thrombotic diseases [73]. However, little is known about the effects of the association between MTHFR gene mutations and hyperhomocysteinemia on thrombosis, and, especially, its role in the prevention and treatment of thrombosis is not yet established [73]. Overall, MTHFR genetic variations appears an important genetic risk locus for thrombotic events that need to be assessed in further studies [23].

2.2.4. FGG gene and thrombosis

Fibrinogen gamma chain, also known as FGG gene, is a human gene located in chromosome 4 at position 32.1 and belongs to fibrinolysis cascade [57, 74]. The FGG gene provides substrate for the formation of the fibrinogen gamma (γ) chain, which is one of the sub-units of the fibrinogen protein. To form fibrinogen, the γ chain attaches to the fibrinogen A alpha and fibrinogen B beta chains, each produced from different genes. Two sets of this three-protein complexes form the final product that is one of the most important actors in clot formation - functional fibrinogen [75].

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2.3. Assesment of viscoelastic clot properties 2.3.1. Principles of thromboelastography

Thromboelastography (TEG) – provides assessment of whole blood viscoelastic properties from the beginning of the first fibrin strands formation to the end with fibrinolysis [78]. This method assess the onset of blood clot formation, speed of clotting (kinetics of clot growth), clot quality such as strength and stability and breakdown [78]. The concept of thrombo-elastography had been invented and described by Helmut Harter in Heidel-berg, Germany back in 1948 [79]. TEG in clinical practice has been used for decades to guide blood transfusion during liver transplant, hepatic and cardiac surgeries. Improved technology, wider availability and increasing number of studies have led to extended indications and use of TEG in differ-rent field of hemostasis [80].

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Fig. 2.3.1.1. Characteristic thromboelastographic (TEG) patterns.

Adapted from [89].

A – Normal TEG pattern. B – anticoagulation caused by coagulation factor deficiency or inhibition leading to a prolonged R-time. C – Platelet dysfunction or pharmacological inhi-bition leading to decreased maximum amplitude (MA). D – Hyperfibrinolysis. E – Hyper-coagulability leading to shortened R and K times.

The major TEG parameters are listed below [89]:

• “r” (reaction time, in minutes) – it is time from the beginning of

the trace until a first pin oscillation of 2 mm is reached. The period of latency time is taken from the placement of the blood sample in the cuvette to the initial fibrin formation. Represents plasma coagulation factors and circulating inhibitory activity.

• “K” (clotting time, in minutes) – it is the time from the end of “r”

until a fixed level of clot firmness, i.e. 20 mm of pin oscillation is reached. “K” is associated with the activity of the intrinsic clotting factors, fibrinogen and platelets. This value reflects thrombin‘s ability to cleave soluble fibrinogen.

• “α angle” (degrees) – rate of clot polymerisation or clot growth,

closely related to “K” and measure the slope between “r” and “K”. It is affected primarily by the rate of thrombin generation. “K” and „α angle“ correspond mainly with fibrinogen concentration.

• “MA” (maximum amplitude, millimeters) – it is a measurement of

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• “SEMS” or “G” (shear elastic modules strength, dynes per square

centimeter) – a parametric measure of clot firmness expressed in metric units calculated from “MA”.

• “LY30” (fibrinolysis at 30 minutes) – rate of amplitude reduction

30 minutes after “MA”. Measures the rate of fibrinolysis.

The principle of TEG action mode is presented and explained in Fig. 2.3.1.2.

Fig. 2.3.1.2. Schematical representation of TEG workflow.

Adapted from [87, 88, 90–92].

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Normal ranges of major TEG parameters according to producers recommendations are the following: “r” – 3 (5–10) min, “K” – 1–3 min, “α angle” – 53–72 degrees, “MA” – 50–70 mm, “G” – 4.6–10.9 d/sc, “LY30” – 0–8% [89].

2.3.2. Thromboelastography in bariatric surgery

Obesity is an established risk factor for deep vein thrombosis and pulmonary embolism. These complications are major causes of morbidity and mortality of bariatric patients. Prevalence of obesity continues to rise annually and bariatric surgery became a gold standard for treatment of morbidly obese patients. To date, the incidence of hypercoagulability in patients with extreme overweight still remain poorly investigated. Only a few studies used thromboelastography method for evaluation of blood coagulation alterations during perioperative period in bariatric surgery [27, 28, 93]. Furthermore, there are no studies that have looked at TEG parameter changes after bariatric surgery in relation to weight loss in the long follow up period. The overall findings of most of the studies show that part of obese patients have hypercoagulable state, which was identified in 18.3% to 50% of the patients undergoing bariatric surgery [27, 94].

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2.4. Coagulation cascade

In 1960's Davie, Ratnoff and Macfarlane first described fundamental principles of blood coagulation system using “waterfall” and “cascade” concepts and published articles in Nature and Science journals [97, 98]. Coagulation is a dynamic process that leads to balanced hemostasis. Imbalance within coagulation cascade is induced by various factors including comorbidities, drugs and other internal and external factors [99]. Historical classification of the coagulation cascade into extrinsic and intrinsic parts has recently been challenged, but still holds true for robust understanding of coagulation processes within the body [76]. Normal coagulation within humans is represented by a subtle balance between the procoagulant pathway that is responsible for clot formation and the mechanisms that do not allow clot formation initiation where not needed. Two paths, intrinsic and extrinsic, originate separately but do overlap at very different molecular interaction point to form what is known as the common pathway [99]. These interactions further lead to fibrinogen active-tion and formaactive-tion of platelet and fibrin mesh. Under normal condiactive-tions coagulation cascade is bended towards protective anti thrombotic state in order to avoid unnecessary thrombotic complications [99]. The summary of major thrombotic and antithrombotic components within the coagulation system are presented in Table 2.4.1.

