Diagnostic value of the gene expression of Growth Differentiation Factor 15 and Telomerase Reverse Transcriptase in middle-aged patients with acute coronary artery disease: a pilot case-control study 

Diagnostic value of the gene expression of Growth Differentiation Factor 15 and Telomerase Reverse Transcriptase in middle-aged patients with acute coronary artery disease: a pilot case-control study | 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 Diagnostic value of the gene expression of Growth Differentiation Factor 15 and Telomerase Reverse Transcriptase in middle-aged patients with acute coronary artery disease: a pilot case-control study MA Abdelsabour, NK Idriss, AD Blann, AA Mosa, DA Fouad, AM Amal, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5129243/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 : Differential diagnosis of the various manifestations of ischaemic heart disease can be difficult, especially in the young, with many investigations being relevant. We hypothesised that expression of the genes for Growth Differentiation Factor 15 ( GDF15 ) and Telomerase Reverse Transcriptase ( TERT ) have a place in the diagnosis of an acute coronary artery disease event in those aged up to 55 years with existing coronary artery disease. Venous blood was obtained from 53 patients (27 with diabetes) presenting with an acute coronary syndrome and subsequently shown to have coronary artery disease, and from 46 age and sex matched controls free of cardiovascular disease and its risk factors. Relative expression of leukocyte transcriptome GAPDH, GDF15 and TERT were determined by real-time polymerase chain reaction and quantified by quantitation-comparative Ct (ΔCt). Results: Compared to controls, mean (95% CI) relative expression of GDF15 mRNA in the patients was 1.38 (1.13-1.49) (p<0.001), and of TERT was 1.12 (1.04-1.20) p=0.003), with GDF15 being greater than that of TERT (p<0.001). There was no difference in relative GDF15 expression in 26 patients free of diabetes (1.6 [1.42-1.78]) versus the 27 patients with diabetes (1.6 [1.29-1.91]) (p=0.996), and no difference in relative TERT expression in patients free of diabetes (1.19 [1.06-1.33]) compared to those with diabetes (1.25 [0.98-1.50]) (p=0.739). Conclusions : Compared to healthy controls, GDF15 and TERT expressions are both increased in coronary artery disease and in coronary artery disease+diabetes, with no difference between the patient groups. These genes may have roles in the diagnosis and pathogenesis of acute coronary artery disease. Background The pathogenesis of coronary artery disease has long been linked to the four major risk factors of diabetes, hypertension, hypercholesterolaemia and smoking, but numerous genetic influences are emerging, with over 300 loci and over 1500 candidate genes implicated ( 1 , 2 ). Growth differentiation factor 15 (GDF-15), also known as macrophage inhibitory cytokine-1, is a member of the transforming growth factor-β superfamily and has functions likely to have a role in numerous processes such as angiogenesis and inflammation ( 3 ). Coded for by GDF15 at 19p13.11, increased serum levels have been reported in many cardiovascular diseases such as atrial fibrillation and heart failure and are linked to increased all-cause death and cardiovascular death in coronary artery disease and with death following an ischaemic stroke ( 4 , 5 ). Several studies have indicated that levels of circulating GDF-15 rise with age and are directly, perhaps causally, linked to the aetiology of these cardiovascular diseases ( 6 , 7 ). Data from the Framingham study points to effects of risk factors as well as genetics on plasma concentrations of GDF-15 ( 8 ), and although variants of GDF15 are linked to ischaemic stroke in a Chinese population ( 9 ), this has been countered in a meta-analysis ( 10 ). Telomere length has long been associated with a variety of diseases, including atherogenesis and CAD ( 11 – 13 ). Telomerase is a ribonucleoprotein enzyme with two catalytically essential subunits, the telomerase RNA, and telomerase reverse transcriptase protein (TERT) that regulates telomere length ( 14 , 15 ). As this enzyme effectively reduces telomere length, several commentators have suggested it may be a new therapeutic target ( 16 , 17 ). Over-expression of human TERT (h-TERT), coded for by TERT at 5p15.33, may be important in cancer ( 18 ), whilst variants of TERT may be linked to outcome in stable CAD ( 19 ) and risk of ischaemic stroke ( 20 ). Thus, the literature points to potential roles for both GDF15 and TERT and their protein products in coronary artery disease, and our literature search failed to find any report of both genes or molecules analysed together. However, the great majority of clinical data focuses on late-middle aged and elderly patients, with little data on the young middle-aged, a leading factor in why we feel the current investigation is necessary. In order to fill this gap in the knowledge, we therefore hypothesised altered expression of both GDF15 and TERT in relatively young patients presenting to hospital with an acute coronary syndrome subsequently shown to be a myocardial infarction. Methods We tested our hypothesis in consecutive patients with existing cardiovascular disease (myocardial infarction, stroke, arterial surgery) aged up to 55 years old presenting to the Cardiac Catheter Unit, Cardiology Department, Assuit University Hospital, Egypt, in an acute coronary syndrome subsequently shown by angiography to have CAD precipitating a myocardial infarction. Data from patients was controlled by samples from healthy hospital and laboratory staff free of cardiovascular disease or its risk factors. Exclusion criteria were age > 55 years, psychological disorders, cancer, autoimmune diseases, acute or chronic inflammatory diseases, and diabetic patients treated with an inhibitor of dipeptidyl peptidase. The purpose and method of the study were explained to each participant and informed consent was obtained. A predesigned case record form was used to record age and clinical data of the consenting subjects. This study was approved by the ethical committee of the Faculty of Medicine, Assiut University (IRB no. 17200057). This study is not a clinical trial, so there is no applicable number. Venous blood was collected from each patient on presentation, and from each control. Serum urea, creatinine, creatine kinase, creatine kinase MB, troponin-T (patients only), HbA1c, total cholesterol, triglycerides, and high-density lipoprotein were performed by the routine hospital clinical chemistry service (which subscribes to the national quality control service) by established methods. Low-density lipoprotein was calculated according to the Freidwald formula. Total RNA was extracted from EDTA-anticoagulated whole blood using Direct-zol™ RNA Miniprep Plus (Zymo Research, Irvine, CA, USA), followed by a SensiFAST™ SYBR® Hi-ROX One-Step Kit for cDNA by RT-PCR (Meridian Bioscience, Memphis, TN, USA). The reactions were performed in a thermal cycler (MJ Research, Inc., Waltham, MA, USA) and the reaction products stored at -20°C until analysed. The thermal profile consisted of 20 minutes at 46°C for reverse transcription then at 95°C for 1 minute for reverse transcriptase inactivation. Gene expression was standardized to that of GAPDH as a control and is presented as fold change. We applied 1 µM of each forward and reverse primer specific for each target gene: TERT , 5′-GGAGCAAGTTGCAAAGCATTG-3′ 5′-TCCCACGACGTAGTCCATGTT-3′. GDF15 , 5′-TCAAGGTCGTGGGACGTGACA-3′ 5′-GCCGTGCGGACGAAGATTCT-3′ and GAPDH , 5′-AGCCACATCGCTCAGACAC-3′and 5′-GCCCAATACGACCAAATCC-3′. The products were quantified by SYBR green dye analysis. The mean expression levels of GDF15 and TERT mRNA relative to those of GAPDH were analysed by the quantitation-comparative Ct (ΔΔCt) method on a fluorescence quantitative PCR analyser (ABI 7500, Applied Biosystems, Foster City, CA, USA). We hypothesised a difference in relative expression of GDF15 or TERT of at least 12.5% compared to expression in the controls. To achieve this at alpha 80% calls for 40 per