The distribution of sport performance gene variations through COVID-19 disease severity

The distribution of sport performance gene variations through COVID-19 disease severity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The distribution of sport performance gene variations through COVID-19 disease severity Guven Yenmis, Ilayda Kallenci, Mehmet Dokur, Suna Koc, Sila Basak Yalinkilic, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5674989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Since its emergence in 2020, researchers worldwide have been collaborating to better understand the SARS-CoV-2 disease’s pathophysiology. Disease severity can vary based on several factors, including comorbidities and genetic variations. Notably, recent studies have highlighted the role of genes associated with athletic performance, such as ACE, ACTN3, and PPARGC1A, in influencing muscle function, cardiovascular health, and the body's metabolic response. Given that these genes also impact oxidative metabolism, inflammation, and respiratory efficiency, we hypothesized that they might play a critical role in the host’s response to SARS-CoV-2 infection. Aims This study aimed to investigate the association between disease severity and genetic polymorphisms in these sports performance-related genes, specifically ACE rs4646994, ACTN3 rs1815739, and PPARGC1A rs8192678. Methods A total of 422 COVID-19-positive patients were included in the study. The participants were divided into three groups: a severe group (77 patients) requiring intensive care unit (ICU) admission, a mild group (300 patients) exhibiting at least one symptom, and an asymptomatic control group. Genotyping was performed using restriction fragment length polymorphism PCR. Results The D allele and DD genotype of ACE and the T allele and TT genotype of ACTN3 were found to confer protective effects against severe SARS-CoV-2 infection. Conversely, the PPARGC1A TC genotype and the ACE-PPARGC1A ins/ins + TC combined genotype were associated with increased disease severity (p < 0.05). Conclusions Although vaccination has reduced the severity of SARS-CoV-2, the virus continues to impact human health. Interindividual differences due to these genetic variations will broaden the horizon of knowledge on the pathophysiology of the disease. COVID-19 sports performance ACTN3 ACE PPARGC1A intensive care unit disease severity Figures Figure 1 1. Introduction The coronavirus disease 2019 (COVID-19), induced by the severe acute respiratory syndrome virus (SARS-CoV-2), has unexpectedly shifted into a pandemic, with more than 776 million confirmed cases and above 7 million deaths worldwide as of September 22, 2024 [ 1 ]. SARS-CoV-2 can cause severe and potentially life-threatening infections, particularly in elderly individuals with significant underlying health conditions. The most frequently reported comorbidities among COVID-19 patients are coronary artery disease (CAD) (6–8%), diabetes mellitus (DM) (19%), and hypertension (HT) (27–30%) [ 2 , 3 ]. In addition to these comorbidities, COVID-19 may manifest with a variety of clinical symptoms, including fever [ 4 ], fatigue [ 5 ], and severe complications such as sepsis [ 6 ], pulmonary embolism (PE) [ 7 ], bronchopneumonia (BP) [ 8 ], and acute respiratory distress syndrome (ARDS) [ 9 ]. These complications can exacerbate the severity of the disease and contribute to the progression of critical illness. Therefore, identifying correlations between these predictive factors may help reveal additional risk indicators, aiding in the identification and management of high-risk populations [ 10 ]. Moderate symptoms of COVID-19 may progress to fatal respiratory failure due to the development of ARDS. The renin-angiotensin-aldosterone system (RAAS) has been implicated in the pathophysiology of COVID-19 [ 11 ]. Additionally, the roles of angiotensin-converting enzymes 1 and 2 (ACE and ACE2) in ARDS progression are well established. Alveolar epithelial cells express high levels of ACE and ACE2, which serve as key regulators of RAAS by maintaining immunological and pulmonary vascular homeostasis. SARS-CoV-2 enters alveolar epithelial cells via membrane-bound ACE2 receptors. Disruption of the ACE/ACE2 counter-regulation through this mechanism leads to endothelial dysfunction and triggers severe, detrimental immune responses [ 12 ]. Common genetic variants in ACE genes have been associated with increased risk for HT, PE, renal failure, and cardiac diseases. Specifically, the 287 base pair ACE I/D polymorphism (rs4646994) is linked to elevated levels of ACE in the blood, an enzyme that counteracts the function of ACE2. The D/D genotype is reported to exhibit the highest ACE levels compared to the ID and II genotypes [ 13 ]. Due to the antagonistic relationship between ACE and ACE2, increased ACE expression is associated with reduced ACE2 receptor expression, and this polymorphism has been connected to poor outcomes in both ARDS and SARS [ 14 ]. Consequently, the presence of the D allele may also influence the clinical course of COVID-19 by lowering ACE2 receptor levels. Alpha-Actinin-3 (ACTN3) is a key component of the skeletal muscle Z-disk, specifically in fast-twitch muscle fibers [ 15 ], and interacts with various structural, signaling, and metabolic proteins [ 16 , 17 ]. A C > T substitution at codon 577 of the ACTN3 gene is a common genetic mutation, leading to a stop codon (X) and the production of a truncated protein [ 18 ]. The ACTN3 R577X polymorphism (rs1815739) results in ACTN3 deficiency, which is associated with reduced rapid contractile capacity, lower bone mass or density, but enhanced endurance performance. This deficiency affects approximately 1.5 billion people worldwide, resulting in muscle weakness and a shift toward a more oxidative metabolism. In a comprehensive meta-analysis of the ACTN3 rs1815739 polymorphism, Alfred et al. demonstrated that the homozygous CC genotype is more common in Europeans, although it is not associated with athletic performance in the general population, whereas the homozygous TT genotype is frequently linked to improved aerobic fitness [ 19 ]. Some studies suggest that ACTN3 polymorphisms may influence the inflammatory response, which plays a critical role in determining COVID-19 severity [ 20 ]. Since fast-twitch fibers are also present in respiratory muscles, variations in ACTN3 could affect the strength and endurance of these muscles, potentially influencing respiratory efficiency and contributing to the severity of COVID-19 [ 21 ]. The PPARGC1A gene, located at the chromosomal position 4p15.2, plays key roles in fiber type determination, lipid metabolism, skeletal muscle fiber formation, and glucose regulation [ 22 , 23 ]. It encodes the transcriptional coactivator peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), which belongs to the peroxisome proliferator-activated receptor family. PPARGC1A activates various transcription factors that regulate a wide range of biological processes. Several amino acid polymorphisms have been identified within the coding region of PPARGC1A , including rs8192678 (Gly482Ser), which has been suggested to have functional significance [ 24 ]. The PPARGC1A rs8192678 polymorphism has been associated with high blood pressure, obesity, and DM [ 25 – 27 ]. Additionally, PPARGC1A plays a role in oxidative phosphorylation, which is essential for aerobic capacity and endurance [ 28 ]. As a result, polymorphisms in PPARGC1A have been linked to cardiovascular health [ 29 ]. Given the frequent cardiac complications observed in severe COVID-19 cases, these polymorphisms may be correlated with disease outcomes. Furthermore, PPARGC1A has been implicated in the regulation of inflammatory processes, mitochondrial efficiency, and energy production. Thus, variants in PPARGC1A could impact overall physical endurance and the body's ability to mount an effective immune response to SARS-CoV-2, influencing disease severity [ 24 , 30 ]. Based on this background, the present study is set to investigate the roles of the ACE rs4646994, ACTN3 rs1815739, and PPARGC1A rs8192678 polymorphisms in determining the severity of COVID-19 outcomes. By doing so, we aim to contribute to the growing body of evidence on host genetic factors shaping individual variability in disease outcomes. We hypothesize that specific genotypes of these genes may confer protective or risk-enhancing effects, potentially explaining some of the inter-individual variability observed in COVID-19 severity. This research not only seeks to elucidate the genetic factors influencing COVID-19 outcomes but also explores the potential application of these findings in athletic populations to predict susceptibility to infectious diseases such as COVID-19. 2. Materials and Methods 2.1. Samples A total of 422 COVID-19-positive samples were collected from the patients who were admitted to Biruni University Hospital between March and October 2021. The samples were divided into two main groups: COVID-19 positive asymptomatic control (outpatients with no symptoms and no need for hospitalization.) and COVID-19 positive symptomatic patients (inpatients or outpatients with symptoms). The symptomatic group was futher divided into two subclasses: mild (ymptoms requiring hospitalization without intensive care) and severe (cases requiring intensive care, mechanical ventilation, or resulting in death). Severity classification was based on WHO guidelines and confirmed by reviewing medical records The control group had 50 samples (age 31 ± 1.6), the mild group had 300 samples (age 48 ± 1.1), and the severe group had 77 samples (age 63 ± 1.8). There was no gender bias (49.6% female). Exclusion criteria included teenagers and elderly patients (under 20 or over 65 years old), pregnant or breastfeeding patients, those with weakened immune systems (e.g., receiving chemotherapy), and patients unable to follow up due to various reasons. The demographic evaluation of age, gender, CAD, DM, HT, and smoking status is shown in Table 1 . All patients were vaccinated with Pfizer-BioNTech and followed up for eight months to monitor disease progression. Table 1 Demographic evaluation of COVID-19 patients by the severity of COVID-19 disease Control Group Patient Groups Asymp (n = 50) Mild (n = 299) Severe (n = 77) Overall (n = 376) Age (M ± SE) 31 ± 1.6 48 ± 1.1 63 ± 1.8 51 ± 1.0 Gender, n (%) Male 22 (44) 151 (50) 42 (55) 193 (51) Female 28 (56) 149 (50) 35 (45) 184 (49) Smoking, n (%) No 30 (60) 170 (57) 25 (32) 195 (52) Yes 20 (40) 129 (43) 52 (68) 181 (48) HT, n (%) No 43 (86) 207 (69) 30 (39) 237 (63) Yes 7 (14) 92 (31) 47 (61) 139 (37) DM, n (%) No 44 (88) 281 (94) 72 (94) 353 (94) Yes 6 (12) 18 (6) 5 (6) 23 (6) CAD, n (%) No 50 (100) 272 (91) 58 (75) 330 (88) Yes 0 (0) 27 (9) 19 (25) 46 (12) Fever, n (%) No 50 (100) 84 (28) 73 (95) 157 (42) Yes 0 (0) 215 (72) 4 (5) 219 (58) Fatigue, n (%) No 50 (100) 83 (28) 74 (96) 157 (42) Yes 0 (0) 216 (72) 3 (4) 219 (58) Sepsis, n (%) No 50 (100) 266 (89) 8 (10) 276 (73) Yes 0 (0) 33 (11) 69 (90) 102 (27) ARDS, n (%) No 50 (100) 270 (90) 21 (27) 291 (77) Yes 0 (0) 29 (10) 56 (73) 85 (23) PE, n (%) No 50 (100) 278 (93) 75 (97) 353 (94) Yes 0 (0) 21 (7) 2 (3) 23 (6) BP, n (%) No 50 (100) 265 (89) 73 (95) 338 (90) Yes 0 (0) 34 (11) 4 (5) 38 (10) One of the symptom data of a participant is missing in the mild group. One of the disease or smoking history data of a participant is missing in the mild group. HT, Hypertension; DM, Diabetes Mellitus; CAD, Coronary Artery Disease; ARDS, Acute Respiratory Distress Syndrome; PE, Pulmonary Embolism; BP, Bronchopneumonia. 