Table 2.4.1. Thrombogenic and antithrombogenic components in the body

Site Thrombogenic Antithrombogenic

Vessel wall Exposed endothelium Heparin

Tissue factor Thrombomodulin

Collagen Tissue plasminogen activator

Circulating elements Platelets Antithrombin Platelet activating factor Protein C and S Clotting factor Plasminogen Prothrombin

Fibrinogen vWF Adapted from [100].

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called “secondary hemostasis” occurs in parallel, and is related to the coagu-lation factors, platelet plug and damaged cells within the coagucoagu-lation pathways [101]. Thrombin removes A and B fibrinopeptides from function-nal fibrinogen subunits and converts fibrinogen into fibrin, the main protein in blood clots. Two fibrin proteins (subunits) have an affinity to each other and attach together forming a stable network that bind platelet together, stabilizing plug [74, 100]. Once bleeding has been efficiently stopped by blood clot formation, dissolution of the thrombus is essential to restore vessel permeability. This process, known as fibrinolysis [101]. The overall schematic representation of coagulation cascade is presented in Fig. 2.4.1.

Fig. 2.4.1. Major pathways within blood coagulation cascade.

Adapted from [102]).

2.4.1. Fibrinogen

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In order to form a blood clot during tissue and vascular injury, fibrinogen must first be cleaved by thrombin. Soluble fibrinogen monomer is converted to fibrin monomer as thrombin removes N-terminal fibrinopeptides A and B. Fibrin molecules can link together through the interaction of the E domain on one fibrin molecule to the D domains on four other fibrin molecules, thereby subsequently polymerizing to form insoluble multi-stranded 3-dimention gel network of entangled fibers – fibrin-based clot [103].

Fibrinogen functions primarily to occlude blood vessels and thereby stop excessive bleeding. However, fibrinogen's product, fibrin, is essential for fibrinolysis by at least two important mechanisms. First, fibrin serves three low affinity binding sites for thrombin to sequester it from attacking fibrinogen this way reducing thrombin activity. This activity, sometimes referred to as antithrombin I. Loss or reduction in this antithrombin 1 activity due to mutations in fibrinogen genes or hypo-fibrinogen conditions can lead to excessive blood clotting and thrombosis. Second, fibrin's Aα chain accelerates by at least 100-fold the mount of plasmin activated by tissue plasminogen activator; plasmin breaks-down blood clots [104–106]. Plasmin's attack on fibrin releases D-dimers. The detection of these dimers in blood is used as a clinical test for fibrinolysis [104].

The variables that affect fiber architecture are ultimately important for fibrinolysis, since both fiber size and arrangement impact tissue plasmi-nogen activator (tPA) binding and rates of fibrinolysis [107, 108]. Throm-bus formation depends upon not only the total fibrinogen concentration, but also the isoform composition of the fibrinogen pool. Clot structure, therefore, reflects the complex interplay of many factors ranging from polymorphisms in fibrinogen itself, to the efficiency of thrombin generation, the reactivity of associated cells, such as platelets, and the biochemical environment. These components define fibrin clot architecture, which is a key determinant of the efficiency of clot lysis [76, 109].

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25 2.4.2. Coagulation factor VII

Factor VII is the vitamin K depended coagulation serine protease responsible for starting a cascade of proteolytic events that lead to thrombin generation, fibrin deposition, and platelet activation [112]. FVII only becomes an efficient catalyst when associated with its protein cofactor, tissue factor (TF). TF is found on the outside of blood vessels – normally not exposed to the bloodstream. The two proteins form a complex at the site of injury, where extravascular TF becomes exposed to the blood [113]. Once bound to TF, FVII is activated to FVIIa by different proteases, among which are thrombin (factor IIa), factor Xa, IXa, XIIa, and the FVIIa-TF complex itself. The complex of factor VIIa with TF catalyzes the conversion of factor IX and factor X into the active proteases, factor IXa and factor Xa, respectively [114].

The action of the factor is impeded by tissue factor pathway inhibitor, which is released almost immediately after initiation of coagulation. Factor VII is vitamin K dependent. Use of warfarin or similar anticoagulants decreeses hepatic synthesis of FVII. A recombinant form of human factor VIIa has U.S. Food and Drug Administration approval for uncontrolled bleeding in hemophilia patients. It is sometimes also used off lable in severe uncontrolable bleeding [115].

Several studies suggest that obesity is characterized by an increased expression of several prothrombotic factor, impaired fibrinolysis and plate-let hyper-reactivity [7]. Weight loss has been found to (partially) revert both metabolic and coagulation alterations found in obese subjects. R. Lupoli et al. had documented that bariatric surgery is able to reduce the hypercoagu-lable state by reducing levels of clotting factors and fibrinolytic variables with significant reduction of FVII [116].

2.4.3. Antithrombin III

Antithrombin III (ATIII) is an important endogenous anticoagulant plasma protein responsible for inactivation of several enzymes of coagula-tion cascade, particularly thrombin and factor Xa. The biological activity of ATIII is mediated by a polysaccharide, heparin, which enhances the binding of antithrombin to FII and FX [117].

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which then degrades factor Va and VIIIa, effectively impeding further thrombin generation. The second inhibitory pathway of thrombin is provided by specific circulating enzyme inhibitors. Among these, antithrombin III is the major inhibitor, accounting for approximately 80% of the thrombin inhibitory activity in plasma [117].