group, and for additional assurance we recruited in excess of this requirement. A sample size of 80 provides the power to defend a Pearson correlation coefficient of > 0.31. In post-hoc analyses we noted the large number of diabetics and considered their analysis in relation to non-diabetics. A sample size of 25 per group provides the power to robust defend a difference of 16% in a test statistic compared to a control group, and a sample size of 50 provides the power to defend a Pearson correlation coefficient of > 0.39. Categorical data were analysed by the chi-square test, continuously variable data by student’s t test or the Mann-Whitney U test and correlated by Pearson’s method. All analyses were performed on Minitab release 21. Results Clinical, demographic, and laboratory data of the controls and patients are shown in Table 1 . The two groups were matched for age and sex. There was no difference in levels of serum urea between the groups, but (as partially expected) creatinine, HbA1c, all cardiology metrics, and all lipid metrics were increased in the patients. There was no difference in the expression of GAPDH between the groups, but the expression of GDF15 and TERT were both increased. Compared to expression in controls, mean (95% confidence interval) relative expression of GDF15 in the patients was 1.38 (1.13–1.49) (p < 0.001), and of TERT was 1.12 (1.04–1.20) (p = 0.003), with relative expression of GDF15 being greater than that of TERT (p < 0.001). Table 1 Clinical, demographic and laboratory data on Cases and Controls Controls (n = 46) Cases (n = 53) P value Age (years) 40.3 (11.1) 42.9 (9.6) 0.205 Sex (m/f)(n) 31/15 41/12 0.267 Urea (mg/dL) 4.2 (1.8) 4.2 (1.4) 0.835 Creatinine (mg/dL) 0.88 (0.27) 1.15 (0.46) < 0.001 CK (IU/L) 39 (13) 138 (46) < 0.001 CK-MB (ng/mL) 0.8 (0.3) 7.5 (4.0) < 0.001 Cholesterol (mg/dL) 135 (12) 211 (59) < 0.001 Triglycerides (mg/dL) 93 (17) 111 (33) 0.001 HDL (mg/dL) 37 (7) 42 (10) 0.003 LDL (mg/dL) 79 (16) 147 (58) < 0.001 HbA1c (%) 4.7 (0.4) 8.1 (3.6) < 0.001 GADPH expression (units) 17.7 (5.5) 18.6 (5.7) 0.403 GDF15 expression (units) 19.9 (5.7) 26.8 (3.6) < 0.001 TERT expression (units) 17.7 (5.4) 21.3 (6.3) 0.003 Data mean (standard deviation) or number of subjects. CK = creatine kinase, CK-MB = creatine kinase isotype MB. HDL = high density lipoprotein, LDL = low density lipoprotein. Expression of GDF15 and TERT failed to correlate significantly in the controls with r = 0.22, p = 0.131, but in the patients the correlation was significant at p = 0.55, p < 0.01. Expression of GDF15 and TERT failed to correlate significantly with age in the controls with r = 0.15, p = 0.327 and r = 0.23, p = 0.131 respectively, or in the patients with r = 0.08 p = 0.576 for GDF15 and r= -0.02 p = 0.879 for TERT . Clinical, demographic, pharmacotherapy, and laboratory data of the two patient groups are shown in Table 2 . The two groups were matched for smoking, age, and sex. Serum creatinine and HbA1c were higher in the diabetic group, but there was no difference in levels of serum urea, or in cardiology or lipid metrics. There was no difference in the relative expression of GDF15 in the patients free of diabetes (1.6 [1.42–1.78]) compared to those with diabetes (1.6 [1.29–1.91]) (p = 0.996). Similarly, there was no difference in the expression of TERT in patients free of diabetes (1.19 [1.06–1.33]) compared to those with diabetes (1.25 [0.98–1.50]) (p = 0.739). GDF15 and TERT correlated significantly in the patients with CAD (r = 0.48, p = 0.013) and also in patients with CAD and diabetes (r = 0.57, p = 0.002). Table 2 Clinical, demographic, pharmacotherapy, and laboratory data on patient subgroups at presentation CAD (n = 26) CAD and diabetes (n = 27) P value Age (years) 41.3 (11.6) 44.6 (7.2) 0.223 Sex (m/f) 23/3 18/7 0.138 Smoking (y/n) 16/10 12/15 0.213 Urea (mg/dL) 4.5 (1.2) 4.0 (1.5) 0.157 Creatinine (mg/dL) 1.0 (0.3) 1.3 (0.5) 0.032 CK (IU/L) 126 (45) 149 (45) 0.067 CK-MB (ng/mL) 8.4 (3.9–10.9) 6.0 (3.5–8.30 0.118 Troponin T (ng/mL)* 0.12 (0.06–1.25) 0.08 (0.05-1.00) 0.676 Cholesterol (mg/dL) 203 (48) 219 (68) 0.306 Triglycerides (mg/dL) 115 (39) 107 (28) 0.394 HDL (mg/dL) 40 (8) 45 (11) 0.073 LDL (mg/dL) 140 (44) 153 (70) 0.404 HbA1c (%) 4.6 (0.4) 11.4 (1.6) < 0.001 GAPDH expression (units) 18.6 (5.2) 18.7 (6.2) 0.901 GDF15 expression (units) 27.7 (2.4) 26.0 (4.4) 0.104 TERT expression (units) 21.7 (6.0) 21.0 (6.6) 0.699 Aspirin 26 27 - Beta-blocker 7 7 - Insulin 0 12 - Metformin 0 10 - Sulphonylurea 0 5 - Statin 21 8 - Ezetimibe 6 8 - ACEI 7 11 - Data mean (standard deviation), median (interquartile range) or number of subjects. CK = creatine kinase, CK-MB = creatine kinase isotype MB. HDL = high density lipoprotein, LDL = low density lipoprotein. CAD = coronary artery disease. *reference value < 0.01 ng/mL. Discussion Although the four major risk factors for coronary artery disease can be addressed and possibly treated, myocardial infarction and stroke still remain the leading global cause of death ( 21 ). In Egypt, ischaemic heart disease is a major public health issue, and in 2021 was the second leading cause of death in each 5-year age band from 25–29 years to 55–59 years (the first being COVID-19) and the leading cause from age 60 to 85-plus years. In the pre-COVID era, e.g. 2015–2019, ischaemic heart disease and stroke were the leading two causes of death ( 22 ). Molecular genetics are providing other potential causes of cardiovascular disease ( 1 , 2 , 23 ), among which are the genes GDF15 and TERT that code for molecules with markedly different physiological functions ( 3 , 14 ). Under certain circumstances, increased circulating levels of their protein products are present in the blood, and as such may be markers and/or targets of particular diseases that include cardiovascular disease and cancer ( 4 , 17 , 24 – 27 ). These increased levels may be related to features that include age and/or genetic control ( 6 , 28 ). The principal results of our study contribute to the literature in that we report (a) increased expression of both GDF15 and TERT in relatively young patients with CAD, with the relative expression of GDF15 being greater than that of TERT , (b) that this expression does not vary with the presence of diabetes, (c) that the co-expressions of these two genes is unrelated in healthy controls but correlate significantly in the patients regardless of their diabetes status, and (d) expression of either gene failed to correlate with age in patients or in controls. This last point is in contrast to other data on this risk factor ( 14 , 29 ), although our population has a maximum age of 55 years, and so excludes the elderly who are more likely to carry a greater burden of disease. As both genes and their products are altered in diabetics without cardiovascular disease ( 30 – 32 ), we note that this risk factor does not have a modulating effect on gene expression on a platform of acute coronary artery disease, although some have reported increases in those patients with more extensive disease ( 32 – 34 ). However, the precise clinical science of our data is clouded by the use of metformin by over a third of our patients with diabetes, as this drug has a positive influence on circulating GDF-15 ( 35 ). Furthermore, serum GDF-15 has been shown to correlate with blood glucose and HbA1c ( 36 ), although it is unknown if these, or the effect of metformin, are due to the direct promotion of the transcription of GDF15 , post-transcription changes, or other mechanisms. Although various SNPs in TERT have been reported in diabetes, there is no comparable data on the quantitative expression of this gene ( 37 – 39 ). Our cross-sectional study is unable to answer the question of whether the increased expression of these genes were present before the patients were admitted to hospital, or they are the product of the acute coronary syndrome. We accept the possibility that either of these two processes could be present. However, whatever the mechanism, it is notably that the relative expression of the two genes correlated strongly in the patients but not in the controls for reasons