2.2. Genotyping Four milliliters of peripheral blood were drawn from each participant, and DNA was isolated using the Promega Wizard Genomic DNA Purification Kit. The concentration and optical density (OD) of DNA were measured using a NanoDrop spectrophotometer (NanoPhotometer P300, Implen GmbH, Germany). Samples with concentrations between 40–60 ng/µl and OD values of 1.8 ± 0.1 were included​. Polymerase Chain Reaction (PCR) was performed for each polymorphism according to the manufacturer's instructions (PCR Master Mix (2X), Catalog # K0172, Massachusetts, USA). First, the DNA was denaturated at 94°C for 2 minutes. Then, it went through 35 cycles of thermal cycling, which included denaturation at 94°C for 30 seconds, primer annealing at 59°C for 30 seconds, and extension at 72°C for 30 seconds. The protocol concluded with a final extension step at 72°C for 3 minutes. The primers were sense-CTGTAAGCCACTGCTGGAGA and antisense-AAATGAAGGGACCCAAGTG for ACE rs4646994, sense-GTGTGGCTGGTACACTCTGTG antisense-CTGTCTCGGGCTCATCTGTA for ACTN3 rs1815739, and sense-TGCTACCTGAGAGAGACTTTGG and antisense-TGGAATATGGTGATCGGGAACA for PPARGC1A rs8192678. PCR products were electrophoresed at 120 V on a 2% agarose gel for 20 minutes using a Bio-Rad electrophoresis system (Sub-Cell Model 192 Cell, Biorad, USA), and the PCR fragments were monitored through a screening system (ChemiDoc MP, Biorad, USA). The PCR product of ACE rs4646994 has two alleles: 371 bp (the deletion allele) or 660 bp (the insert allele). The del/del genotype is observed as a single band with 371 bp in length, whereas the heterozygous del/ins genotype is observed as two distinct bands with 371 bp and 660 bp in length, and the ins/ins genotype is observed as a single band with 660 bp in length (Fig. 1 a). The restriction fragment length polymorphism (RFLP)-PCR was used to find out the genotypes of the PPRGC1A rs8192678 and ACTN3 rs1815739 polymorphisms.One unit of Mspl restriction enzyme (Catalog #R0106S, New England Biolabs, Ipswich, USA) was used to digest the PPRGC1A PCR product. The PCR product-enzyme mixture was incubated at 37°C for 15 min. The restriction-digested PCR product was electrophoresed at 100 V on a 2% agarose gel for 30 minutes. Homozygous rs8192678 CC genotype was observed as two bands with 149-bp and 311-bp in lengths; heterozygous rs8192678 C/T genotype was observed as three distinct bands with 149-bp, 311-bp, and 460-bp in lengths; and homozygous rs8192678 TT genotype was observed as a single band with 460-bp in length (Fig. 1 b). One unit of Ddel restriction enzyme (Catalog #R0175L, New England Biolabs, Ipswich, USA) was used to digest the ACTN3 PCR product. The PCR product and restriction enzyme mixture were incubated at 37°C for 15 minutes, followed by thermal inactivation of the restriction enzyme at 65°C for 15 minutes. The restriction-digested PCR product was electrophoresed on a 2% agarose gel at 100 V for 10 minutes. The homozygous rs1815739 CC genotype was observed as a single band with 412-bp, the heterozygous rs1815739 C/T genotype was observed as two distinct bands with 207-bp and 412-bp in length, and the homozygous rs1815739 TT genotype was observed as a single band with 207-bp in length (Fig. 1 c). 2.3. Statistical Analysis Statistical analysis was conducted using GraphPad Prism 5.0 software (GraphPad Software, San Diego, CA, USA) and Minitab 18 Statistical Software (Minitab Inc.). The power of the study was calculated as 80%. Age was displayed as mean ± standard error, demographic and symptomatic data were displayed as counts and percentages, and genotypes and allele frequencies were displayed as counts. Mann Whitney The U test was used for the comparison of ages between the two groups. The Hardy-Weinberg equilibrium was determined for compatibility among groups using chi-square tests (χ²). The comparison of frequencies and ratios between groups was evaluated using chi-square tests or Fisher's exact test. The associations between rs4646994, rs8192678, and rs1815739 genotypes, alleles, and COVID-19 were assessed using chi-square tests to calculate odds ratios (ORs), with homozygous dominant genotypes as the reference category for each polymorphism. A p-value of < 0.05 was considered statistically significant. 3. Results 3.1. Demographic evaluation of the patients A total of 377 COVID-19 patients were included in the study, comprising 300 mild cases with at least one symptom, 77 severe cases requiring intensive care, and 50 asymptomatic controls. The male percentage was 44% in the asymptomatic group, 50% in the mild group, and 55% in the severe group, with no significant gender distribution differences (p > 0.05). However, the mean age significantly increased with disease severity across all groups (p < 0.0001) (Data not shown). 3.2. The genotype analysis of ACE1 rs4646994, PPARGC1A rs8192678, and ACTN3 rs1815739 polymorphisms The effects of ACE1 rs4646994, PPARGC1A rs8192678, and ACTN3 rs1815739 polymorphisms on COVID-19 severity were evaluated. All polymorphisms were in Hardy-Weinberg equilibrium (HWE) (data not shown). ACE1 rs4646994 was found to have a protective effect, especially in female patients, where the DD genotype showed a 3.095-fold protective effect compared to II + ID (p = 0.01)​. 3.3. The genotype analysis of the ACE1 rs4646994 polymorphism A significant protective effect was observed for the ACE1 rs4646994 DD genotype in the severe group compared to the mild group, with a 1.912 times protective role (p = 0.0185) (Table 2 ). In females, the protective role of the DD genotype was even higher, with a 3.095-fold protective effect (p = 0.01). Additionally, the ID genotype was associated with a 1.937 times increased risk of severe disease in females (p = 0.0112)​ (Supplementary Files Table S1 and Table S2). Table 2 The distribution of the ACE1 rs4646994 polymorphism and the disease severity ACE1 rs4646994 Control Group Patient Groups p-value (OR) Genotypes Asymp (n) Mild (n) Severe (n) Overall (n) Asymp vs Patient Asymp vs Mild Asymp vs Severe Mild vs Severe II 8 48 15 63 Ref Ref Ref Ref ID 25 122 40 162 0.6517 (0.823) 0.6386 (0.813) 0.7541 (0.853) 0.8901 (1.049) DD 17 130 22 152 0.7797 (1.135) 0.5980 (1.275) 0.4947 (0.690) 0.0988 (0.542) Dominant Model (II + ID vs DD) 33 170 55 225 Ref Ref Ref Ref 17 130 22 152 0.3906 (1.311) 0.2157 (1.484) 0.5170 (0.777) 0.0185 (0.523) Recessive Model (II vs ID + DD) 8 48 15 63 Ref Ref Ref Ref 42 252 62 314 0.8991 (0.949) 1.0000 (1.000) 0.6188 (0.787) 0.4652 (0.787) I 41 218 70 288 Ref Ref Ref Ref D 59 382 84 466 0.5883 (1.124) 0.3709 (1.218) 0.4844 (0.834) 0.0377 (0.685) Ref, reference; OR, odds ratio; a chi-square test was used for analysis. p < 0.05 values were shown in bold 3.4. The genotype analysis of the PPARGC1A rs8192678 polymorphism The PPARGC1A rs8192678 TC genotype was found to significantly increase the risk of COVID-19 disease by 4.122-fold in all patients (p < 0.0001), with similar increases observed in both the mild and severe groups. The T allele was found to increase disease risk only in the mild group by 1.565-fold (p = 0.0456). In the recessive model, TC + TT increased the COVID-19 disease risk by 3.054-fold in all COVID-19 patients, by 3.167-fold in the mild group, and by 2.667-fold in the severe group (p = 0.0002, p = 0.0001, and p = 0.0092, respectively) (Table 3 ). Table 3 The distribution of the PPARGC1A rs8192678 polymorphism and the disease severity PPARGC1A rs8192678 Control Patients p-value (OR) Genotypes Asymp n OverallPatients n Mild n Severe n Asymp vs Overall Asymp vs Mild Asymp vs Severe Mild vs Severe CC 25 93 72 21 Ref Ref Ref Ref TC 15 230 183 47 < 0.0001 (4.122) < 0.0001 (4.236) 0.0013 (3.730) 0.6684 (0.881) TT 10 54 45 9 0.3634 (1.452) 0.2854 (1.563) 0.8995 (1.071) 0.3911 (0.686) Dominant Model (CC + TCvs TT) 40 323 255 68 Ref Ref Ref Ref 10 54 45 9 0.2907 (0.669) 0.3684 (0.706) 0.1995 (0.529) 0.4593 (0.750) Recessive Model (CC vs TC + TT) 25 93 72 21 Ref Ref Ref Ref 25 284 228 56 0.0002 (3.054) 0.0001 (3.167) 0.0092 (2.667) 0.5523 (0.842) C allele 65 416 324 89 Ref Ref Ref Ref T allele 35 338 273 65 0.0626 (1.509) 0.0456 (1.565) 0.2507 (1.356) 0.4336 (0.867) Ref, reference; OR, odds ratio; Chi-square test was used for analysis. p < 0.05 values were shown in bold As the polymorphism was analyzed in terms of gender, carrying the TC genotype increased the risk of COVID-19 disease by 4.625-fold in women (p = 0.0006) and by 3.542-fold in men (p = 0.011) of overall COVID-19 patients, by 4.74-fold in women (p = 0.0006) and by 3.651-fold (p = 0.0106) in men of the mild group, 4.167-fold (p = 0.0141) in women, and 3.214-fold (p = 0.0481) in men of the severe group. In the recessive model, in women, TC + TT increased the risk of COVID-19 disease by 3.564-fold in overall COVID-19 patients, by 3.167-fold in the mild group, and by 3.333-fold in the severe group (p = 0.0014, p = 0.0016, and p = 0.0237). In men, however, TC + CC increased the risk of disease by 2.517-fold in overall COVID-19 patients and by 2.662-fold in the mild group (p = 0.0393 and p = 0.0321, respectively) (Supplementary Files Table S3 and Table S4). 3.5. The genotype analysis of the ACTN3 rs1815739 polymorphism For ACTN3 rs1815739, the TT genotype was associated with an 8.389-fold reduction in COVID-19 disease risk (p < 0.0001) compared to the CC + TC genotypes. This protective effect was even more pronounced in the mild group, with a 16.15-fold reduction (p < 0.0001). In the gender-based analysis, the TT genotype was associated with a 9.308-fold reduction in disease risk in women and a 7.537-fold reduction in men (p < 0.0001)​ When the effect of the T allele compared to the C allele on both overall COVID-19 patients and disease severity was examined, the T allele was associated with a 4.514-fold reduction of COVID-19 disease risk overall in COVID-19 patients and a 5.819-fold reduction of the risk in the mild group (p < 0.0001) (Table 4 ). Table 4 The distribution of the ACTN3 rs1815739 polymorphism and the disease severity ACTN3 rs1815739 Control Patients p-value (OR) Genotypes Asymp n Overall Patients n Mild n Severe n Asymp vs Patient Asymp vs Mild Asymp vs Severe Mild vs Severe CC 0 5 5 0 Ref Ref Ref Ref TC 11 260 241 19 1.0000 b (2.059) 1.0000 b (1.909) - 1.0000 b (0.888) TT 39 112 54 58 0.3322 b (0.259) 0.1537 b (0.125) - 0.0572 b (11.81) Dominant Model (CC + TCvs TT) 11 265 246 19 Ref Ref Ref Ref 39 112 54 58 < 0.0001 a (0.119) < 0.0001 a (0.062) 0.7288 a (0.861) < 0.0001 a (13.91) Recessive Model CC vs TC + TT) 0 5 5 0 Ref Ref Ref Ref 50 372 295 77 1.0000 b (0.671) 1.0000 b (0.532) - 0.5878 b (2.885) C allele 11 270 251 19 Ref Ref Ref Ref T allele 89 484 349 135 < 0.0001 a (0.222) < 0.0001 a (0.172) 0.7469 a (0.878) < 0.0001 a (5.110) Ref, reference; OR, odds ratio a Chi-square test or b Fisher's exact test were used for analysis. p < 0.05 values were shown in bold. As women and men were evaluated separately, the T allele was associated with a 4.828-fold and 4.195-fold reduction in the COVID-19 disease risk in overall COVID-19 patients (p = 0.0001 and p = 0.0015) and a 6.105-fold and 5.508-fold reduction of the COVID-19 disease risk in the mild group (p < 0.0001 and p = 0.0001). Besides, compared to the mild group, TT was associated with a 13.91-fold increase in the COVID-19 disease risk in the severe group compared to CC + TC and a 5.11-fold increase in the COVID-19 disease risk in the severe group compared to the C allele (p < 0.0001). Furthermore, compared to CC + TC, the TT genotype was associated with a 13.67-fold and 14.06-fold increase in the COVID-19 disease risk in women and men in the severe group, respectively. The T allele was also associated with a 4.965-fold and 5.226-fold increase in the COVID-19 disease risk in women and men of the severe group, respectively (p < 0.0001) (Supplementary File Table S5 and Table S6). 