Antithrombin is the major inhibitor of thrombin, factor IXa, and factor Xa in plasma, but it also inactivates the other serine proteases of the intrinsic coagulation pathway, factors XIa and XIIa, as well as some noncoagulation serine proteases, such as plasmin, kallikrein and the comple-ment enzyme C1. Most proteases are inactivated much more slowly than thrombin [117].

Antithrombin, protein C and protein S are major compounds of the phy-siological anticoagulant system and their deficiencies are known to be severe risk factors for venous thromboembolism [118]. Altogether, they even constitute about 60–70% of misdiagnosed cases of inherited thrombo-philia. Congenital antithrombin deficiency is the most clinically resulting in thrombosis in the majority of those affected. Acquired deficiencies of AT are observed in patients with cirrhosis, sepsis, disseminated intravascular coagulation, diabetes, old age, obesity and certain other conditions [119].

There is a growing number of evidence that the deficiency of antithrom-bin III found in obese patients may be reversed with weight loss. Low ATIII levels in morbidly obese patient and its concentration return to normal with weight reduction was investigated in 1983 by Batist at al. [120]. In the common pathway of the coagulation cascade, the ATIII was observed to be high in the obese group with respect to control values [110].

2.4.4. Proteins S and C

Protein S (PS) and protein C (PC) are two vitamin K-dependent plasma glycoprotein synthesized in hepatocytes. The best characterized function of PS is its role in the anti-coagulation pathway, where it functions as a cofactor to PC. Plasma protein C is activated after complex formation with thrombin on the endothelial cell receptor thrombomodulin. Activated pro-tein C, augmented by propro-tein cofactors (propro-tein S and factor V) and lipid cofactors, cleaves critical sites in the activated procoagulant factors V and VIII, thus inactivating these enzymes. PC serves a critical role in the regulation of thrombin. Activated protein C also functions in the regulation of inflammation [121].

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caused by increased consumption (disseminated intravascular coagulation, severe infection, acute VTE) or by decreased synthesis (e.g. administration of vitamin K antagonists, hepatic synthetic dysfunction). Patients deficient in proteins C or S are in the hypercoagulable state and therefore at high risk for thrombotic complications when administered to surgery [122].

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3. METHODS

3.1. Ethics

The study was approved by Kaunas Regional Ethics Committee of Biomedical Surveys (Protocol No. BE-2-10). All patients and controls gave their informed consent to take part in this study.

3.2. Genetic study 3.2.1. Study population

The group of morbidly obese subjects consisted of patients referred for elective bariatric surgery with a BMI>40 kg/m2_{. Control subjects were} healthy individuals with BMI<30 kg/m2_{, who came from previous genotyping} studies [125, 126]. Morbidly obese patients and controls were recruited during the years 2011–2015 in the Departments of Surgery and Gastroente-rology, Lithuanian University of Health Sciences (Kaunas, Lithuania). The inclusion criteria for control group were no previous history of malignancy, VTE and BMI<30 kg/m2_{. In total, 623 individuals (303 controls and 320} morbidly obese patients), were included in the genotyping study. All patients were of European ethnicity. Genotyping experiments were performed at Instute for Digestive Research at Lithuanian University of Health Scienes.

3.2.2. DNA extraction

Blood samples from obese patients and controls were collected in EDTA-containing vacutainer tubes and stored immediately at –80°C until further use. Genomic DNA from whole blood mononuclear cells was isolated using stan-dard salting-out method. DNA samples were aliquoted and further stored at −20°C until real time polymerase chain reaction (RT-PCR) analysis. Equip-ment used for DNA extraction from peripheral blood mononuclear cells included:

• Automatic pipettes “Eppendorf research” (100–1000 μl; Eppendorf AG, Germany)

• Centrifuge “Eppendorf Centrifuge 5424” (Eppendorf AG, Germany) • Water heating bath “GFL 1083” (GFL, Germany)

• Eppendorf tubes, 1.5 ml–2 ml

• Termomixer “Eppendorf Thermomixer comfort” (Eppendorf AG, Germany)

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Reagents used for DNA extraction: Ammonium chloride (Carl Roth GmbH, Germany); Potassium bicarbonate (Sigma-ALORICH Chemie GmbH, Germany); Etilendiaminum tetraacetatatum (Carl Roth GmbH, Germany); Three-hydrogen chloride (Carl Roth GmbH, Germany); Sodium chloride (Merck KGaA, Germany); Proteinase K (Fermentas, Lithuania); 96% and 70% ethanol (A.B. Stumbras, Lithuania); Sodium lauryl sulfate (AppliChem, Germany); Three – EDTA buffer “Roti stoch” (Carl Roth GmbH, Germany); NaOH (Merck KGaA, Germany); 99.9% chloroform (Scharlau Chemie S.A., Spain).

DNA extraction was performed using a protocol described previously [127]. Briefly, on day one after sample collection the blood was cooled down to 4°C and transfused to the centrifugal test-tubes. Lysis buffer was poured to the blood and incubated for 30 min. The sample was centrifuged for 15 min at 4°C with 3000 g. The upper layer was used and lysis buffer was added on the retained and centrifuged once again. Washing out procedure was repeated until upper layer to lost red color. The test-tube is covered, mixed and incubated in water bath for 18 h at 37°C. One day two obtained samples were further incubated for 1 h at 55°C. NaCl solution was added and the product was centrifuged at 3000 g for 15 min at 16°C. Chloroform was added to the sample and centrifuged once again at 3000 g for 20 min. Produced top layer was translocated to clean 50 ml centrifugal tubes and rotated until DNA strands turned visible. The tubes containing genomic DNA was dried out and then melted in TE buffer. Obtained DNA samples were further stored in –20°C until further use.