that demand an explanation that at present we cannot supply. In this pilot study we accept the limitations of a small sample size, and so can criticised for being at risk of false positives or false negatives, although the case/control and CAD with/without diabetes differences we found exceeded those of our hypothesis, so we believe our findings are robust. This limited sample size also precludes more complex multivariate analyses. We also acknowledge possible influences of other pathology we have not addressed, such as ACS-derived inflammation, and that we lack a diabetes control group. Nevertheless, we submit that our data has potential implications for the aetiology and/or pathogenesis of coronary artery disease. Conclusions Our pilot data provides evidence for alterations in the relative expression of GDF-15 and TERT in middle aged patients suffering an acute myocardial infarction, with a firm correlation between the two indices. There was no difference in the expression of these genes according to the presence of diabetes. We believe our data justifies a larger study to determine the mechanisms and implications of these findings, which, we speculate, may contribute further to our knowledge of the pathogenesis and management of cardiovascular disease. Abbreviations ACS Acute Coronary Syndrome CAD Coronary Artery Disease COVID-19 Coronavirus Disease 19 CK Creatine kinase Creatine kinase MB Creatine kinase muscle/brain cDNA Complimentary Deoxyribonucleic Acid EDTA Ethylene Diamine Tetra-Acetic Acid GAPDH Glyceraldehyde-3-phosphate dehydrogenase GDF15 Growth Differentiation Factor 15 HbA1c Glycated Haemoglobin HDL High Density Lipoprotein LDL Low Density Lipoprotein RNA Ribonucleic Acid RT-PCR Real-Time Polymerase Chain Reaction TERT Telomere Reverse Transcriptase USA United States of America Declarations Author Contribution Abdelsabour MA: Study conception, obtaining samples, design, consideration of the manuscript. Idriss NK: Genetic analysis, acquisition and interpretation of data, consideration of the manuscript. Blann AD: Performed final statistical analysis, interpretation of data, wrote final draft of the manuscript. Mosa AA: Genetic analysis and drafting the manuscript Fouad DA: Study design and writing the protocol, consideration of the manuscript. Amal AM: Recruiting samples and data analysis, consideration of the manuscript Ashry A: Recruiting samples and data analysis, consideration of the manuscript Sayed SA: Recruiting samples and data analysis, consideration of the manuscript Nasreldin E: Study design and writing the protocol, consideration of the manuscript. Hassan SA : recurring samples , and consideration of the manuscript. Elnaggar MG: Contributed data and performed the analysis, consideration of the manuscript Meki AA: Study design and writing the protocol, consideration of the manuscript. Hassen HA: Study design and writing the protocol, consideration of the manuscript.Gaber MA :writing the protocol, Laboratory investigations and data interpretation Acknowledgement We are deeply in debt for the grant office of the Faculty of Medicine, Assiut University which supported the study, Medical Biochemistry Department, the Cardiovascular Department and the Faculty of Computers and Information. Data Availability An Excel file of the raw data will be provided to bona-fide researchers on application to the corresponding author. References López Rodríguez M, Arasu UT, Kaikkonen MU. Exploring the genetic basis of coronary disease using functional genomics. Atherosclerosis 2023:374;87-98. Butnariu LI, Florea L, Badescu MC, et al. Etiologic puzzle of coronary artery disease: How important is genetic component. Life. 2022;12:865. doi: 10.3390/life12060865. Wang J, Wei L, Yang X, Zhong J. Roles of Growth Differentiation Factor 15 in Atherosclerosis and Coronary Artery Disease. J Am Heart Assoc. 2019;8:e012826. Zhang S, Hao P, Li J, et al. Prognostic value of growth differentiation factor 15 in patients with coronary artery disease: A meta-analysis and systematic review. Front Cardiovasc Med. 2023;10:1054187. Brenière C, Méloux A, Pédard M, et al. Growth differentiation factor-15 (GDF-15) is associated with mortality in ischemic stroke patients treated with acute revascularization therapy. Frontiers Neurol, 2019:10,611-617. Pence BD. Growth Differentiation Factor-15 in Immunity and Aging. Front Aging. 2022 Feb 9;3:837575. Semba RD, Gonzalez-Freire M, Tanaka T, et al. Elevated plasma growth and differentiation factor 15 is associated with slower gait speed and lower physical performance in healthy community-dwelling adults. J Gerontol A Biol Sci Med Sci 2020:75;175-180. Jiang J, Thalamuthu A, Ho JE, et al. A Meta-Analysis of Genome-Wide Association Studies of Growth Differentiation Factor-15 Concentration in Blood. Front Genet. 2018;9:97. doi: 10.3389/fgene.2018.00097 Xiang Y, Zhang T, Guo J, et al. The Association of Growth Differentiation Factor-15 Gene Polymorphisms with Growth Differentiation Factor-15 Serum Levels and Risk of Ischemic Stroke. Stroke Cerebrovasc Dis. 2017;26:2111-2119. Wang Z, Yang F, Ma M, et al. The impact of growth differentiation factor 15 on the risk of cardiovascular diseases: two-sample Mendelian randomization study. BMC Cardiovasc Disord. 2020;20:462. Yin H, Pickering JG. Telomere Length: Implications for Atherogenesis. Curr Atheroscler Rep. 2023 Mar;25(3):95-103. Chen B, Yan Y, Wang H, Xu J. Association between genetically determined telomere length and health-related outcomes: A systematic review and meta-analysis of Mendelian randomization studies. Aging Cell. 2023 May 26:e13874. Zimnitskaya OV, Petrova MM, Lareva NV, et al. Leukocyte Telomere Length as a Molecular Biomarker of Coronary Heart Disease. Genes. 2022;13:1234. doi: 10.3390/genes13071234. Palamarchuk AI, Kovalenko EI, Streltsova MA. Multiple actions of telomerase reverse transcriptase in cell death regulation. Biomedicines. 2023;11:1091. doi:10.3390/ biomedicines 11041091. Zurek M, Altschmied J, Kohlgrüber S, et al. Role of Telomerase in the Cardiovascular System. Genes. 2016;7:29. doi: 10.3390/genes7060029. Yeh JK, Wang CY. Telomeres and Telomerase in Cardiovascular Diseases. Genes. 2016;7(9):58. doi: 10.3390/genes7090058 Hoffmann J, Richardson G, Haendeler J, et al. Telomerase as a therapeutic target in cardiovascular disease. Arterioscl Thromb Vasc Biol 2021:41;1047-1061 Sharma S, Chowdhury S. Emerging mechanisms of telomerase reactivation in cancer. Trends Cancer. 2022 Aug;8(8):632-641. Opstad T, Solheim S, Pettersen A et al. TERT and TET2 Genetic Variants Affect Leukocyte Telomere Length and Clinical Outcome in Coronary Artery Disease Patients—A Possible Link to Clonal Hematopoiesis. Biomedicines 2022, 10, 2027. https://doi.org/10.3390/ biomedicines 10082027. Zhang S, Ji G, Liang Y, et al. Polymorphisms in Telomere Length Associated TERC and TERT predispose for Ischemic Stroke in a Chinese Han population. Sci Rep. 2017 Jan 6;7:40151. doi: 10.1038/srep40151. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death Accessed 20 th September 2024. https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates Accessed 20 th September 2024 Tirdea C, Hostiuc S, Moldovan H, et al. Identification of Risk Genes Associated with Myocardial Infarction - Big Data Analysis and Literature Review. Int J Mol Sci. 2022 ;23:15008. Mourouzis K, Siasos G, Bozini N, et al. Association of Growth Differentiation Factor 15 with Arterial Stiffness and Endothelial Function in Subpopulations of Patients with Coronary Artery Disease: A Proof-of-Concept Study. Recent Adv Inflamm Allergy Drug Discov. 2022;16:107-115. Kroenke CH, Pletcher MJ, Lin J, et al. Telomerase, telomere length, and coronary artery calcium in black and white men in the CARDIA study. Atherosclerosis. 2012;220:506-12. Nopp S, Moik F, Kraler S, et al. Growth differentiation factor-15 and prediction of cancer-associated thrombosis and mortality: a prospective cohort study. J Thromb Haemost. 2023 May 14:S1538-7836(23)00414-2. Holesova Z, Krasnicanova L, Saade R, et al. Telomere Length Changes in Cancer: Insights on Carcinogenesis and Potential for Non-Invasive Diagnostic Strategies. Genes (Basel). 