3.6. Combined genotype analysis of ACE1 rs4646994, PPARGC1A rs8192678, and ACTN3 rs1815739 polymorphisms The combined analysis of ACE1 rs4646994 and PPARGC1A rs8192678 polymorphisms revealed significant interactions. For instance, the ins/ins + TC genotype was associated with a 7.6-fold increased risk of COVID-19 disease (p = 0.016) and a similar risk-8.25-fold increase in the severe group (p = 0.0393) (Table 5 ). No statistically significant differences were found when comparing the combined genotypes of ACE1 rs4646994 and ACTN3 rs1815739 (p > 0.05)​ (Table 6 ). Table 5 The combined genotype analysis of ACE1 rs4646994 and PPARGC1A rs8192678 polymorphisms ACE1 rs4646994 + PPARGC1A rs8192678 Control Patients p-value (OR) Asymp n Overall Patients n Mild n Severe n Asymp vs Patient Asymp vs Mild Asymp vs Severe Mild vs Severe II + CC 6 15 11 4 Ref Ref Ref Ref II + TC 2 38 27 11 0.0160 b (7.600) 0.0380 b (7.364) 0.0393 b (8.250) 1.0000 b (1.120) II + TT 0 10 10 0 0.1411 b (8.806) 0.0570 b (11.87) - 0.1245 b (0.122) ID + CC 13 39 27 12 0.7529 a (1.200) 0.8378 a (1.133) 0.7233 b (1.385) 1.0000 b (1.222) ID + TC 8 103 80 23 0.0104 b (5.150) 0.0102 b (5.455) 0.0645 b (4.313) 0.7449 b (0.791) ID + TT 4 20 15 5 0.4764 b (2.000) 0.4629 b (2.045) 0.6563 b (1.875) 1.0000 b (0.917) DD + CC 6 39 34 5 0.1749 b (2.600) 0.1522 b (3.091) 1.0000 b (1.250) 0.2436 b (0.404) DD + TC 5 89 76 13 0.0047 b (7.120) 0.0031 b (8.291) 0.1245 b (3.900) 0.2628 b (0.470) DD + TT 4 24 20 4 0.2906 b (2.400) 0.2697 b (2.727) 1.0000 b (1.500) 0.6857 b (0.550) OR, odds ratio; Ref, reference; a Chi-square test or b Fisher's exact test were used for analysis. p < 0.05 values were shown in bold. Table 6 The combined genotype analysis of ACE1 rs4646994 and ACTN3 rs1815739 polymorphisms ACE1 rs4646994 + ACTN3 rs1815739 Control Patients p-value (OR) Asymp n Overall Patients n Mild n Severe n Asymp vs Overall Asymp vs Mild Mild vs Severe II + CC 0 1 1 0 Ref Ref Ref II + TC 0 43 42 1 - - 1.0000 (0.106) II + TT 8 19 5 14 1.0000 (0.765) 0.4286 (0.216) 0.3000 (7.909) ID + TC 5 111 98 13 1.0000 (6.758) 1.0000 (5.970) 1.0000 (0.411) ID + TT 20 50 23 27 1.0000 (0.821) 1.0000 (0.382) 0.4706 (3.511) DD + TC 6 106 101 5 1.0000 (5.462) 1.0000 (5.205) 1.0000 (0.163) DD + TT 11 43 26 17 1.0000 (1.261) 1.0000 (0.768) 1.0000 (1.981) OR, odds ratio; Ref, reference; Fisher's exact test was used for analysis. Since there was more than one 0 (zero) in the reference value, the asymptomatic control and severe groups could not be compared. In addition, ID + CC and DD + CC could not be compared due to the insufficient number of samples and were removed from the table. 4. Discussion The WHO database indicates that the susceptibility and severity of COVID-19 vary globally [ 1 ]. COVID-19 infection can lead to various symptoms and complications that can negatively impact athletic activity [ 31 ]. However, some sports performance gene variations may also determine the severity of SARS-CoV-2 infection. Some researchers have hypothesized that regional differences in gene frequencies may explain these variations [ 32 , 33 ]. Moreover, variations in the expression and function of immune response genes may underlie individual susceptibility to a disease, the risk of hospitalization, and the likelihood of adverse events.Inherited and environmental factors that alter the expression and function of RAAS components, such as ACE, could explain the risk of developing COVID-19 and its adverse outcomes. Common variants in the two ACE genes have been linked to symptoms observed in COVID-19. In particular, the ACE rs4646994 polymorphism has been extensively studied due to its role in cardiovascular and pulmonary conditions [ 34 , 35 ]. The D allele of this polymorphism is associated with higher ACE expression, leading to reduced ACE2 receptor availability, the primary entry point for SARS-CoV-2​ [ 14 ]. In accordance with these findings, in the present study, DD was found to have a 1.912 times more protective role compared to II + ID, and the D allele alone was found to have a protective role compared to the I allele. These findings support the hypothesis that ACE polymorphisms can modulate disease severity by affecting lung function and immune response during infection​. However, some studies suggest that the D/D genotype might increase susceptibility to ARDS, a common complication in severe COVID-19 cases [ 13 ]​. Therefore, it is hypothesized that having the D allele for the ACE I/D polymorphism may worsen the clinical course of COVID-19 by decreasing ACE2 receptor levels [ 36 ]. This suggests that individuals with the ACE DD genotype could benefit from targeted therapies aimed at modulating the RAS pathway, such as ACE inhibitors or angiotensin receptor blockers. The PPARGC1A gene encodes a protein that regulates key genes involved in glucose and fatty acid metabolism [ 37 ]. The PPARGC1A rs8192678 polymorphism is also implicated in metabolic regulation, and polymorphisms in this gene have been linked to diabetes mellitus and hypertension, both risk factors for severe COVID-19 [ 25 – 27 ]. Physiological evidence suggests that this polymorphism affects blood lipid levels and insulin sensitivity, as the Ser allele carriers exhibit higher levels of insulin resistance and lipid dysregulation, which can lead to worse outcomes in patients already suffering from metabolic comorbidities [ 38 , 39 ]. As a consequence, these individuals have an elevated risk of type 2 DM [ 40 , 41 ]. Given that DM itself is a risk factor for severe COVID-19, the PPARGC1A rs8192678 polymorphism may influence COVID-19 pathophysiology. According to our results, the TC genotype was found to be a risk factor for all groups compared to the asymptomatic control group, and the T allele alone was found to be a risk factor compared to the C allele. This suggests that genetic variations in energy metabolism and oxidative phosphorylation, crucial during immune response, may determine how individuals respond to SARS-CoV-2 infection. Moreover, understanding this genotype's role could pave the way for interventions like antioxidant therapies or mitochondrial enhancers to help reduce the severity of COVID-19 in affected individuals. The ACTN3 R577X polymorphism, which affects about 1.5 billion people worldwide, results in a deficiency of the ACTN3 protein, a key component of fast-twitch muscle fibers. This deficiency leads to decreased muscle strength, improved endurance performance, and reduced bone mass. ACTN3 interacts with a variety of proteins involved in muscle structure, metabolism, and signaling, suggesting that its absence has a broad impact on muscle function [ 15 – 17 ]. The X allele of the ACTN3 gene, which is associated with less muscle mass, lower strength, and higher VO2 max, is more common in endurance athletes with a type I muscle fiber predominance. This suggests that the ACTN3 X allele may confer an advantage for endurance performance [ 42 ]. The XX genotype was also related to elevated cardiovascular fitness [ 43 ], which may explain the low VO2 max and high prevalence of CAD and HT in severe COVID-19 patients. As this genotype results in reduced alpha-actinin-3, potentially impairing respiratory efficiency, which could worsen outcomes in patients with respiratory infections like COVID-19. This genetic variation might be especially relevant for patients with pre-existing respiratory conditions or reduced muscle strength. The ACTN3 R577X polymorphism is a common nonsense mutation that results in the complete absence of ACTN3 in an estimated 16% of the global population [ 18 ]. Individuals with the RR genotype of the ACTN3 gene, which is associated with a higher proportion of type II muscle fibers and greater muscle strength, tend to perform better in strength and speed-power tests [ 44 ]. Interestingly, carriers of the ACTN3 X allele have a 1.72-fold higher risk of death than those with the ACTN3 577RR genotype in patients with congestive heart failure, suggesting that the ACTN3 genotype may be a prognostic marker for this condition [ 45 ]. This may be relevant to severe COVID-19 disease, as congestive heart failure can impair lung physiology. The findings of the current study, by contrast, do not support the previous research. We found that carrying the ACTN3 577XX genotype or T allele alone may decrease the severity of the COVID-19 disease.The ACTN3 TT genotype appears to offer a protective effect, possibly by supporting better muscle function and reducing the likelihood of respiratory complications during severe illness. This subgroup may inherently face a lower risk of severe outcomes, providing valuable insights for resource allocation and tailored clinical management. In terms of combined genetic effects, polymorphisms in ACE, ACTN3, and PPARGC1A likely interact to influence COVID-19 outcomes. The risk of COVID-19 disease was found to elevate in the presence of ACE1 rs4646994ins/ins + PPARGC1A rs8192678TC, ACE1 rs4646994ins/del + PPARGC1A rs8192678TC, and ACE1 rs4646994del/del + PPARGC1A rs8192678TC genotypes, but no statistically significant differences were found when ACE1 rs4646994-ins/ins and ACTN3 rs1815739 CC combined genotypes compared to other genotypes. This suggests that genetic predispositions affecting muscle function, cardiovascular health, and metabolism collectively impact an individual’s risk of severe COVID-19. Such findings emphasize the need to further investigate how genetic factors interact to influence COVID-19 severity, with the potential to inform predictive models and guide clinical interventions​. Conclusion To the best of our knowledge, this is the first study investigating the effect of ACTN3 and PPARGC1A polymorphisms on COVID-19 disease severity. A key limitation of case-control association studies is their small sample size, which can obscure true associations and lead to conflicting findings. Thus, the present findings in the Turkish population may require replication with larger sample sizes across multiple populations. Also, we did not follow the asymptomatic-control study subjects after sample retrieval to determine if they developed disease symptoms. At the individual patient level, risk stratification based on these genetic variants could guide more targeted clinical care, leading to better patient outcomes and reduced mortality and morbidity​. Future research should continue to explore how these polymorphisms can be used as biomarkers for COVID-19 outcomes. Declarations Funding: Financial support was received from the Scientific and Technological Research Council of Turkey (TUBITAK) (Fon number: 1919B012103061) and Biruni University Scientific Research Projects (Project Number: Biruni-BAP-2022-01-03). Author Contribution Conceptualization: G.Y., Me.Do.; Sample Collection: Me.Do., S.K., Ma.De.; Methodology: I.K., S.B.Y., E.A., H.A.; Data interpretation and Statistical analysis H.A.; Writing - first draft preparation: G.Y., Me.Do, E.A., I.K.; Writing – review, editing, and approval the final version of the manuscript: G.Y., Me.Do., S.K., Ma.De., I.K., E.A., S.B.Y., H.A.; Supervision: G.Y., Me.Do. Acknowledgement We would like to thank Biruni University Rector Prof. Dr. Adnan Yuksel M.D. and Biruni University BAMER president Prof. Dr. I. Tuncer Degim PhD. for their support of this scientific research. Data Availability Data is provided within the manuscript or supplementary information files. References World Health Organisation. Available online: https://data.who.int/dashboards/covid19/cases?n=c (accessed November 15, 2024) Gallo G, Calvez V, Savoia C. (2022) Hypertension and COVID-19: Current Evidence and Perspectives. High Blood Press Cardiovasc Prev 29(2):115–123. doi: 10.1007/s40292-022-00506-9 . Epub 2022 Feb 20. Huang C, Wang Y, Li X et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 15;395(10223):497–506. doi: 10.1016/S0140-6736(20)30183-5. Epub 2020 Jan 24. Erratum in: Lancet 2020;395(10223):496. doi: 10.1016/S0140-6736(20)30252-X . Islam MA, Kundu S, Alam SS et al. 