3.2.3. Evaluation of DNA concentration and purity by spectrophotometer

DNA concentration was measured using NanoDrop™ 2000 Spectro-photometers (Thermo Scientific™). DNA sample was vortexed and 2 μl are pipetted directly on spectrophotometer; DNAs concentration measurement was further documented in lab book based on machine readings.

3.2.4. Sample allocation on the 96 well plates for RT-PCR

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Table 3.2.4.1. 96 well RT-PCR plate layout used for genotyping experiments

1 2 3 4 5 6 7 8 9 10 11 12 A 0326 0688 0333 0556 0385 NTC 0562 0854 0030 0685 0334 0538 B 0322 0681 0338 0653 0182 0863 0002 0873 0064 0872 0654 0181 C 0323 0683 0008 0819 0830 0516 0345 0687 0014 0856 0827 0589 D 0321 0663 0005 0566 0831 0515 0310 0672 0009 0542 0874 0519 E 0316 0329 0028 0565 0832 0512 0328 NTC 0056 0573 0864 0564 F 0318 0332 0086 0533 0881 0510 0354 0333 0077 0586 0891 0514 G 0311 0330 0056 0690 0868 0509 0317 0340 0068 0695 0832 0543 H 0305 0331 NTC 0335 0868 0508 0309 0339 0565 0344 0875 0539 NTC – no template controls.

3.2.5. Genotyping with TaqMan® RT-PCR method

In this study genotyping was performed using TaqMan®_{method with} RT-PCR for ABO C>T (rs505922), F5 C>G (rs6427196), MTHFR C>T (rs1801133), and FGG C>T (rs6536024) SNPs. The RT-PCR is a method used for amplification of DNA [128]. The whole process of RT-PCR has six major steps: 1) initiation; 2) denaturation; 3) hybridization; 4) extension; 5) final extension; and 6) final hold. Steps 3–5 are repeated in continuous cycles (~35–45 times) until required amount of DNA is amplified.

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Table 3.2.5.1. Contents of one RT-PCR reaction

Reagents Producer Volume (μl) for

1 reaction

TaqManUniversal PCR Master Mix Thermo Fisher Scientific 3.25 Primer and TaqMan Probe dye mix Thermo Fisher Scientific 0.16

Nuclease free water Sigma Aldrich 1.59

Genomic DNA 1.00

Total 6

The PCR procedure consisted of several major steps: 1) Start (95°C; 10 min; for one cycle); 2) Denaturation (92°C, 15 s); 3) Elongation, nucleoli-tic cleavage of hybridized probes (60°C, 90 s) repeated for 45 cycles; and 4) termination at 4°C for one cycle. The PCR was performed using RT- PCR thermocycler (Applied Biosystems 7500; Fast; USA) according to manufactu-rer’s instructions. Genotyping results were evaluated using “7500 Software v2.0.5” that enabled automatic discrimination of individual alleles (Fig. 3.2.5.1).

Fig. 3.2.5.1. Allelic Discrimination Plot. Based on genotyping results

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3.3. Study of coagulation, inflammatory and lipid biomarkers 3.3.1. Study population

60 consecutive patients undergoing laparoscopic bariatric surgery (gastric bypass or gastric plication) were recruited at Department of Surgery, Lithuanian University of Health Sciences Hospital during the period from October, 2014 to June, 2015. Patients on anticoagulation therapy, having chronic kidney/liver failure, coagulation disorders, previous history of DVT, using oral contraception have been excluded from the study. The patients were followed for clinically evident thrombotic complications for a year after the study by arranging clinical appointments one month and one year after the surgery.

3.3.2. Anesthesia

All the patients underwent general endotracheal anesthesia, had postoperative pain management in accordance to the routines of the department and were given standardized thromboembolic prophylaxis with 500 ml 6% Dextran 70 (Fresenius, Poland) after induction of anesthesia and Nadroparin 0.3 ml sc (Fraxiparine; GlaxoSmithKline, Poland) starting in the morning of postoperative day 1 (POD1) until the discharge from the hospital. The need for premedication was determined individually by the anesthesiologist. Standard anesthesia monitoring, including electrocardio-gram, non-invasive blood pressure and pulse oxygen saturation (SpO2) (“S/5 Compact” Datex-Ohmeda) was applied after the patients arriving in the operating room. After an intravenous catheter insertion, the lactate ringer’s solution was started to infuse.

Patients were positioned in the recommended ramp position and preoxygenated with 100% oxygen via a face mask for 3 min. Standardized general anesthesia was induced with a bolus injection of propofol 2–3 mg/kg and fentanyl 2–2,5 μg/kg. Once the patient become unresponsive to verbal commands neuromuscular blocker was then administered to facilitate tracheal intubation. Anesthesia was maintained with sevoflurane and remifentanil titrated to clinical signs of adequate anesthesia.

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3.3.3. Thromboelastography measurements

Thromboelastography was performed using TEG®₅₀₀₀ Thromboelasto-graph®_{Hemostasis Analyzer System (Braintree, USA). Thromboelastographic} alterations were assessed at six time points:

1) at baseline prior to induction of anesthesia; 2) immediately after the end of the surgery;

3) in the morning of 1st_{postoperative day (POD1);} 4) in the morning of 2nd_{postoperative day (POD2);} 5) one month after the surgery;

6) one year after the surgery.