2023 Mar 14;14(3):715. Perez-Rivera JA, Pabon-Osuna P, Cieza-Borrella C, et al. The role of the TERC-63G>A and TERT-1327C>T telomerase polymorphisms in the study of men with acute coronary syndrome. Minerva Cardioangiol. 2015;63:467-74. Pence BD. Growth differentiation factor-15 in immunity and aging. Front Aging 2022:3;837575. Baltzis D, Meimeti E, Grammatikopoulou et al. Assessment of telomerase activity in leukocytes of type 2 diabetes mellitus patients having or not foot ulcer: Possible correlation with other clinical parameters. Exp Ther Med. 2018 Apr;15(4):3420-3424. Huda N, Yasmin T, Nabi AHMN. MNS16A VNTR polymorphism of human telomerase gene: Elucidation of a gender specific potential allele associated with type 2 diabetes in Bangladeshi population. J Diabetes Complications. 2021 Oct;35(10):108018. Zhu X, Zhang Y, Liang F, et al. Relationship Between plasma growth differentiation factor 15 Levels and Complications of Type 2 Diabetes Mellitus: A Cross-sectional Study. Can J Diabetes. 2023 Mar;47(2):117-123.e7. Sendur SN, Firlatan B, Baykal G, et al. Serum growth differentiation factor-15 levels are associated with the severity of diabetic foot ulcer. Hormones 2022 Dec;21(4):719-728. Al Dehaini DMB, Al-Bustan SA, Malalla ZHA, et al. The influence of TERC, TERT and ACYP2 genes polymorphisms on plasma telomerase concentration, telomeres length and T2DM. Gene. 2021;766:145127. Al-Kuraishy HM, Al-Gareeb AI, Alexiou A, et al. Metformin and growth differentiation factor 15 (GDF15) in type 2 diabetes mellitus: A hidden treasure. J Diabetes. 2022;14:806-814. Adela R, Banerjee SK. GDF-15 as a Target and Biomarker for Diabetes and Cardiovascular Diseases: A Translational Prospective. J Diabetes Res. 2015;2015:490842. Sanchez M, Kannengiesser C, Hoang S, et al. Leukocyte telomere length, allelic variations in related genes and risk of coronary heart disease in people with long-standing type 1 diabetes. Cardiovasc Diabetol. 2022 Oct 11;21(1):206. Al-Dehaini DMB, Al-Bustan SA, Malalla ZHA, et al. Analogous telomeres shortening and different metabolic profile: hypertension versus hypertension/type 2 diabetes mellitus comorbidity. Cardiovasc Endocrinol Metab. 2020 Sep 3;10(2):106-112. Goswami A, Huda N, Yasmin T, et al. Association study of leukocyte telomere length and genetic polymorphism within hTERT promoter with type 2 diabetes in Bangladeshi population. Mol Biol Rep. 2021 Jan;48(1):285-295. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5129243","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":361656271,"identity":"cc485fa7-0583-46d7-92d5-27c9a21f4023","order_by":0,"name":"MA Abdelsabour","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"MA","middleName":"","lastName":"Abdelsabour","suffix":""},{"id":361656272,"identity":"62fbb88e-a6e3-44a3-8476-8e10a11acee6","order_by":1,"name":"NK Idriss","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"NK","middleName":"","lastName":"Idriss","suffix":""},{"id":361656273,"identity":"b44be98b-75c7-4c7f-b5ca-e3df1b1dfb6b","order_by":2,"name":"AD Blann","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACNgYGAxCdwAciPzaASMbGA/i08MO0sIHUzmxgkABSDXi1SDYgaWHmBWthYMCrxeB287YPH9sY8tjYzx57bLvDpk63/TDQlhqbaJxa7hwrnjmzjaGYjScv3Tj3TJqE2ZlEoJZjabkNuLTcyDFm5t3GkNjGkGMmndt2WMLsAFALY8NhnFrsQVr+grTwvzGTtgRpOf8QvxawLYwgLRJAWxhBWm4QsMXgRloxY+8/iWI2iTdmkr1taZLbbgBtScDrl+TNDD/O2OTx8+eYSfxss+E3O5/+8MGHGhucWqBAAo2fgF/5KBgFo2AUjAICAAAR/F4ixFr96gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Huddersfield","correspondingAuthor":true,"prefix":"","firstName":"AD","middleName":"","lastName":"Blann","suffix":""},{"id":361656274,"identity":"c03c10c1-9956-484a-9ffe-2a2e68ff6199","order_by":3,"name":"AA Mosa","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"AA","middleName":"","lastName":"Mosa","suffix":""},{"id":361656275,"identity":"01f18148-2e52-48ff-915c-55151efc95b6","order_by":4,"name":"DA Fouad","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"DA","middleName":"","lastName":"Fouad","suffix":""},{"id":361656276,"identity":"8e1f4519-6732-47c8-90cc-cf528cd2c378","order_by":5,"name":"AM Amal","email":"","orcid":"","institution":"Assiut University Hospitals","correspondingAuthor":false,"prefix":"","firstName":"AM","middleName":"","lastName":"Amal","suffix":""},{"id":361656277,"identity":"24014763-814f-4837-8554-58388150cb17","order_by":6,"name":"A Ashry","email":"","orcid":"","institution":"Assiut University Hospitals","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"","lastName":"Ashry","suffix":""},{"id":361656278,"identity":"4c830dcb-9995-4d59-93bf-a1f4902749da","order_by":7,"name":"SA Sayed","email":"","orcid":"","institution":"Assiut University Hospitals","correspondingAuthor":false,"prefix":"","firstName":"SA","middleName":"","lastName":"Sayed","suffix":""},{"id":361656279,"identity":"ed940b25-bbdb-4f97-ab4d-e11a8f8a4eb1","order_by":8,"name":"E Nasreldin","email":"","orcid":"","institution":"Assiut University Hospitals","correspondingAuthor":false,"prefix":"","firstName":"E","middleName":"","lastName":"Nasreldin","suffix":""},{"id":361656280,"identity":"a33b0268-4de1-4746-b63a-5ed82946ae7e","order_by":9,"name":"SA Hassen","email":"","orcid":"","institution":"Assiut University Hospitals","correspondingAuthor":false,"prefix":"","firstName":"SA","middleName":"","lastName":"Hassen","suffix":""},{"id":361656281,"identity":"4d2abc71-3bb5-4c12-8647-836e9d7a5f26","order_by":10,"name":"MG Elnaggar","email":"","orcid":"","institution":"South Egypt Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"MG","middleName":"","lastName":"Elnaggar","suffix":""},{"id":361656282,"identity":"aa422ae6-973c-4ff4-a005-1d59a0250605","order_by":11,"name":"AA Meki","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"AA","middleName":"","lastName":"Meki","suffix":""},{"id":361656283,"identity":"38ba5782-5572-4271-98f4-112188a94e25","order_by":12,"name":"HA Hassen","email":"","orcid":"","institution":"Assiut University","correspondingAuthor":false,"prefix":"","firstName":"HA","middleName":"","lastName":"Hassen","suffix":""},{"id":361656284,"identity":"c75a1aa7-4daa-466e-af30-0b0b63268b1d","order_by":13,"name":"M Gaber","email":"","orcid":"","institution":"South Egypt Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Gaber","suffix":""}],"badges":[],"createdAt":"2024-09-21 15:03:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5129243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5129243/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66128296,"identity":"84759b0e-61e2-4571-a8ea-b9e4998803d1","added_by":"auto","created_at":"2024-10-08 03:19:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":450133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5129243/v1/c9a67470-a1ed-481c-b133-4df82010dbcd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic value of the gene expression of Growth Differentiation Factor 15 and Telomerase Reverse Transcriptase in middle-aged patients with acute coronary artery disease: a pilot case-control study ","fulltext":[{"header":"Background","content":"\u003cp\u003eThe pathogenesis of coronary artery disease has long been linked to the four major risk factors of diabetes, hypertension, hypercholesterolaemia and smoking, but numerous genetic influences are emerging, with over 300 loci and over 1500 candidate genes implicated (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGrowth differentiation factor 15 (GDF-15), also known as macrophage inhibitory cytokine-1, is a member of the transforming growth factor-β superfamily and has functions likely to have a role in numerous processes such as angiogenesis and inflammation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Coded for by \u003cem\u003eGDF15\u003c/em\u003e at 19p13.11, increased serum levels have been reported in many cardiovascular diseases such as atrial fibrillation and heart failure and are linked to increased all-cause death and cardiovascular death in coronary artery disease and with death following an ischaemic stroke (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Several studies have indicated that levels of circulating GDF-15 