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Seto JT, Chan S, Turner N et al. (2010) The effect of α-actinin-3 deficiency on muscle aging. Exp Gerontol 46(4):292–302. doi: 10.1016/j.exger.2010.11.006. Epub 2010 Nov 26. Charos AE, Reed BD, Raha D et al. (2012) A highly integrated and complex PPARGC1A transcription factor binding network in HepG2 cells. Genome Res 22(9):1668–79. doi: 10.1101/gr.127761.111 . Lin J, Wu H, Tarr PT et al. (2002) Transcriptional co-activator PGC-1 alpha drives the formation of slow-twitch muscle fibres. Nature 418(6899):797–801. doi: 10.1038/nature00904 . Handschin C, Spiegelman BM. (2008) The role of exercise and PGC1alpha in inflammation and chronic disease. Nature 454(7203):463–9. doi: 10.1038/nature07206 . Vimaleswaran KS, Luan J, Andersen G et al. (2008) The Gly482Ser genotype at the PPARGC1A gene and elevated blood pressure: a meta-analysis involving 13,949 individuals. J Appl Physiol (1985) 105(4):1352–8. doi: 10.1152/japplphysiol.90423.2008 . Epub 2008 May 8. Barroso I, Luan J, Sandhu MS et al. (2006) Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes. Diabetologia 49(3):501–5. doi: 10.1007/s00125-005-0130-2 . Epub 2006 Jan 25. Ridderstråle M, Johansson LE, Rastam L et al. (2006) Increased risk of obesity associated with the variant allele of the PPARGC1A Gly482Ser polymorphism in physically inactive elderly men. Diabetologia49(3):496–500. doi: 10.1007/s00125-005-0129-8 . Epub 2006 Feb 9. Lin J, Handschin C, Spiegelman BM. (2005) Metabolic control through the PGC-1 family of transcription coactivators. Cell Metab 1(6):361–70. doi: 10.1016/j.cmet.2005.05.004 . Arany Z, He H, Lin J et al. (2005) Transcriptional coactivator PGC-1 alpha controls the energy state and contractile function of cardiac muscle. Cell Metab 1(4):259–71. doi: 10.1016/j.cmet.2005.03.002 . Puigserver P, Spiegelman BM. (2003) Peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1 alpha): transcriptional coactivator and metabolic regulator. Endocr Rev 24(1):78–90. doi: 10.1210/er.2002-0012 . Widmann M, Gaidai R, Schubert I, et al. (2024) COVID-19 in Female and Male Athletes: Symptoms, Clinical Findings, Outcome, and Prolonged Exercise Intolerance-A Prospective, Observational, Multicenter Cohort Study (CoSmo-S). Sports Med 54(4):1033–1049. doi: 10.1007/s40279-023-01976-0 . Hatami N, Ahi S, Sadeghinikoo A et al. (2020) Worldwide ACE (I/D) polymorphism may affect COVID-19 recovery rate: an ecological meta-regression. Endocrine 68(3):479–484. doi: 10.1007/s12020-020-02381-7 . Epub 2020 Jun 15. Cao Y, Li L, Feng Z et al. (2020) Comparative genetic analysis of the novel coronavirus (2019-nCoV/SARS-CoV-2) receptor ACE2 in different populations. Cell Discov 6:11. doi: 10.1038/s41421-020-0147-1 . Marshall RP, Webb S, Bellingan GJ et al. (2002) Angiotensin converting enzyme insertion/deletion polymorphism is associated with susceptibility and outcome in acute respiratory distress syndrome. Am J Respir Crit Care Med 166(5):646–50. doi: 10.1164/rccm.2108086 . Itoyama S, Keicho N, Quy T et al. (2004) ACE1 polymorphism and progression of SARS. Biochem Biophys Res Commun 323(3):1124–9. doi: 10.1016/j.bbrc.2004.08.208 . Susilo H, Pikir BS, Thaha M et al. (2022) The Effect of Angiotensin Converting Enzyme (ACE) I/D Polymorphism on Atherosclerotic Cardiovascular Disease and Cardiovascular Mortality Risk in Non-Hemodialyzed Chronic Kidney Disease: The Mediating Role of Plasma ACE Level. Genes (Basel) 13(7):1121. doi: 10.3390/genes13071121 . Yoon JC, Puigserver P, Chen G et al. (2001) Control of hepatic gluconeogenesis through the transcriptional coactivator PGC-1. Nature 413(6852):131–8. doi: 10.1038/35093050 . Hara K, Tobe K, Okada T et al. (2002) A genetic variation in the PGC-1 gene could confer insulin resistance and susceptibility to Type II diabetes. Diabetologia 45(5):740–3. doi: 10.1007/s00125-002-0803-z . Epub 2002 Apr 23. Zhang SL, Lu WS, Yan L et al. (2007) Association between peroxisome proliferator-activated receptor-gamma coactivator-1alpha gene polymorphisms and type 2 diabetes in southern Chinese population: role of altered interaction with myocyte enhancer factor 2C. Chin Med J (Engl) 120(21):1878–85. Ek J, Andersen G, Urhammer SA et al. (2001) Mutation analysis of peroxisome proliferator-activated receptor-gamma coactivator-1 (PGC-1) and relationships of identified amino acid polymorphisms to Type II diabetes mellitus. Diabetologia 44(12):2220–6. doi: 10.1007/s001250100032 . Andrulionyte L, Peltola P, Chiasson JL et al. (2006) Single nucleotide polymorphisms of PPARD in combination with the Gly482Ser substitution of PGC-1A and the Pro12Ala substitution of PPARG2 predict the conversion from impaired glucose tolerance to type 2 diabetes: the STOP-NIDDM trial. Diabetes 55(7):2148–52. doi: 10.2337/db05-1629 . Yang N, MacArthur DG, Gulbin JP et al. (2003) ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet 73(3):627–31. doi: 10.1086/377590 . Epub 2003 Jul 23. North K. (2008) Why is alpha-actinin-3 deficiency so common in the general population? The evolution of athletic performance. Twin Res Hum Genet 11(4):384–94. doi: 10.1375/twin.11.4.384 . Vincent B, De Bock K, Ramaekers M et al. (2007) ACTN3 (R577X) genotype is associated with fiber type distribution. Physiol Genomics. 32(1):58–63. doi: 10.1152/physiolgenomics.00173.2007 . Epub 2007 Sep 11. Bernardez-Pereira S, Santos PC, Krieger JE et al. (2014) ACTN3 R577X polymorphism and long-term survival in patients with chronic heart failure. BMC Cardiovasc Disord 14:90. doi: 10.1186/1471-2261-14-90 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5674989","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":393414814,"identity":"34342927-5baf-46f0-8a92-acea893a3cb3","order_by":0,"name":"Guven 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University","correspondingAuthor":false,"prefix":"","firstName":"Hulya","middleName":"","lastName":"Arkan","suffix":""}],"badges":[],"createdAt":"2024-12-19 08:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5674989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5674989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72291968,"identity":"c161b31f-7def-4ffe-a6a2-4cd45d2a254b","added_by":"auto","created_at":"2024-12-24 17:30:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35277,"visible":true,"origin":"","legend":"\u003cp\u003eThe restriction enzyme patterns of ACE rs4646994 (a), PPRGC1A rs8192678 (b), and ACTN3 rs1815739 (c) polymorphisms. a) First lane is a 100-bp size marker, lane 1 is del/del (homozygous), lane 2 is ins/ins (homozygous), and lane 3 is ins/del (heterozygous). b) First lane is a 50-bp size marker, lane 1 is TT (homozygous), lane 2 is TC (heterozygous), and lane 3 is CC (homozygous). c) First lane is a 50-bp size marker, lane 1 is TT (homozygous), lane 2 is TC (heterozygous), and lane 3 is CC (homozygous).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5674989/v1/d1148357d3cd67afa5adcada.jpg"},{"id":72687636,"identity":"acff29d5-8842-4255-bc11-50a82df2482d","added_by":"auto","created_at":"2024-12-31 08:46:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1291149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5674989/v1/2bd0a3c2-cdb5-416e-b02d-6263d6bcbc8b.pdf"},{"id":72292451,"identity":"c45c1b16-4b49-4220-89b5-12a316674294","added_by":"auto","created_at":"2024-12-24 17:38:31","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":48419,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-5674989/v1/f6a06c750f1a0862507656a9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The distribution of sport performance gene variations through COVID-19 disease severity","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe coronavirus disease 2019 (COVID-19), induced by the severe acute respiratory syndrome virus (SARS-CoV-2), has unexpectedly shifted into a pandemic, with more than 776\u0026nbsp;million confirmed cases and above 7\u0026nbsp;million deaths worldwide as of September 22, 2024 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSARS-CoV-2 can cause severe and potentially life-threatening infections, particularly in elderly individuals with significant underlying health conditions. The most frequently reported comorbidities among COVID-19 patients are coronary artery disease (CAD) (6\u0026ndash;8%), diabetes mellitus (DM) (19%), and hypertension (HT) (27\u0026ndash;30%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition to these comorbidities, COVID-19 may manifest with a variety of clinical symptoms, including fever [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], fatigue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and severe complications such as sepsis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], pulmonary embolism (PE) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], bronchopneumonia (BP) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and acute respiratory distress syndrome (ARDS) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These complications can exacerbate the severity of the disease and contribute to the progression of critical illness. Therefore, identifying correlations between these predictive factors may help reveal additional risk indicators, aiding in the identification and management of high-risk populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModerate symptoms of COVID-19 may progress to fatal respiratory failure due to the development of ARDS. The renin-angiotensin-aldosterone system (RAAS) has been implicated in the pathophysiology of COVID-19 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, the roles of angiotensin-converting enzymes 1 and 2 (ACE and ACE2) in ARDS progression are well established. Alveolar epithelial cells express high levels of ACE and ACE2, which serve as key regulators of RAAS by maintaining immunological and pulmonary vascular homeostasis. SARS-CoV-2 enters alveolar epithelial cells via membrane-bound ACE2 receptors. Disruption of the ACE/ACE2 counter-regulation through this mechanism leads to endothelial dysfunction and triggers severe, detrimental immune responses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Common genetic variants in ACE genes have been associated with increased risk for HT, PE, renal failure, and cardiac diseases. Specifically, the 287 base pair ACE I/D polymorphism (rs4646994) is linked to elevated levels of ACE in the blood, an enzyme that counteracts the function of ACE2. The D/D genotype is reported to exhibit the highest ACE levels compared to the ID and II genotypes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to the antagonistic relationship between ACE and ACE2, increased ACE expression is associated with reduced ACE2 receptor expression, and this polymorphism has been connected to poor outcomes in both ARDS and SARS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consequently, the presence of the D allele may also influence the clinical course of COVID-19 by lowering ACE2 receptor levels.