Blood was sampled from a clean venipuncture, and placed into citrated vacutainer tubes containing 3.9% (0.129 M) trisodium citrate (Becton Dickinson Vacutainer Systems, Plymouth, UK) in the ratio of one part of citrate to nine parts of whole blood, removing the need for immediate pro-cessing. The first 2 ml blood from each sample was discarded. Whole blood clot formation was assessed using TEG, according to the manufactures recommendations. All TEG analysis were conducted within 1.5 hour from blood sampling, but not earlier than 30 minutes, as it has been documented that blood stored for less than 30 minutes is not stable [130, 131]. For each TEG sample one ml whole blood was transferred into pre-warmed cuvettes with Kaolin (Haemoscope Co, Niles, IL, USA) to activate coagulation pro-cess. All vials with kaolin were stored at 2–10°C. After adequate mixing by five gentle inversions, 0.34 ml Kaolin-activated blood and 0.02 ml of calcium chloride were pipetted into a 37°C pre-warmed disposable plain cup and analyzed with TEG. The measurements were started immediately and run until all parameters of interest (“r”, “K”, “α angle”, “MA” and “G”) were finalized. The TEG underwent system electronics testing and daily quality control according to manufacturer‘s protocol [28, 94, 132, 133].

3.3.4. Measurment of coagulation factors, inflammatory biomarkers and lipid profile

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3.4. Calculation of the study power

The statistical power within a given sample size is influenced by several factors: the effect size, sample size, and the confidence interval. For the genetic study a sample size was selected based on assumption of 80% power to detect a variant at 5% significance, with minor allele frequency assumed to be 15% in cases and 7% in controls, with a ratio between cases and controls of 1:1. The following assumptions gave an outcome of 239 cases for each group. The calculation was performed using online power calcu-lation platform for genetic studies [134]. For the analysis of average differ-rences between the TEG parameters, coagulation, inflammation and lipid profile measurements the sample size was calculated based on the assump-tion of 95% confidence level and confidence interval of 15 with unknown population size yielding a sample size of 43 individuals. The prior mention-ned calculation was performed using a web based online calculator [135].

3.5. Statistical analysis 3.5.1. Genetic study

All study participants were stratified into two groups: 320 morbidly obese patients (BMI>40 kg/m2_{) and 303 control non-obese individuals} (BMI<30 kg/m2_{). Age and BMI are presented as mean and standard} devia-tions and was compared using unpaired Student's t-test. Categorical data (gender, distribution of genotypes or alleles) are presented as frequencies; comparesons were performed using the Chi-square test.

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3.5.2. Study of coagulation, inflammatory and lipid biomarkers

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4. RESULTS

4.1. Genetic study 4.1.1. Study population of genetic analysis

Characteristics of control and morbidly obese patient groups are presen-ted in Table 4.1.1.1. In total 623 individuals participapresen-ted in the study (320 morbidly obese and 303 control subjects). Individuals in the control group were significantly older than morbid obesity group subjects, 61.5 and 42.6 years, respectively (P<0.001). Males accounted for 60.9% in a group of patients with BMI>40 kg/m2_{, while in the control group they constituted} 42.6% (P<0.001). As expected, mean BMI in morbidly obese group was significantly higher (46.0 kg/m2_{) than compared to control group (25.1} kg/m2_{, P<0.001). Since proportion of males and females as well as age were} significantly different between the two groups, gender and age was included as confounding factors in further logistic regression analysis of genotyping results.

Table 4.1.1.1. Characteristics of the study groups

Morbid obesity group

(n=320) Control group (n=303) P value

Gender (n, %) Males 195 (60.9) 129 (42.6) <0.001

Females 125 (39.1) 174 (57.4)

Age (years) Mean ± SD 42.6±11.2 61.5±8.2 <0.001

BMI (kg/m2_{) Mean ± SD} _{46.0 ± 4.2} _{25.1 ± 2.7} _<0.001

BMI, body mass index.

4.1.2. Hardy-Weinberg equilibrium analysis

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Table 4.1.2.1. Analysis of Hardy-Weinberg equilibrium

SNP Allele frequencies Genotype

distribution frequency of Determined heterozygous allele Expected frequency of heterozygous allele P value rs1801133 T (0.281) C (0.719) 43/264/315 0.424 0.404 0.235 rs6427196 C (0.052) G (0.948) 1/63/559 0.101 0.099 1 rs6536024 C (0.343) T (0.657) 20/319/184 0.512 0.495 0.418 rs505922 C (0.396) T (0.604) 90/313/220 0.502 0.478 0.241 SNP, single nucleotide polymorphism.

4.1.3. Genotype and allele frequencies of ABO C>T, F5 C>G , MTHFR C>T and FGG C>T SNPs in morbidly obese and non-obese control individuals

Genotype and allele distributions for ABO T>C (rs505922), F5 C>G (rs6427196), MTHFR G>A (rs1801133) and FGG T>C (rs6536024) single nucleotide polymorphisms in both morbid obesity and control groups are presented in Table 4.1.3.1. All individuals that were included in the study were successfully genotyped for rs505922, rs6427196 and rs6536024 loci, while one individual in morbid obesity group failed genotyping for rs1801133. Overall, the genotypes and alleles of rs505922, rs6427196, rs1801133 and rs6536024 SNPs had similar distribution between morbidly obese and non-obese control individuals.