rise with age and are directly, perhaps causally, linked to the aetiology of these cardiovascular diseases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Data from the Framingham study points to effects of risk factors as well as genetics on plasma concentrations of GDF-15 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and although variants of \u003cem\u003eGDF15\u003c/em\u003e are linked to ischaemic stroke in a Chinese population (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), this has been countered in a meta-analysis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTelomere length has long been associated with a variety of diseases, including atherogenesis and CAD (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Telomerase is a ribonucleoprotein enzyme with two catalytically essential subunits, the telomerase RNA, and telomerase reverse transcriptase protein (TERT) that regulates telomere length (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As this enzyme effectively reduces telomere length, several commentators have suggested it may be a new therapeutic target (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Over-expression of human TERT (h-TERT), coded for by \u003cem\u003eTERT\u003c/em\u003e at 5p15.33, may be important in cancer (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), whilst variants of \u003cem\u003eTERT\u003c/em\u003e may be linked to outcome in stable CAD (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and risk of ischaemic stroke (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, the literature points to potential roles for both \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e and their protein products in coronary artery disease, and our literature search failed to find any report of both genes or molecules analysed together. However, the great majority of clinical data focuses on late-middle aged and elderly patients, with little data on the young middle-aged, a leading factor in why we feel the current investigation is necessary. In order to fill this gap in the knowledge, we therefore hypothesised altered expression of both \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e in relatively young patients presenting to hospital with an acute coronary syndrome subsequently shown to be a myocardial infarction.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe tested our hypothesis in consecutive patients with existing cardiovascular disease (myocardial infarction, stroke, arterial surgery) aged up to 55 years old presenting to the Cardiac Catheter Unit, Cardiology Department, Assuit University Hospital, Egypt, in an acute coronary syndrome subsequently shown by angiography to have CAD precipitating a myocardial infarction. Data from patients was controlled by samples from healthy hospital and laboratory staff free of cardiovascular disease or its risk factors. Exclusion criteria were age\u0026thinsp;\u0026gt;\u0026thinsp;55 years, psychological disorders, cancer, autoimmune diseases, acute or chronic inflammatory diseases, and diabetic patients treated with an inhibitor of dipeptidyl peptidase. The purpose and method of the study were explained to each participant and informed consent was obtained. A predesigned case record form was used to record age and clinical data of the consenting subjects. This study was approved by the ethical committee of the Faculty of Medicine, Assiut University (IRB no. 17200057). This study is not a clinical trial, so there is no applicable number.\u003c/p\u003e \u003cp\u003eVenous blood was collected from each patient on presentation, and from each control. Serum urea, creatinine, creatine kinase, creatine kinase MB, troponin-T (patients only), HbA1c, total cholesterol, triglycerides, and high-density lipoprotein were performed by the routine hospital clinical chemistry service (which subscribes to the national quality control service) by established methods. Low-density lipoprotein was calculated according to the Freidwald formula.\u003c/p\u003e \u003cp\u003eTotal RNA was extracted from EDTA-anticoagulated whole blood using Direct-zol\u0026trade; RNA Miniprep Plus (Zymo Research, Irvine, CA, USA), followed by a SensiFAST\u0026trade; SYBR\u0026reg; Hi-ROX One-Step Kit for cDNA by RT-PCR (Meridian Bioscience, Memphis, TN, USA). The reactions were performed in a thermal cycler (MJ Research, Inc., Waltham, MA, USA) and the reaction products stored at -20\u0026deg;C until analysed. The thermal profile consisted of 20 minutes at 46\u0026deg;C for reverse transcription then at 95\u0026deg;C for 1 minute for reverse transcriptase inactivation. Gene expression was standardized to that of \u003cem\u003eGAPDH\u003c/em\u003e as a control and is presented as fold change. We applied 1 \u0026micro;M of each forward and reverse primer specific for each target gene: \u003cem\u003eTERT\u003c/em\u003e, 5\u0026prime;-GGAGCAAGTTGCAAAGCATTG-3\u0026prime; 5\u0026prime;-TCCCACGACGTAGTCCATGTT-3\u0026prime;. \u003cem\u003eGDF15\u003c/em\u003e, 5\u0026prime;-TCAAGGTCGTGGGACGTGACA-3\u0026prime; 5\u0026prime;-GCCGTGCGGACGAAGATTCT-3\u0026prime; and \u003cem\u003eGAPDH\u003c/em\u003e, 5\u0026prime;-AGCCACATCGCTCAGACAC-3\u0026prime;and 5\u0026prime;-GCCCAATACGACCAAATCC-3\u0026prime;. The products were quantified by SYBR green dye analysis. The mean expression levels of \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e mRNA relative to those of \u003cem\u003eGAPDH\u003c/em\u003e were analysed by the quantitation-comparative Ct (ΔΔCt) method on a fluorescence quantitative PCR analyser (ABI 7500, Applied Biosystems, Foster City, CA, USA).\u003c/p\u003e \u003cp\u003eWe hypothesised a difference in relative expression of \u003cem\u003eGDF15\u003c/em\u003e or \u003cem\u003eTERT\u003c/em\u003e of at least 12.5% compared to expression in the controls. To achieve this at alpha\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and 1-beta\u0026thinsp;\u0026gt;\u0026thinsp;80% calls for 40 per group, and for additional assurance we recruited in excess of this requirement. A sample size of 80 provides the power to defend a Pearson correlation coefficient of \u0026gt;\u0026thinsp;0.31. In post-hoc analyses we noted the large number of diabetics and considered their analysis in relation to non-diabetics. A sample size of 25 per group provides the power to robust defend a difference of 16% in a test statistic compared to a control group, and a sample size of 50 provides the power to defend a Pearson correlation coefficient of \u0026gt;\u0026thinsp;0.39. Categorical data were analysed by the chi-square test, continuously variable data by student\u0026rsquo;s t test or the Mann-Whitney U test and correlated by Pearson\u0026rsquo;s method. All analyses were performed on Minitab release 21.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClinical, demographic, and laboratory data of the controls and patients are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The two groups were matched for age and sex. There was no difference in levels of serum urea between the groups, but (as partially expected) creatinine, HbA1c, all cardiology metrics, and all lipid metrics were increased in the patients. There was no difference in the expression of \u003cem\u003eGAPDH\u003c/em\u003e between the groups, but the expression of \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e were both increased. Compared to expression in controls, mean (95% confidence interval) relative expression of \u003cem\u003eGDF15\u003c/em\u003e in the patients was 1.38 (1.13\u0026ndash;1.49) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and of \u003cem\u003eTERT\u003c/em\u003e was 1.12 (1.04\u0026ndash;1.20) (p\u0026thinsp;=\u0026thinsp;0.003), with relative expression of \u003cem\u003eGDF15\u003c/em\u003e being greater than that of \u003cem\u003eTERT\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eClinical, demographic and laboratory data on Cases and Controls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.9 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (m/f)(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGADPH\u003c/em\u003e