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlpha-Actinin-3 (ACTN3) is a key component of the skeletal muscle Z-disk, specifically in fast-twitch muscle fibers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and interacts with various structural, signaling, and metabolic proteins [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A C\u0026thinsp;\u0026gt;\u0026thinsp;T substitution at codon 577 of the ACTN3 gene is a common genetic mutation, leading to a stop codon (X) and the production of a truncated protein [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The ACTN3 R577X polymorphism (rs1815739) results in ACTN3 deficiency, which is associated with reduced rapid contractile capacity, lower bone mass or density, but enhanced endurance performance. This deficiency affects approximately 1.5\u0026nbsp;billion people worldwide, resulting in muscle weakness and a shift toward a more oxidative metabolism. In a comprehensive meta-analysis of the ACTN3 rs1815739 polymorphism, Alfred et al. demonstrated that the homozygous CC genotype is more common in Europeans, although it is not associated with athletic performance in the general population, whereas the homozygous TT genotype is frequently linked to improved aerobic fitness [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some studies suggest that ACTN3 polymorphisms may influence the inflammatory response, which plays a critical role in determining COVID-19 severity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Since fast-twitch fibers are also present in respiratory muscles, variations in ACTN3 could affect the strength and endurance of these muscles, potentially influencing respiratory efficiency and contributing to the severity of COVID-19 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe \u003cb\u003ePPARGC1A\u003c/b\u003e gene, located at the chromosomal position 4p15.2, plays key roles in fiber type determination, lipid metabolism, skeletal muscle fiber formation, and glucose regulation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. It encodes the transcriptional coactivator peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A), which belongs to the peroxisome proliferator-activated receptor family. PPARGC1A activates various transcription factors that regulate a wide range of biological processes. Several amino acid polymorphisms have been identified within the coding region of \u003cb\u003ePPARGC1A\u003c/b\u003e, including rs8192678 (Gly482Ser), which has been suggested to have functional significance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The \u003cb\u003ePPARGC1A\u003c/b\u003e rs8192678 polymorphism has been associated with high blood pressure, obesity, and DM [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, PPARGC1A plays a role in oxidative phosphorylation, which is essential for aerobic capacity and endurance [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As a result, polymorphisms in \u003cb\u003ePPARGC1A\u003c/b\u003e have been linked to cardiovascular health [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Given the frequent cardiac complications observed in severe COVID-19 cases, these polymorphisms may be correlated with disease outcomes. Furthermore, \u003cb\u003ePPARGC1A\u003c/b\u003e has been implicated in the regulation of inflammatory processes, mitochondrial efficiency, and energy production. Thus, variants in \u003cb\u003ePPARGC1A\u003c/b\u003e could impact overall physical endurance and the body's ability to mount an effective immune response to SARS-CoV-2, influencing disease severity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on this background, the present study is set to investigate the roles of the \u003cb\u003eACE\u003c/b\u003e rs4646994, \u003cb\u003eACTN3\u003c/b\u003e rs1815739, and \u003cb\u003ePPARGC1A\u003c/b\u003e rs8192678 polymorphisms in determining the severity of COVID-19 outcomes. By doing so, we aim to contribute to the growing body of evidence on host genetic factors shaping individual variability in disease outcomes. We hypothesize that specific genotypes of these genes may confer protective or risk-enhancing effects, potentially explaining some of the inter-individual variability observed in COVID-19 severity. This research not only seeks to elucidate the genetic factors influencing COVID-19 outcomes but also explores the potential application of these findings in athletic populations to predict susceptibility to infectious diseases such as COVID-19.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Samples\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA total of 422 COVID-19-positive samples were collected from the patients who were admitted to Biruni University Hospital between March and October 2021. The samples were divided into two main groups: COVID-19 positive asymptomatic control (outpatients with no symptoms and no need for hospitalization.) and COVID-19 positive symptomatic patients (inpatients or outpatients with symptoms). The symptomatic group was futher divided into two subclasses: mild (ymptoms requiring hospitalization without intensive care) and severe (cases requiring intensive care, mechanical ventilation, or resulting in death). Severity classification was based on WHO guidelines and confirmed by reviewing medical records\u003c/p\u003e\u003cp\u003eThe control group had 50 samples (age 31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6), the mild group had 300 samples (age 48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1), and the severe group had 77 samples (age 63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8). There was no gender bias (49.6% female). \u003cb\u003eExclusion criteria included\u003c/b\u003e teenagers and elderly patients (under 20 or over 65 years old), pregnant or breastfeeding patients, those with weakened immune systems (e.g., receiving chemotherapy), and patients unable to follow up due to various reasons. The demographic evaluation of age, gender, CAD, DM, HT, and smoking status is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All patients were vaccinated with \u003cb\u003ePfizer-BioNTech\u003c/b\u003e and followed up for eight months to monitor disease progression.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic evaluation of COVID-19 patients by the severity of COVID-19 disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl Group\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePatient Groups\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymp (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild (n\u0026thinsp;=\u0026thinsp;299)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;376)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e151 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193 (51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184 (49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195 (52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHT, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237 (63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e353 (94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCAD, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e330 (88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFever, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219 (58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFatigue, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157 (42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219 (58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSepsis, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e276 (73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eARDS, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e291 (77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePE, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e353 (94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e338 (90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOne of the symptom data of a participant is missing in the mild group. One of the disease or smoking history data of a participant is missing in the mild group. HT, Hypertension; DM, Diabetes Mellitus; CAD, Coronary Artery Disease; ARDS, Acute Respiratory Distress Syndrome; PE, Pulmonary Embolism; BP, Bronchopneumonia.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Genotyping\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFour milliliters of peripheral blood were drawn from each participant, and DNA was isolated using the \u003cb\u003ePromega Wizard Genomic DNA Purification Kit.\u003c/b\u003e The concentration and optical density (OD) of DNA were measured using a \u003cb\u003eNanoDrop spectrophotometer\u003c/b\u003e (NanoPhotometer P300, Implen GmbH, Germany). Samples with concentrations between \u003cb\u003e40\u0026ndash;60 ng/\u0026micro;l\u003c/b\u003e and OD values of \u003cb\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/b\u003e were included​.\u003c/p\u003e \u003cp\u003ePolymerase Chain Reaction (PCR) was performed for each polymorphism according to the manufacturer's instructions (PCR Master Mix (2X), Catalog # K0172, Massachusetts, USA). First, the DNA was denaturated at 94\u0026deg;C for 2 minutes. Then, it went through 35 cycles of thermal cycling, which included denaturation at 94\u0026deg;C for 30 seconds, primer annealing at 59\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 30 seconds. The protocol concluded with a final extension step at 72\u0026deg;C for 3 minutes. The primers were sense-CTGTAAGCCACTGCTGGAGA and antisense-AAATGAAGGGACCCAAGTG for ACE rs4646994, sense-GTGTGGCTGGTACACTCTGTG antisense-CTGTCTCGGGCTCATCTGTA for ACTN3 rs1815739, and sense-TGCTACCTGAGAGAGACTTTGG and antisense-TGGAATATGGTGATCGGGAACA for PPARGC1A rs8192678.\u003c/p\u003e \u003cp\u003ePCR products were electrophoresed at 120 V on a 2% agarose gel for 20 minutes using a Bio-Rad electrophoresis system (Sub-Cell Model 192 Cell, Biorad, USA), and the PCR fragments were monitored through a screening system (ChemiDoc MP, Biorad, USA). The PCR product of ACE rs4646994 has two alleles: 371 bp (the deletion allele) or 660 bp (the insert allele). The del/del genotype is observed as a single band with 371 bp in length, whereas the heterozygous del/ins genotype is observed as two distinct bands with 371 bp and 660 bp in length, and the ins/ins genotype is observed as a single band with 660 bp in length (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe restriction fragment length polymorphism (RFLP)-PCR was used to find out the genotypes of the PPRGC1A rs8192678 and ACTN3 rs1815739 polymorphisms.One unit of Mspl restriction enzyme (Catalog #R0106S, New England Biolabs, Ipswich, USA) was used to digest the PPRGC1A PCR product. The PCR product-enzyme mixture was incubated at 37\u0026deg;C for 15 min. The restriction-digested PCR product was electrophoresed at 100 V on a 2% agarose gel for 30 minutes. Homozygous rs8192678 CC genotype was observed as two bands with 149-bp and 311-bp in lengths; heterozygous rs8192678 C/T genotype was observed as three distinct bands with 149-bp, 311-bp, and 460-bp in lengths; and homozygous rs8192678 TT genotype was observed as a single band with 460-bp in length (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eOne unit of Ddel restriction enzyme (Catalog #R0175L, New England Biolabs, Ipswich, USA) was used to digest the ACTN3 PCR product. The PCR product and restriction enzyme mixture were incubated at 37\u0026deg;C for 15 minutes, followed by thermal inactivation of the restriction enzyme at 65\u0026deg;C for 15 minutes. The restriction-digested PCR product was electrophoresed on a 2% agarose gel at 100 V for 10 minutes. The homozygous rs1815739 CC genotype was observed as a single band with 412-bp, the heterozygous rs1815739 C/T genotype was observed as two distinct bands with 207-bp and 412-bp in length, and the homozygous rs1815739 TT genotype was observed as a single band with 207-bp in length (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStatistical analysis was conducted using GraphPad Prism 5.0 software (GraphPad Software, San Diego, CA, USA) and Minitab 18 Statistical Software (Minitab Inc.). The power of the study was calculated as 80%. Age was displayed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error, demographic and symptomatic data were displayed as counts and percentages, and genotypes and allele frequencies were displayed as counts. Mann Whitney The U test was used for the comparison of ages between the two groups. The Hardy-Weinberg equilibrium was determined for compatibility among groups using chi-square tests (χ\u0026sup2;). The comparison of frequencies and ratios between groups was evaluated using chi-square tests or Fisher's exact test. The associations between rs4646994, rs8192678, and rs1815739 genotypes, alleles, and COVID-19 were assessed using chi-square tests to calculate odds ratios (ORs), with homozygous dominant genotypes as the reference category for each polymorphism. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Demographic evaluation of the patients\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 377 COVID-19 patients were included in the study, comprising 300 mild cases with at least one symptom, 77 severe cases requiring intensive care, and 50 asymptomatic controls. The male percentage was 44% in the asymptomatic group, 50% in the mild group, and 55% in the severe group, with no significant gender distribution differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the mean age significantly increased with disease severity across all groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Data not shown).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The genotype analysis of ACE1 rs4646994, PPARGC1A rs8192678, and ACTN3 rs1815739 polymorphisms\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe effects of ACE1 rs4646994, PPARGC1A rs8192678, and ACTN3 rs1815739 polymorphisms on COVID-19 severity were evaluated. All polymorphisms were in Hardy-Weinberg equilibrium (HWE) (data not shown). ACE1 rs4646994 was found to have a protective effect, especially in female patients, where the DD genotype showed a 3.095-fold protective effect compared to II\u0026thinsp;+\u0026thinsp;ID (p\u0026thinsp;=\u0026thinsp;0.01)​.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The genotype analysis of the ACE1 rs4646994 polymorphism\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA significant protective effect was observed for the ACE1 rs4646994 DD genotype in the severe group compared to the mild group, with a 1.912 times protective role (p\u0026thinsp;=\u0026thinsp;0.0185) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In females, the protective role of the DD genotype was even higher, with a 3.095-fold protective effect (p\u0026thinsp;=\u0026thinsp;0.01). Additionally, the ID genotype was associated with a 1.937 times increased risk of severe disease in females (p\u0026thinsp;=\u0026thinsp;0.0112)​ (Supplementary Files Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution of the ACE1 rs4646994 polymorphism and the disease severity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE1 rs4646994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl Group\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePatient Groups\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value (OR)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAsymp (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMild (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSevere (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOverall (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAsymp vs Patient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAsymp vs Mild\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eAsymp vs Severe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eMild vs Severe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6517 (0.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6386 (0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7541 (0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8901 (1.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7797 (1.135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5980 (1.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4947 (0.690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0988 (0.542)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant Model (II\u0026thinsp;+\u0026thinsp;ID vs DD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3906 (1.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2157 (1.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5170 (0.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0185\u003c/b\u003e (0.523)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive Model (II vs ID\u0026thinsp;+\u0026thinsp;DD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8991 (0.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000 (1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6188 (0.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4652 (0.787)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5883 (1.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3709 (1.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4844 (0.834)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.0377\u003c/b\u003e (0.685)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eRef, reference; OR, odds ratio; a chi-square test was used for analysis. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 values were shown in bold\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4. The genotype analysis of the PPARGC1A rs8192678 polymorphism\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe PPARGC1A rs8192678 TC genotype was found to significantly increase the risk of COVID-19 disease by 4.122-fold in all patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with similar increases observed in both the mild and severe groups. The T allele was found to increase disease risk only in the mild group by 1.565-fold (p\u0026thinsp;=\u0026thinsp;0.0456). In the recessive model, TC\u0026thinsp;+\u0026thinsp;TT increased the COVID-19 disease risk by 3.054-fold in all COVID-19 patients, by 3.167-fold in the mild group, and by 2.667-fold in the severe group (p\u0026thinsp;=\u0026thinsp;0.0002, p\u0026thinsp;=\u0026thinsp;0.0001, and p\u0026thinsp;=\u0026thinsp;0.0092, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution of the PPARGC1A rs8192678 polymorphism and the disease severity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePPARGC1A \u003cem\u003ers8192678\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value (OR)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymp\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverallPatients\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymp vs Overall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAsymp vs\u003c/p\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAsymp vs Severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003evs\u003c/p\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eRef\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e (4.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003cp\u003e(4.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0013\u003c/b\u003e (3.730)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6684 (0.881)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3634 (1.452)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2854 (1.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8995 (1.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3911 (0.686)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant Model\u003c/p\u003e \u003cp\u003e(CC\u0026thinsp;+\u0026thinsp;TCvs TT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2907 (0.669)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3684 (0.706)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1995 (0.529)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4593 (0.750)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive Model\u003c/p\u003e \u003cp\u003e(CC vs TC\u0026thinsp;+\u0026thinsp;TT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e (3.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e (3.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0092\u003c/b\u003e (2.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5523 (0.842)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0626 (1.509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0456\u003c/b\u003e (1.565)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2507 (1.356)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4336 (0.867)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eRef, reference; OR, odds ratio; Chi-square test was used for analysis. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 values were shown in bold\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs the polymorphism was analyzed in terms of gender, carrying the TC genotype increased the risk of COVID-19 disease by 4.625-fold in women (p\u0026thinsp;=\u0026thinsp;0.0006) and by 3.542-fold in men (p\u0026thinsp;=\u0026thinsp;0.011) of overall COVID-19 patients, by 4.74-fold in women (p\u0026thinsp;=\u0026thinsp;0.0006) and by 3.651-fold (p\u0026thinsp;=\u0026thinsp;0.0106) in men of the mild group, 4.167-fold (p\u0026thinsp;=\u0026thinsp;0.0141) in women, and 3.214-fold (p\u0026thinsp;=\u0026thinsp;0.0481) in men of the severe group. In the recessive model, in women, TC\u0026thinsp;+\u0026thinsp;TT increased the risk of COVID-19 disease by 3.564-fold in overall COVID-19 patients, by 3.167-fold in the mild group, and by 3.333-fold in the severe group (p\u0026thinsp;=\u0026thinsp;0.0014, p\u0026thinsp;=\u0026thinsp;0.0016, and p\u0026thinsp;=\u0026thinsp;0.0237). In men, however, TC\u0026thinsp;+\u0026thinsp;CC increased the risk of disease by 2.517-fold in overall COVID-19 patients and by 2.662-fold in the mild group (p\u0026thinsp;=\u0026thinsp;0.0393 and p\u0026thinsp;=\u0026thinsp;0.0321, respectively) (Supplementary Files Table S3 and Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5. The genotype analysis of the ACTN3 rs1815739 polymorphism\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor ACTN3 rs1815739, the TT genotype was associated with an 8.389-fold reduction in COVID-19 disease risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared to the CC\u0026thinsp;+\u0026thinsp;TC genotypes. This protective effect was even more pronounced in the mild group, with a 16.15-fold reduction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In the gender-based analysis, the TT genotype was associated with a 9.308-fold reduction in disease risk in women and a 7.537-fold reduction in men (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)​\u003c/p\u003e \u003cp\u003eWhen the effect of the T allele compared to the C allele on both overall COVID-19 patients and disease severity was examined, the T allele was associated with a 4.514-fold reduction of COVID-19 disease risk overall in COVID-19 patients and a 5.819-fold reduction of the risk in the mild group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution of the ACTN3 rs1815739 polymorphism and the disease severity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eACTN3 \u003cem\u003ers1815739\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value (OR)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymp\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall Patients\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymp vs Patient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAsymp vs\u003c/p\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAsymp vs Severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003evs Severe\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (2.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (1.