Table 4.1.3.1. Genotype and allele frequencies of ABO C>T (rs505922), F5

C>G (rs6427196), MTHFR C>T (rs1801133) and FGG C>T (rs6536024) SNPs in morbidly obese and non-obese control individuals

Alleles/genotypes Morbid obesity group

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38 Table 4.1.3.1. Continued

Alleles/genotypes Morbid obesity group

(n=320), n (%) (n=303), n (%) Control group MTHFR C>T (rs1801133) C _T 447 (70.1) _{191 (29.9)} 447 (73.8) _{159 (26.2)} CC 156 (49) 159 (54.5) CT 135 (42) 129 (38.7) TT 28 (8.9) 15 (6.9) FGG C>T (rs6536024) T 355 (55.5) 532 (66) C 285 (44.5) 274 (34) TT 100 (30.8) 184 (43.6) TC 155 (49.4) 164 (44.9) CC 65 (19.8) 55 (11.6)

4.1.4. Association analysis of ABO C>T (rs505922), F5 C>G

(rs6427196), MTHFR C>T (rs1801133) and FGG C>T (rs6536024) genotypes and alleles with morbid obesity

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Table 4.1.4.1. Association analysis of ABO C>T, F5 C>G , MTHFR C>T,

and FGG C>T genotypes and alleles with morbid obesity

Alleles/genotypes aOR 95% CI P value

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4.2. Coagulation, inflammatory and lipid biomarkers in the short and long term follow up after bariatric surgery study

4.2.1. Study population

The study included 60 consecutive patients (18 males and 42 females) referred to bariatric surgery with a mean age of 39.1 ± 11.9 years (age range 18-62) and a mean BMI of 47.5 ± 8.5 years (BMI range 34.3–84.9). The mean duration of surgical procedure was 91.4 ± 20.0 min. Gastric bypass surgery was performed in 38 (63.3%) patients and gastric plication in 22 (36.7%) patients. 35% were active smokers at the time of the surgery and 65% had previous diagnosis of arterial hypertension. The summary of patients’ characteristics is presented in Table 4.2.1.1. The patients were followed up for one year. Dynamics of patient weight loss and BMI after bariatric surgery is presented in Table 4.2.1.2.

Table 4.2.1.1. Clinical characteristics of study participants

All patients (n=60) Sex/male, n (%) 18 (30.0) Age, years ± SD 39.1 ± 11.9 BMI, kg/m2_{± SD} _{47.5 (8.5)} Gastric bypass, n (%) 38 (63.3) Smokers, n (%) 21 (35.0) AH, n (%) 39 (65.0)

Data are presented as n (%) or mean ± standard deviation.

Table 4.2.1.2. Dynamics of BMI and weight loss one month and one year

after the bariatric surgery

Baseline One month after surgery One year after surgery P value baseline vs

one month baseline vs one year

BMI, kg/m2_{± SD 47.5 ± 8.5} _{43.0 ± 10.4 32.7 ± 7.3} _0.001 _<0.0001

Weight (kg) ± SD 138.1 ± 63.3 123.7± 31.2 94.2 ± 22.4 <0.001 <0.0001

Significant p-values are marked in bold.

4.2.2. Preoperative hypercoagulability rates determined by TEG

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differences in BMI and CRP levels (Table 4.2.2.1). The age of patients in the group of G<11 was lower (37.8 years) than in the G≥11 group (43.6 years), but not statistically significant (p = 0.117). The only parameter that was significantly higher in G≥11 group compared to G<11 group was fibrinogen concentration: 4.23 g/l vs 3.8 g/l (p = 0.021). Among all study participants, 17 patients (28.3%) had MA≥68 mm and 43 patients (71.7%) had MA<68 mm. Patients with MA≥68 mm were significantly older (44.2 years) than in the group of patients with MA<68 mm (37.1 years; p = 0.046). Fibrinogen levels were also higher in hypercoagulable group (MA≥68 mm), when compared to the remaining subjects with MA<68 mm (Table 4.2.2.1, p = 0.025).

Table 4.2.2.1. Clinical characteristics of study participants in G<11 dyne/cm2

vs G≥1 dyne/cm2_{and MA<68 mm vs MA≥68 mm groups}

G<11

(n=46) (n=14) G≥11 P value MA<68 (n=43) MA≥68 (n=17) P value

Sex/male, n (%) 14 (30.4) 4 (25.0) 0.259 15 (34.8) 3 (17.6) 0.189 Age, years ± SD 37.8 ± 11.8 43.6 ±11.7 0.117 37.1 ± 11.5 44.2 ± 11.8 0.046 BMI, kg/m2_{± SD} 47.8 ± 9.1 46.3 ± 6.4 0.499 47.6 ± 9.4 47.0 ± 6.2 0.775 Gastric bypass, n (%) 30 (65.2) 8 (57.1) 0.211 27 (62.7) 11 (64.7) 0.768 AH, n (%) 29 (63.0) 10 (71.4) 0.231 27 (62.7) 12 (70.5) 0.568 CRP, mg/l ± SD 6.3 ± 4.8 5.8 ± 3.6 0.682 6.3 ± 4.8 5.8 ± 3.7 0.668 ESR, mm/h ± SD 16.1 ±10.1 16.6 ± 9.4 0.865 15.3 ± 9.6 18.6 ± 10.3 0.261 D-dimers, mg/l ± SD 0.47 ± 0.32 0.63 ±0.48 0.268 0.46 ± 0.32 0.60 ± 0.44 0.245 Fibrinogen, g/l 3.80 ± 0.80 4.23±0.51 0.021 3.8 ± 0.8 4.2 ± 0.5 0.025 Platelet count 244 ± 78 249 ± 70 0.821 243 ± 81 262 ± 73 0.389 aPTT 33.9 ± 4.9 34.9 ± 6.8 0.608 33.7 ± 4.9 34.6 ± 6.4 0.604 INR 0.99 ± 0.1 0.99 ± 0.1 1 0.99 ± 0.1 0.99 ± 0.1 1 Data are presented as n (percentage) or mean ± standard deviation. P values represent statistical comparison between patients with G≥11 vs G<11 dynes/cm2_{and MA≥68 mm vs}