expression (units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.7 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDF15\u003c/em\u003e expression (units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTERT\u003c/em\u003e expression (units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.7 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData mean (standard deviation) or number of subjects. CK\u0026thinsp;=\u0026thinsp;creatine kinase, CK-MB\u0026thinsp;=\u0026thinsp;creatine kinase isotype MB. HDL\u0026thinsp;=\u0026thinsp;high density lipoprotein, LDL\u0026thinsp;=\u0026thinsp;low density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExpression of \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e failed to correlate significantly in the controls with r\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;=\u0026thinsp;0.131, but in the patients the correlation was significant at p\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Expression of \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e failed to correlate significantly with age in the controls with r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;=\u0026thinsp;0.327 and r\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;0.131 respectively, or in the patients with r\u0026thinsp;=\u0026thinsp;0.08 p\u0026thinsp;=\u0026thinsp;0.576 for \u003cem\u003eGDF15\u003c/em\u003e and r= -0.02 p\u0026thinsp;=\u0026thinsp;0.879 for \u003cem\u003eTERT\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eClinical, demographic, pharmacotherapy, and laboratory data of the two patient groups are shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The two groups were matched for smoking, age, and sex. Serum creatinine and HbA1c were higher in the diabetic group, but there was no difference in levels of serum urea, or in cardiology or lipid metrics. There was no difference in the relative expression of \u003cem\u003eGDF15\u003c/em\u003e in the patients free of diabetes (1.6 [1.42\u0026ndash;1.78]) compared to those with diabetes (1.6 [1.29\u0026ndash;1.91]) (p\u0026thinsp;=\u0026thinsp;0.996). Similarly, there was no difference in the expression of \u003cem\u003eTERT\u003c/em\u003e in patients free of diabetes (1.19 [1.06\u0026ndash;1.33]) compared to those with diabetes (1.25 [0.98\u0026ndash;1.50]) (p\u0026thinsp;=\u0026thinsp;0.739). \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e correlated significantly in the patients with CAD (r\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;=\u0026thinsp;0.013) and also in patients with CAD and diabetes (r\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;=\u0026thinsp;0.002).\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\u003eClinical, demographic, pharmacotherapy, and laboratory data on patient subgroups at presentation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAD and diabetes (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.3 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (m/f)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (y/n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.4 (3.9\u0026ndash;10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.5\u0026ndash;8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin T (ng/mL)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.06\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.05-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e expression (units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDF15\u003c/em\u003e expression (units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.7 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTERT\u003c/em\u003e expression (units)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.7 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.0 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulphonylurea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEzetimibe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData mean (standard deviation), median (interquartile range) or number of subjects. CK\u0026thinsp;=\u0026thinsp;creatine kinase, CK-MB\u0026thinsp;=\u0026thinsp;creatine kinase isotype MB. HDL\u0026thinsp;=\u0026thinsp;high density lipoprotein, LDL\u0026thinsp;=\u0026thinsp;low density lipoprotein. CAD\u0026thinsp;=\u0026thinsp;coronary artery disease. *reference value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 ng/mL.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough the four major risk factors for coronary artery disease can be addressed and possibly treated, myocardial infarction and stroke still remain the leading global cause of death (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In Egypt, ischaemic heart disease is a major public health issue, and in 2021 was the second leading cause of death in each 5-year age band from 25\u0026ndash;29 years to 55\u0026ndash;59 years (the first being COVID-19) and the leading cause from age 60 to 85-plus years. In the pre-COVID era, e.g. 2015\u0026ndash;2019, ischaemic heart disease and stroke were the leading two causes of death (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMolecular genetics are providing other potential causes of cardiovascular disease (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), among which are the genes \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e that code for molecules with markedly different physiological functions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Under certain circumstances, increased circulating levels of their protein products are present in the blood, and as such may be markers and/or targets of particular diseases that include cardiovascular disease and cancer (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). These increased levels may be related to features that include age and/or genetic control (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe principal results of our study contribute to the literature in that we report (a) increased expression of both \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e in relatively young patients with CAD, with the relative expression of \u003cem\u003eGDF15\u003c/em\u003e being greater than that of \u003cem\u003eTERT\u003c/em\u003e, (b) that this expression does not vary with the presence of diabetes, (c) that the co-expressions of these two genes is unrelated in healthy controls but correlate significantly in the patients regardless of their diabetes status, and (d) expression of either gene failed to correlate with age in patients or in controls. This last point is in contrast to other data on this risk factor (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), although our population has a maximum age of 55 years, and so excludes the elderly who are more likely to carry a greater burden of disease. As both genes and their products are altered in diabetics without cardiovascular disease (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), we note that this risk factor does not have a modulating effect on gene expression on a platform of acute coronary artery disease, although some have reported increases in those patients with more extensive disease (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). However, the precise clinical science of our data is clouded by the use of metformin by over a third of our patients with diabetes, as this drug has a positive influence on circulating GDF-15 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Furthermore, serum GDF-15 has been shown to correlate with blood glucose and HbA1c (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), although it is unknown if these, or the effect of metformin, are due to the direct promotion of the transcription of \u003cem\u003eGDF15\u003c/em\u003e, post-transcription changes, or other mechanisms. Although various SNPs in \u003cem\u003eTERT\u003c/em\u003e have been reported in diabetes, there is no comparable data on the quantitative expression of this gene (\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur cross-sectional study is unable to answer the question of whether the increased