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (0.888)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3322\u003csup\u003eb\u003c/sup\u003e (0.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1537\u003csup\u003eb\u003c/sup\u003e (0.125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0572\u003csup\u003eb\u003c/sup\u003e (11.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDominant Model\u003c/p\u003e \u003cp\u003e(CC\u0026thinsp;+\u0026thinsp;TCvs TT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e (0.119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e (0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7288\u003csup\u003ea\u003c/sup\u003e (0.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e (13.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRecessive Model\u003c/p\u003e \u003cp\u003eCC vs TC\u0026thinsp;+\u0026thinsp;TT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (0.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (0.532)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5878\u003csup\u003eb\u003c/sup\u003e (2.885)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e (0.222)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e (0.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7469\u003csup\u003ea\u003c/sup\u003e (0.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e (5.110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eRef, reference; OR, odds ratio \u003csup\u003ea\u003c/sup\u003e Chi-square test or \u003csup\u003eb\u003c/sup\u003e Fisher's exact test were used for analysis. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 values were shown in bold.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs women and men were evaluated separately, the T allele was associated with a 4.828-fold and 4.195-fold reduction in the COVID-19 disease risk in overall COVID-19 patients (p\u0026thinsp;=\u0026thinsp;0.0001 and p\u0026thinsp;=\u0026thinsp;0.0015) and a 6.105-fold and 5.508-fold reduction of the COVID-19 disease risk in the mild group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and p\u0026thinsp;=\u0026thinsp;0.0001). Besides, compared to the mild group, TT was associated with a 13.91-fold increase in the COVID-19 disease risk in the severe group compared to CC\u0026thinsp;+\u0026thinsp;TC and a 5.11-fold increase in the COVID-19 disease risk in the severe group compared to the C allele (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Furthermore, compared to CC\u0026thinsp;+\u0026thinsp;TC, the TT genotype was associated with a 13.67-fold and 14.06-fold increase in the COVID-19 disease risk in women and men in the severe group, respectively. The T allele was also associated with a 4.965-fold and 5.226-fold increase in the COVID-19 disease risk in women and men of the severe group, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Supplementary File Table S5 and Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Combined genotype analysis of ACE1 rs4646994, PPARGC1A rs8192678, and ACTN3 rs1815739 polymorphisms\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe combined analysis of ACE1 rs4646994 and PPARGC1A rs8192678 polymorphisms revealed significant interactions. For instance, the ins/ins\u0026thinsp;+\u0026thinsp;TC genotype was associated with a 7.6-fold increased risk of COVID-19 disease (p\u0026thinsp;=\u0026thinsp;0.016) and a similar risk-8.25-fold increase in the severe group (p\u0026thinsp;=\u0026thinsp;0.0393) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). No statistically significant differences were found when comparing the combined genotypes of ACE1 rs4646994 and ACTN3 rs1815739 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05)​ (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe combined genotype analysis of ACE1 rs4646994 and PPARGC1A rs8192678 polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eACE1 \u003cem\u003ers4646994\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e+\u003c/em\u003e\u003c/p\u003e \u003cp\u003ePPARGC1A \u003cem\u003ers8192678\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value (OR)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymp\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall Patients\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymp vs Patient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAsymp vs Mild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAsymp vs Severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMild vs Severe\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0160\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (7.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0380\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (7.364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0393\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (8.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (1.120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1411\u003csup\u003eb\u003c/sup\u003e (8.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0570\u003csup\u003eb\u003c/sup\u003e (11.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1245\u003csup\u003eb\u003c/sup\u003e (0.122)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7529\u003csup\u003ea\u003c/sup\u003e (1.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8378\u003csup\u003ea\u003c/sup\u003e (1.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7233\u003csup\u003eb\u003c/sup\u003e (1.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (1.222)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0104\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (5.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0102\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (5.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0645\u003csup\u003eb\u003c/sup\u003e (4.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7449\u003csup\u003eb\u003c/sup\u003e (0.791)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4764\u003csup\u003eb\u003c/sup\u003e (2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4629\u003csup\u003eb\u003c/sup\u003e (2.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6563\u003csup\u003eb\u003c/sup\u003e (1.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (0.917)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1749\u003csup\u003eb\u003c/sup\u003e (2.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1522\u003csup\u003eb\u003c/sup\u003e (3.091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (1.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2436\u003csup\u003eb\u003c/sup\u003e (0.404)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0047\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (7.120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0031\u003c/b\u003e\u003csup\u003eb\u003c/sup\u003e (8.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1245\u003csup\u003eb\u003c/sup\u003e (3.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2628\u003csup\u003eb\u003c/sup\u003e (0.470)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2906\u003csup\u003eb\u003c/sup\u003e (2.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2697\u003csup\u003eb\u003c/sup\u003e (2.727)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0000\u003csup\u003eb\u003c/sup\u003e (1.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6857\u003csup\u003eb\u003c/sup\u003e (0.550)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eOR, odds ratio; Ref, reference; \u003csup\u003ea\u003c/sup\u003e Chi-square test or \u003csup\u003eb\u003c/sup\u003e Fisher's exact test were used for analysis. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 values were shown in bold.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe combined genotype analysis of ACE1 rs4646994 and ACTN3 rs1815739 polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eACE1 \u003cem\u003ers4646994\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e+\u003c/em\u003e\u003c/p\u003e \u003cp\u003eACTN3 \u003cem\u003ers1815739\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eControl\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePatients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value (OR)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsymp\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall Patients\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymp vs Overall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAsymp vs Mild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMild vs Severe\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0000 (0.106)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000 (0.765)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4286 (0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3000 (7.909)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000 (6.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000 (5.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0000 (0.411)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000 (0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000 (0.382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4706 (3.511)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u0026thinsp;+\u0026thinsp;TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000 (5.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000 (5.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0000 (0.163)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000 (1.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0000 (0.768)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0000 (1.981)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eOR, odds ratio; Ref, reference; Fisher's exact test was used for analysis. Since there was more than one 0 (zero) in the reference value, the asymptomatic control and severe groups could not be compared. In addition, ID\u0026thinsp;+\u0026thinsp;CC and DD\u0026thinsp;+\u0026thinsp;CC could not be compared due to the insufficient number of samples and were removed from the table.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe WHO database indicates that the susceptibility and severity of COVID-19 vary globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. COVID-19 infection can lead to various symptoms and complications that can negatively impact athletic activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, some sports performance gene variations may also determine the severity of SARS-CoV-2 infection.\u003c/p\u003e\u003cp\u003eSome researchers have hypothesized that regional differences in gene frequencies may explain these variations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, variations in the expression and function of immune response genes may underlie individual susceptibility to a disease, the risk of hospitalization, and the likelihood of adverse events.Inherited and environmental factors that alter the expression and function of RAAS components, such as ACE, could explain the risk of developing COVID-19 and its adverse outcomes. Common variants in the two ACE genes have been linked to symptoms observed in COVID-19. In particular, the ACE rs4646994 polymorphism has been extensively studied due to its role in cardiovascular and pulmonary conditions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The D allele of this polymorphism is associated with higher ACE expression, leading to reduced ACE2 receptor availability, the primary entry point for SARS-CoV-2​ [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In accordance with these findings, in the present study, DD was found to have a 1.912 times more protective role compared to II\u0026thinsp;+\u0026thinsp;ID, and the D allele alone was found to have a protective role compared to the I allele. These findings support the hypothesis that ACE polymorphisms can modulate disease severity by affecting lung function and immune response during infection​. However, some studies suggest that the D/D genotype might increase susceptibility to ARDS, a common complication in severe COVID-19 cases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]​. Therefore, it is hypothesized that having the D allele for the ACE I/D polymorphism may worsen the clinical course of COVID-19 by decreasing ACE2 receptor levels [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This suggests that individuals with the ACE DD genotype could benefit from targeted therapies aimed at modulating the RAS pathway, such as ACE inhibitors or angiotensin receptor blockers.