MA<68 mm. P values where calculated using unpaired t-test. G, clot strength (dynes/cm2_);

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4.2.3. Dynamics of TEG parameters, fibrinogen and d-dimers in the perioperative period of bariatric surgery

Mean values of TEG before the surgery and in the immediate postope-rative period are presented in Table 4.2.3.1. All TEG measurements including k-time, α-angle, MA and G did not differ after the surgery when compared to baseline. The only exception was r-time which was shorter (6.02 s) when compared to baseline values (6.74 s; p = 0.003; Table 4.2.3.1).

Table 4.2.3.1. Dynamics of TEG parameters, fibrinogen and D-dimers from

baseline to immediate postsurgical time point.

TEG parameter Baseline Post-surgery P value (baseline vs post-surgery)

r-time (min) 6.74 ± 1.61 6.02 ± 1.41 0.003

k-time (min) 2.21 ± 0.87 1.99 ± 0.77 0.094

α-angle (°) 60.99 ± 8.45 63.02 ± 8.26 0.127

MA (mm) 65.60 ± 4.66 63.57 ± 5.52 0.132

G (dyne/cm2₎ _{9.83 ± 2.15} _{9.48 ± 2.35} _0.255

r-time, reaction time; k-time, clotting time; G, clot strength; MA, maximum amplitude;

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Table 4.2.3.2. Dynamics of TEG parameters, fibrinogen, and D-dimers from

baseline to POD1 and POD2 after the bariatric surgery TEG

parameter Baseline POD1 POD2 _baselineP value

vs POD1 vsbaseline POD2

r-time (min) 6.74 ± 1.61 6.96 ± 1.66 7.43 ± 1.55 0.371 0.022 k-time (min) 2.21 ± 0.87 2.03 ± 0.72 2.09 ± 1.13 0.174 0.505 α-angle (°) 60.99 ± 8.45 62.41 ± 7.63 62.71 ± 9.84 0.29 0.293 MA (mm) 65.60 ± 4.66 65.10 ± 4.54 66.11 ± 4.87 0.391 0.608 G (dyne/cm2₎ _{9.83 ± 2.15 9.61 ± 1.96 10.07 ± 2.21} _0.416 _0.575 Fibrinogen (g/l) 3.90 ± 0.75 3.79 ± 0.59 4.16 ± 0.62 0.109 0.012 D-dimer (mg/l) 0.49 ± 0.35 1.41 ± 1.04 0.95 ± 0.47 <0.001 <0.001

r-time, reaction time; k-time, clotting time; G, clot strength; MA, maximum amplitude,

P-values were calculated using paired t-test; significant P-values are marked in bold

4.2.4. Dynamics of TEG parameters, fibrinogen and D-dimers one month and one year after the bariatric surgery

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Table 4.2.4.1. Dynamics of TEG parameters, fibrinogen, and D-dimers one

month and one year after the bariatric surgery TEG

parameter Baseline after surgery One month One year after surgery

P value baseline vs

r-time (min) 6.74 ± 1.61 6.75 ± 1.31 6.52 ± 1.02 0.976 0.180 k-time (min) 2.21 ± 0.87 2.14 ± 0.67 2.17 ± 0.78 0.512 0.747 α-angle (°) 60.99 ± 8.45 61.09 ± 7.41 61.28 ± 7.64 0.932 0.811 MA (mm) 65.60 ± 4.66 64.36 ± 3.96 63.04 ± 4.64 0.054 0.001 G (dyne/cm2₎ _{9.83 ± 2.15 9.19 ± 1.64 8.71 ± 1.83} _0.018 _<0.001 Fibrinogen (g/l) 3.90 ± 0.75 4.08 ± 0.79 3.68 ± 0.53 0.157 0.012 D-dimer (mg/l) 0.49 ± 0.35 0.59 ± 0.41 0.54 ± 0.33 0.131 0.343 r-time, reaction time; k-time, clotting time; G, clot strength; MA, maximum amplitude;

P-values were calculated using paired t-test; significant P-values are marked in bold.

4.2.5. Correlation analysis between TEG parameters and clinical or laboratory parameters

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Table 4.2.5.1. Correlation analysis between TEG parameters and clinical or

laboratory parameters prior to bariatric surgery

BMI Fibrinogen Age D-dimer aPTT INR

r-time r -0.002 -0.103 -0.280 -0.121 0.299 -0.034 P-value 0.990 0.446 0.030 0.384 0.020 0.795 k-time r -0.115 -0.313 -0.259 -0.113 -0.243 -0.097 P-value 0.381 0.018 0.046 0.414 0.062 0.460 α angle r 0.047 0.333 0.260 0.117 0.124 0.114 P-value 0.722 0.011 0.045 0.399 0.345 0.385 MA r 0.053 0.431 0.238 0.146 0.092 0.026 P-value 0.686 0.001 0.067 0.293 0.486 0.847 G r 0.019 0.387 0.207 0.138 0.115 0.019 P-value 0.884 0.003 0.112 0.321 0.382 0.887

r – Pearson correlation coefficient; r-time, reaction time (min); k-time, clotting time (min);

α-angle (degrees); G, clot strength (dynes/cm2_{); MA, maximum amplitude (millimeters);}

BMI, body mass index; aPTT, activated partial thromboplastin time; INR, international normalized ratio. Significant r and P-values are marker in bold.