expression of these genes were present before the patients were admitted to hospital, or they are the product of the acute coronary syndrome. We accept the possibility that either of these two processes could be present. However, whatever the mechanism, it is notably that the relative expression of the two genes correlated strongly in the patients but not in the controls for reasons that demand an explanation that at present we cannot supply. In this pilot study we accept the limitations of a small sample size, and so can criticised for being at risk of false positives or false negatives, although the case/control and CAD with/without diabetes differences we found exceeded those of our hypothesis, so we believe our findings are robust. This limited sample size also precludes more complex multivariate analyses. We also acknowledge possible influences of other pathology we have not addressed, such as ACS-derived inflammation, and that we lack a diabetes control group. Nevertheless, we submit that our data has potential implications for the aetiology and/or pathogenesis of coronary artery disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur pilot data provides evidence for alterations in the relative expression of \u003cem\u003eGDF-15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e in middle aged patients suffering an acute myocardial infarction, with a firm correlation between the two indices. There was no difference in the expression of these genes according to the presence of diabetes. We believe our data justifies a larger study to determine the mechanisms and implications of these findings, which, we speculate, may contribute further to our knowledge of the pathogenesis and management of cardiovascular disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Coronary Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary Artery Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOVID-19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus Disease 19\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatine kinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCreatine kinase MB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCreatine kinase muscle/brain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplimentary Deoxyribonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthylene Diamine Tetra-Acetic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAPDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDF15\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrowth Differentiation Factor 15\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated Haemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReal-Time Polymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTERT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTelomere Reverse Transcriptase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States of America\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAbdelsabour MA: Study conception, obtaining samples, design, consideration of the manuscript. Idriss NK: Genetic analysis, acquisition and interpretation of data, consideration of the manuscript. Blann AD: Performed final statistical analysis, interpretation of data, wrote final draft of the manuscript. Mosa AA: Genetic analysis and drafting the manuscript Fouad DA: Study design and writing the protocol, consideration of the manuscript. Amal AM: Recruiting samples and data analysis, consideration of the manuscript Ashry A: Recruiting samples and data analysis, consideration of the manuscript Sayed SA: Recruiting samples and data analysis, consideration of the manuscript Nasreldin E: Study design and writing the protocol, consideration of the manuscript. Hassan SA : recurring samples , and consideration of the manuscript. Elnaggar MG: Contributed data and performed the analysis, consideration of the manuscript Meki AA: Study design and writing the protocol, consideration of the manuscript. Hassen HA: Study design and writing the protocol, consideration of the manuscript.Gaber MA :writing the protocol, Laboratory investigations and data interpretation\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are deeply in debt for the grant office of the Faculty of Medicine, Assiut University which supported the study, Medical Biochemistry Department, the Cardiovascular Department and the Faculty of Computers and Information.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAn Excel file of the raw data will be provided to bona-fide researchers on application to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eL\u0026oacute;pez Rodr\u0026iacute;guez M, Arasu UT, Kaikkonen MU. Exploring the genetic basis of coronary disease using functional genomics. Atherosclerosis 2023:374;87-98.\u003c/li\u003e\n \u003cli\u003eButnariu LI, Florea L, Badescu MC, et al. Etiologic puzzle of coronary artery disease: How important is genetic component. Life. 2022;12:865. doi: 10.3390/life12060865.\u003c/li\u003e\n \u003cli\u003eWang J, Wei L, Yang X, Zhong J. Roles of Growth Differentiation Factor 15 in Atherosclerosis and Coronary Artery Disease. J Am Heart Assoc. 2019;8:e012826.\u003c/li\u003e\n \u003cli\u003eZhang S, Hao P, Li J, et al. Prognostic value of growth differentiation factor 15 in patients with coronary artery disease: A meta-analysis and systematic review. Front Cardiovasc Med. 2023;10:1054187.\u003c/li\u003e\n \u003cli\u003eBreni\u0026egrave;re C, M\u0026eacute;loux A, P\u0026eacute;dard M, et al. Growth differentiation factor-15 (GDF-15) is associated with mortality in ischemic stroke patients treated with acute revascularization therapy. Frontiers Neurol, 2019:10,611-617.\u003c/li\u003e\n \u003cli\u003ePence BD. Growth Differentiation Factor-15 in Immunity and Aging. Front Aging. 2022 Feb 9;3:837575.\u003c/li\u003e\n \u003cli\u003eSemba RD, Gonzalez-Freire M, Tanaka T, et al. Elevated plasma growth and differentiation factor 15 is associated with slower gait speed and lower physical performance in healthy community-dwelling adults. J Gerontol A Biol Sci Med Sci 2020:75;175-180.\u003c/li\u003e\n \u003cli\u003eJiang J, Thalamuthu A, Ho JE, et al. A Meta-Analysis of Genome-Wide Association Studies of Growth Differentiation Factor-15 Concentration in Blood. Front Genet. 2018;9:97. doi: 10.3389/fgene.2018.00097\u003c/li\u003e\n \u003cli\u003eXiang Y, Zhang T, Guo J, et al. The Association of Growth Differentiation Factor-15 Gene Polymorphisms with Growth Differentiation Factor-15 Serum Levels and Risk of Ischemic Stroke. Stroke Cerebrovasc Dis. 2017;26:2111-2119.\u003c/li\u003e\n \u003cli\u003eWang Z, Yang F, Ma M, et al. The impact of growth differentiation factor 15 on the risk of cardiovascular diseases: two-sample Mendelian randomization study. BMC Cardiovasc Disord. 2020;20:462.\u003c/li\u003e\n \u003cli\u003eYin H, Pickering JG. Telomere Length: Implications for Atherogenesis. Curr Atheroscler Rep. 2023 Mar;25(3):95-103.\u003c/li\u003e\n \u003cli\u003eChen B, Yan Y, Wang H, Xu J. Association between genetically determined telomere length and health-related outcomes: A systematic review and meta-analysis of Mendelian randomization studies. Aging Cell. 2023 May 26:e13874.\u003c/li\u003e\n \u003cli\u003eZimnitskaya OV, Petrova MM, Lareva NV, et al. Leukocyte Telomere Length as a Molecular Biomarker of Coronary Heart Disease. Genes. 2022;13:1234. doi: 10.3390/genes13071234.\u003c/li\u003e\n \u003cli\u003ePalamarchuk AI, Kovalenko EI, Streltsova MA. Multiple actions of telomerase reverse transcriptase in cell death regulation. Biomedicines. 2023;11:1091. doi:10.3390/ biomedicines 11041091.\u003c/li\u003e\n \u003cli\u003eZurek M, Altschmied J, Kohlgr\u0026uuml;ber S, et al. Role of Telomerase in the Cardiovascular System. Genes. 2016;7:29. doi: 10.3390/genes7060029.\u003c/li\u003e\n \u003cli\u003eYeh JK, Wang CY. Telomeres and Telomerase in Cardiovascular Diseases. Genes. 2016;7(9):58. doi: 10.3390/genes7090058\u003c/li\u003e\n \u003cli\u003eHoffmann J, Richardson G, Haendeler J, et al. Telomerase as a therapeutic target in cardiovascular disease. Arterioscl Thromb Vasc Biol 2021:41;1047-1061\u003c/li\u003e\n \u003cli\u003eSharma S, Chowdhury S. Emerging mechanisms of telomerase reactivation in cancer. Trends Cancer. 