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe PPARGC1A gene encodes a protein that regulates key genes involved in glucose and fatty acid metabolism [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The PPARGC1A rs8192678 polymorphism is also implicated in metabolic regulation, and polymorphisms in this gene have been linked to diabetes mellitus and hypertension, both risk factors for severe COVID-19 [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Physiological evidence suggests that this polymorphism affects blood lipid levels and insulin sensitivity, as the Ser allele carriers exhibit higher levels of insulin resistance and lipid dysregulation, which can lead to worse outcomes in patients already suffering from metabolic comorbidities [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. As a consequence, these individuals have an elevated risk of type 2 DM [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Given that DM itself is a risk factor for severe COVID-19, the PPARGC1A rs8192678 polymorphism may influence COVID-19 pathophysiology. According to our results, the TC genotype was found to be a risk factor for all groups compared to the asymptomatic control group, and the T allele alone was found to be a risk factor compared to the C allele. This suggests that genetic variations in energy metabolism and oxidative phosphorylation, crucial during immune response, may determine how individuals respond to SARS-CoV-2 infection. Moreover, understanding this genotype's role could pave the way for interventions like antioxidant therapies or mitochondrial enhancers to help reduce the severity of COVID-19 in affected individuals.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe ACTN3 R577X polymorphism, which affects about 1.5\u0026nbsp;billion people worldwide, results in a deficiency of the ACTN3 protein, a key component of fast-twitch muscle fibers. This deficiency leads to decreased muscle strength, improved endurance performance, and reduced bone mass. ACTN3 interacts with a variety of proteins involved in muscle structure, metabolism, and signaling, suggesting that its absence has a broad impact on muscle function [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The X allele of the ACTN3 gene, which is associated with less muscle mass, lower strength, and higher VO2 max, is more common in endurance athletes with a type I muscle fiber predominance. This suggests that the ACTN3 X allele may confer an advantage for endurance performance [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The XX genotype was also related to elevated cardiovascular fitness [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], which may explain the low VO2 max and high prevalence of CAD and HT in severe COVID-19 patients. As this genotype results in reduced alpha-actinin-3, potentially impairing respiratory efficiency, which could worsen outcomes in patients with respiratory infections like COVID-19. This genetic variation might be especially relevant for patients with pre-existing respiratory conditions or reduced muscle strength. The ACTN3 R577X polymorphism is a common nonsense mutation that results in the complete absence of ACTN3 in an estimated 16% of the global population [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Individuals with the RR genotype of the ACTN3 gene, which is associated with a higher proportion of type II muscle fibers and greater muscle strength, tend to perform better in strength and speed-power tests [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Interestingly, carriers of the ACTN3 X allele have a 1.72-fold higher risk of death than those with the ACTN3 577RR genotype in patients with congestive heart failure, suggesting that the ACTN3 genotype may be a prognostic marker for this condition [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This may be relevant to severe COVID-19 disease, as congestive heart failure can impair lung physiology. The findings of the current study, by contrast, do not support the previous research. We found that carrying the ACTN3 577XX genotype or T allele alone may decrease the severity of the COVID-19 disease.The ACTN3 TT genotype appears to offer a protective effect, possibly by supporting better muscle function and reducing the likelihood of respiratory complications during severe illness. This subgroup may inherently face a lower risk of severe outcomes, providing valuable insights for resource allocation and tailored clinical management.\u003c/p\u003e\u003cp\u003eIn terms of combined genetic effects, polymorphisms in ACE, ACTN3, and PPARGC1A likely interact to influence COVID-19 outcomes. The risk of COVID-19 disease was found to elevate in the presence of ACE1 rs4646994ins/ins\u0026thinsp;+\u0026thinsp;PPARGC1A rs8192678TC, ACE1 rs4646994ins/del\u0026thinsp;+\u0026thinsp;PPARGC1A rs8192678TC, and ACE1 rs4646994del/del\u0026thinsp;+\u0026thinsp;PPARGC1A rs8192678TC genotypes, but no statistically significant differences were found when ACE1 rs4646994-ins/ins and ACTN3 rs1815739 CC combined genotypes compared to other genotypes. This suggests that genetic predispositions affecting muscle function, cardiovascular health, and metabolism collectively impact an individual\u0026rsquo;s risk of severe COVID-19. Such findings emphasize the need to further investigate how genetic factors interact to influence COVID-19 severity, with the potential to inform predictive models and guide clinical interventions​.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":" \u003cp\u003eTo the best of our knowledge, this is the first study investigating the effect of ACTN3 and PPARGC1A polymorphisms on COVID-19 disease severity. A key limitation of case-control association studies is their small sample size, which can obscure true associations and lead to conflicting findings. Thus, the present findings in the Turkish population may require replication with larger sample sizes across multiple populations. Also, we did not follow the asymptomatic-control study subjects after sample retrieval to determine if they developed disease symptoms. At the individual patient level, risk stratification based on these genetic variants could guide more targeted clinical care, leading to better patient outcomes and reduced mortality and morbidity​. Future research should continue to explore how these polymorphisms can be used as biomarkers for COVID-19 outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eFinancial support was received from the Scientific and Technological Research Council of Turkey (TUBITAK) (Fon number: 1919B012103061) and Biruni University Scientific Research Projects (Project Number: Biruni-BAP-2022-01-03).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: G.Y., Me.Do.; Sample Collection: Me.Do., S.K., Ma.De.; Methodology: I.K., S.B.Y., E.A., H.A.; Data interpretation and Statistical analysis H.A.; Writing - first draft preparation: G.Y., Me.Do, E.A., I.K.; Writing \u0026ndash; review, editing, and approval the final version of the manuscript: G.Y., Me.Do., S.K., Ma.De., I.K., E.A., S.B.Y., H.A.; Supervision: G.Y., Me.Do.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Biruni University Rector Prof. Dr. Adnan Yuksel M.D. and Biruni University BAMER president Prof. Dr. I. Tuncer Degim PhD. for their support of this scientific research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e Data is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organisation. Available online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.who.int/dashboards/covid19/cases?n=c\u003c/span\u003e\u003cspan address=\"https://data.who.int/dashboards/covid19/cases?n=c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed November 15, 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallo G, Calvez V, Savoia C. (2022) Hypertension and COVID-19: Current Evidence and Perspectives. 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BMC Cardiovasc Disord 14:90. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2261-14-90\u003c/span\u003e\u003cspan address=\"10.1186/1471-2261-14-90\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, sports performance, ACTN3, ACE, PPARGC1A, intensive care unit, disease severity","lastPublishedDoi":"10.21203/rs.3.rs-5674989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5674989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSince its emergence in 2020, researchers worldwide have been collaborating to better understand the SARS-CoV-2 disease\u0026rsquo;s pathophysiology. Disease severity can vary based on several factors, including comorbidities and genetic variations. Notably, recent studies have highlighted the role of genes associated with athletic performance, such as ACE, ACTN3, and PPARGC1A, in influencing muscle function, cardiovascular health, and the body's metabolic response. Given that these genes also impact oxidative metabolism, inflammation, and respiratory efficiency, we hypothesized that they might play a critical role in the host\u0026rsquo;s response to SARS-CoV-2 infection.\u003c/p\u003e\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the association between disease severity and genetic polymorphisms in these sports performance-related genes, specifically ACE rs4646994, ACTN3 rs1815739, and PPARGC1A rs8192678.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 422 COVID-19-positive patients were included in the study. The participants were divided into three groups: a severe group (77 patients) requiring intensive care unit (ICU) admission, a mild group (300 patients) exhibiting at least one symptom, and an asymptomatic control group. Genotyping was performed using restriction fragment length polymorphism PCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe D allele and DD genotype of ACE and the T allele and TT genotype of ACTN3 were found to confer protective effects against severe SARS-CoV-2 infection. Conversely, the PPARGC1A TC genotype and the ACE-PPARGC1A ins/ins\u0026thinsp;+\u0026thinsp;TC combined genotype were associated with increased disease severity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough vaccination has reduced the severity of SARS-CoV-2, the virus continues to impact human health. Interindividual differences due to these genetic variations will broaden the horizon of knowledge on the pathophysiology of the disease.\u003c/p\u003e","manuscriptTitle":"The distribution of sport performance gene variations through COVID-19 disease severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 17:30:26","doi":"10.21203/rs.3.rs-5674989/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c116398b-c100-4c2b-b27f-c836d38c9528","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-31T08:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 17:30:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5674989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5674989","identity":"rs-5674989","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}