4.2.6. Predictive value of plasma fibrinogen for hypercoagulation

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Fig. 4.2.6.1. ROC curve of plasma ﬁbrinogen levels for prediction

of G≥11 dynes/cm2_{[area under the curve (AUC) = 0.680, 95% conﬁdence}

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Fig. 4.2.6.2. ROC curve of plasma ﬁbrinogen levels for predicting

MA≥68 mm [area under the curve (AUC) = 0.679, 95% confidence interval (CI) 0.53–0.83, P = 0.041]. The optimal cutoff point for fibrinogen to predict MA≥68 was 3.85 g/l (sensitivity of 80.0%, specificity of 64.3%)

4.2.7. Analysis of blood coagulation factors, inflammatory markers and lipid profile in the long term follow up after bariatric surgery

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activity and factor VII activity following the bariatric surgery clearly show beneficiary effects through reduction of pro-coagulation associated pathways (Fig. 4.2.7.1).

Table 4.2.7.1. Values of protein S, protein C, antithrombin III activity and

factor VII activity from baseline, within one month and one year after the bariatric surgery

Coagulation

factor Baseline after surgery One month after surgery One year _{baseline vs}P value

Free Protein S (IU/dl) 78.9 ± 29.2 80.6 ± 27.2 85.0 ± 21.5 0.695 0.080 Functional protein C (IU/dl) 84 ± 26.5 64 ± 9.5 74 ± 13.6 <0.001 <0.05 Antithrombin III activity (%) 106.3 ± 20.2 115.6 ± 9.9 119.2 ± 10.2 <0.001 <0.001 Factor VII activity (%) 132.7 ± 44.8 122.8 ± 29.2 123.6 ± 27.5 0.038 0.043 Significant P-values are marked in bold.

Fig. 4.2.7.1. Dynamics of free protein S, functional protein C,

antithrombin III activity and factor VII activity at baseline, one month and one year after the bariatric surgery

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Our study clearly showed significant reduction of inflammatory mar-kers in the follow-up period of bariatric surgery (Table 4.2.7.2; Fig. 4.2.7.2). Hs-CRP decreased from 7.1 mg/l to 4.5 mg/l at one month and to 2.3 mg/l at one year after the surgery (P<0.001; Table 4.2.7.2). Nearly similar decrease was also observed for CRP that significantly decreased both after one month as well as one year when compared to baseline values obtained prior to surgery (Table 4.2.7.2). Dynamics of hs-CRP and CRP following bariatric surgery is presented in Fig. 4.2.7.2.

Table 4.2.7.2. Values of hs-CRP and CRP at baseline, one month and one

year after the bariatric surgery Baseline One month

after surgery after surgery One year _{baseline vs}P value

hs-CRP (mg/l) 7.1 ± 7.4 4.5 ± 4.6 2.3 ± 4.7 <0.001 <0.001

CRP (mg/l) 6.1 ± 4.5 5.2 ± 4.7 2.6 ± 4.4 <0.001 <0.001

Hs-CRP – high sensitivity C reactive protein; CRP – C reactive protein; significant P-values are marked in bold.

Fig. 4.2.7.2. Dynamics of hs-CRP and CRP concentrations at baseline, one

month and one year after the bariatric surgery coagulation factors

hs-CRP – high sensitivity C reactive protein; CRP – C reactive protein *P < 0.001.

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sed significantly one month after the surgery (P<0.001), but then returned back to preoperative values at one year after the operation. LDL levels were also significantly reduced from 3.2 mg/l to 2.7 mg/l when evaluated at one month after surgery. Triglyceride levels revealed a gradual decrease from baseline 1.7 mg/l to 1.3 mg/l at one-year post-surgical time point. HDL levels improved from 1.2 mg/l prior to operation to 1.7 mg/l at one year post-operatively (P<0.001; Table 4.2.7.3; Fig. 4.2.7.3).

Table 4.2.7.3. Lipid profile values at baseline, one month and one year after

the bariatric surgery Coagulation

factor Baseline One month after surgery after surgery One year _{baseline vs}P value

CHOL (mg/l) 5.1 ± 1.2 4.4 ± 1.0 5.2 ± 1.1 <0.001 0.569 LDL (mg/l) 3.2 ± 0.9 2.7 ± 0.8 3.0 ± 1.0 <0.001 0.213 TRIG (mg/l) 1.7 ± 0.8 1.5 ± 0.6 1.3 ± 0.7 0.041 <0.001

HDL (mg/l) 1.2 ± 0.3 1.1 ± 0.3 1.7 ± 0.4 0.122 <0.001

CHOL – cholesterol; LDL – low-density lipoproteins; TRIG – triglycerides; HDL – high density lipoproteins; significant P-values are marked in bold.

Fig. 4.2.7.3. Dynamics of CHOL, LDL, TRIG and HDL from baseline,

one month and one year after the bariatric surgery coagulation factors

THROMBOSIS ASSOCIATED BIOMARKERS IN MORBID OBESITY AND BARIATRIC SURGERY

Kristina Kupčinskienė