2022 Aug;8(8):632-641.\u003c/li\u003e\n \u003cli\u003eOpstad T, Solheim S, Pettersen A et al. TERT and TET2 Genetic Variants Affect Leukocyte Telomere Length and Clinical Outcome in Coronary Artery Disease Patients\u0026mdash;A Possible Link to Clonal Hematopoiesis. Biomedicines 2022, 10, 2027. https://doi.org/10.3390/ biomedicines 10082027.\u003c/li\u003e\n \u003cli\u003eZhang S, Ji G, Liang Y, et al. Polymorphisms in Telomere Length Associated TERC and TERT predispose for Ischemic Stroke in a Chinese Han population. Sci Rep. 2017 Jan 6;7:40151. doi: 10.1038/srep40151.\u003c/li\u003e\n \u003cli\u003ehttps://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death Accessed 20\u003csup\u003eth\u003c/sup\u003e September 2024.\u003c/li\u003e\n \u003cli\u003ehttps://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates Accessed 20\u003csup\u003eth\u003c/sup\u003e September 2024\u003c/li\u003e\n \u003cli\u003eTirdea C, Hostiuc S, Moldovan H, et al. Identification of Risk Genes Associated with Myocardial Infarction - Big Data Analysis and Literature Review. Int J Mol Sci. 2022 ;23:15008.\u003c/li\u003e\n \u003cli\u003eMourouzis K, Siasos G, Bozini N, et al. Association of Growth Differentiation Factor 15 with Arterial Stiffness and Endothelial Function in Subpopulations of Patients with Coronary Artery Disease: A Proof-of-Concept Study. Recent Adv Inflamm Allergy Drug Discov. 2022;16:107-115.\u003c/li\u003e\n \u003cli\u003eKroenke CH, Pletcher MJ, Lin J, et al. Telomerase, telomere length, and coronary artery calcium in black and white men in the CARDIA study. Atherosclerosis. 2012;220:506-12.\u003c/li\u003e\n \u003cli\u003eNopp S, Moik F, Kraler S, et al. Growth differentiation factor-15 and prediction of cancer-associated thrombosis and mortality: a prospective cohort study. J Thromb Haemost. 2023 May 14:S1538-7836(23)00414-2.\u003c/li\u003e\n \u003cli\u003eHolesova Z, Krasnicanova L, Saade R, et al. Telomere Length Changes in Cancer: Insights on Carcinogenesis and Potential for Non-Invasive Diagnostic Strategies. Genes (Basel). 2023 Mar 14;14(3):715.\u003c/li\u003e\n \u003cli\u003ePerez-Rivera JA, Pabon-Osuna P, Cieza-Borrella C, et al. The role of the TERC-63G\u0026gt;A and TERT-1327C\u0026gt;T telomerase polymorphisms in the study of men with acute coronary syndrome. Minerva Cardioangiol. 2015;63:467-74.\u003c/li\u003e\n \u003cli\u003ePence BD. Growth differentiation factor-15 in immunity and aging. Front Aging 2022:3;837575.\u003c/li\u003e\n \u003cli\u003eBaltzis D, Meimeti E, Grammatikopoulou et al. Assessment of telomerase activity in leukocytes of type 2 diabetes mellitus patients having or not foot ulcer: Possible correlation with other clinical parameters. Exp Ther Med. 2018 Apr;15(4):3420-3424.\u003c/li\u003e\n \u003cli\u003eHuda N, Yasmin T, Nabi AHMN. MNS16A VNTR polymorphism of human telomerase gene: Elucidation of a gender specific potential allele associated with type 2 diabetes in Bangladeshi population. J Diabetes Complications. 2021 Oct;35(10):108018.\u003c/li\u003e\n \u003cli\u003eZhu X, Zhang Y, Liang F, et al. Relationship Between plasma growth differentiation factor 15 Levels and Complications of Type 2 Diabetes Mellitus: A Cross-sectional Study. Can J Diabetes. 2023 Mar;47(2):117-123.e7.\u003c/li\u003e\n \u003cli\u003eSendur SN, Firlatan B, Baykal G, et al. Serum growth differentiation factor-15 levels are associated with the severity of diabetic foot ulcer. Hormones 2022 Dec;21(4):719-728.\u003c/li\u003e\n \u003cli\u003eAl Dehaini DMB, Al-Bustan SA, Malalla ZHA, et al. The influence of TERC, TERT and ACYP2 genes polymorphisms on plasma telomerase concentration, telomeres length and T2DM. Gene. 2021;766:145127.\u003c/li\u003e\n \u003cli\u003eAl-Kuraishy HM, Al-Gareeb AI, Alexiou A, et al. Metformin and growth differentiation factor 15 (GDF15) in type 2 diabetes mellitus: A hidden treasure. J Diabetes. 2022;14:806-814.\u003c/li\u003e\n \u003cli\u003eAdela R, Banerjee SK. GDF-15 as a Target and Biomarker for Diabetes and Cardiovascular Diseases: A Translational Prospective. J Diabetes Res. 2015;2015:490842.\u003c/li\u003e\n \u003cli\u003eSanchez M, Kannengiesser C, Hoang S, et al. Leukocyte telomere length, allelic variations in related genes and risk of coronary heart disease in people with long-standing type 1 diabetes. Cardiovasc Diabetol. 2022 Oct 11;21(1):206.\u003c/li\u003e\n \u003cli\u003eAl-Dehaini DMB, Al-Bustan SA, Malalla ZHA, et al. Analogous telomeres shortening and different metabolic profile: hypertension versus hypertension/type 2 diabetes mellitus comorbidity. Cardiovasc Endocrinol Metab. 2020 Sep 3;10(2):106-112.\u003c/li\u003e\n \u003cli\u003eGoswami A, Huda N, Yasmin T, et al. Association study of leukocyte telomere length and genetic polymorphism within hTERT promoter with type 2 diabetes in Bangladeshi population. Mol Biol Rep. 2021 Jan;48(1):285-295.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5129243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5129243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eBackground\u003c/u\u003e: Differential diagnosis of the various manifestations of ischaemic heart disease can be difficult, especially in the young, with many investigations being relevant. We hypothesised that expression of the genes for Growth Differentiation Factor 15 (\u003cem\u003eGDF15\u003c/em\u003e) and Telomerase Reverse Transcriptase (\u003cem\u003eTERT\u003c/em\u003e) have a place in the diagnosis of an acute coronary artery disease event in those aged up to 55 years with existing coronary artery disease. Venous blood was obtained from 53 patients (27 with diabetes) presenting with an acute coronary syndrome and subsequently shown to have coronary artery disease, and from 46 age and sex matched controls free of cardiovascular disease and its risk factors. Relative expression of leukocyte transcriptome \u003cem\u003eGAPDH,\u003c/em\u003e \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003ewere determined by real-time polymerase chain reaction and quantified by quantitation-comparative Ct (ΔCt).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eResults:\u003c/u\u003e Compared to controls, mean (95% CI) relative expression of \u003cem\u003eGDF15\u003c/em\u003e mRNA in the patients was 1.38 (1.13-1.49) (p\u0026lt;0.001), and of \u003cem\u003eTERT\u003c/em\u003e was 1.12 (1.04-1.20) p=0.003), with \u003cem\u003eGDF15 \u003c/em\u003ebeing greater than that of \u003cem\u003eTERT\u003c/em\u003e(p\u0026lt;0.001). There was no difference in relative \u003cem\u003eGDF15\u003c/em\u003e expression in 26 patients free of diabetes (1.6 [1.42-1.78]) versus the 27 patients with diabetes (1.6 [1.29-1.91]) (p=0.996), and no difference in relative \u003cem\u003eTERT\u003c/em\u003eexpression in patients free of diabetes (1.19 [1.06-1.33]) compared to those with diabetes (1.25 [0.98-1.50]) (p=0.739).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConclusions\u003c/u\u003e: Compared to healthy controls, \u003cem\u003eGDF15\u003c/em\u003e and \u003cem\u003eTERT\u003c/em\u003e expressions are both increased in coronary artery disease and in coronary artery disease+diabetes, with no difference between the patient groups. These genes may have roles in the diagnosis and pathogenesis of acute coronary artery disease.\u003c/p\u003e","manuscriptTitle":"Diagnostic value of the gene expression of Growth Differentiation Factor 15 and Telomerase Reverse Transcriptase in middle-aged patients with acute coronary artery disease: a pilot case-control study ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 03:11:47","doi":"10.21203/rs.3.rs-5129243/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":"87734c17-0bc5-4fde-a45e-d077553b2a85","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T03:11:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 03:11:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5129243","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5129243","identity":"rs-5129243","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}