Comparing Left Ventricular Mechanical Dyssynchrony between Diabetic and Non-Diabetic Patients with Normal Gated SPECT MPI

Comparing Left Ventricular Mechanical Dyssynchrony between Diabetic and Non-Diabetic Patients with Normal Gated SPECT MPI | 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 Comparing Left Ventricular Mechanical Dyssynchrony between Diabetic and Non-Diabetic Patients with Normal Gated SPECT MPI Elinaz Hosseinzadeh, Mohammadali Ghodsirad, Toktam Alirezaei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-497675/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2021 Read the published version in The International Journal of Cardiovascular Imaging → Version 1 posted 6 You are reading this latest preprint version Abstract Introduction; the aim of this study was to employ phase analysis to diagnose left ventricular mechanical dyssynchrony (LVMD)in asymptomatic patients with diabetes mellitus type 2 and normal perfusion study to prevent diabetic cardiomyopathy. Methods & materials; Ninety-three consecutive patients with known type 2 diabetes and 81 age- and gender- matched patients without diabetes who were candidates for SPECT-MPI were considered as the control group. The presence of LVMD as an indicator of cardiomyopathy- was determined using phase analysis for each scan with quantitative gated SPECT (QGS) and corridor4DM (4DM) software. All outcomes such as phase bandwidth (PBW) and phase standard deviation (PSD) were compared between the two groups. Results; A total of 174 patients were included in the study. There were no statistically significant difference regarding demographic factors between the two groups (P>0.05). PBW showed statistically significant differences (increased in diabetics) between the control and diabetic patients (P < 0.05). Kruskal Wallis analysis revealed that as the duration of diabetes is prolonged, especially more than 15 years, the probability of LVMD is increased as well (p=0.021). Discussion; Fraction of asymptomatic diabetic patients with normal ejection fraction and gated SPECT MPI-especially those with prolonged diabetes- might have some degrees of LVMD. Phase analysis can detect this which in turn would prevent progress into heart failure. Cardiac & Cardiovascular Systems Diabetes mellitus Cardiomyopathy Phase analysis SPECT-MPI phase standard deviation phase bandwidth LVMD Figures Figure 1 Figure 2 Figure 3 Introduction Diabetes is the most common human metabolic disease and its incidence is increasing in different societies ( 1 – 3 ). Cardiovascular complications include cardiomyopathies, ischemia, or asymptomatic infarction or finally heart failure. Diabetic cardiomyopathy (DCM) was first described by Rubler et al. indicating a wide range of pathologic changes in the myocardium of diabetic patients ( 4 , 5 ). Underlying reasons for diabetic cardiomyopathy include increased oxidative stress, mitochondrial dysfunction, impairment in calcium handling and extracellular matrix remodeling which lead to a defect in myocardial contractility and finally DCM progression ( 6 , 7 ). Nonetheless, the exact mechanisms are not well elucidated. According to the literature the prevalence of DCM has been reported around 12% in patients with diabetes which could increase up to 22% in elderly patients. DCM would lead to a preclinical left ventricular diastolic dysfunction, which might develop to a clinical heart failure without any evident coronary artery disease or valvular involvement ( 8 – 10 ). Korosoglou et al. described left ventricular mechanical dyssynchrony (LVMD) in patients with DM even without any signs for LV diastolic dysfunction. They finally indicated that LVMD could occur as the first pathologic changes during the course of diabetic cardiomyopathy ( 11 ). Prevention and early diagnosis of cardiovascular disease in the early stages can reduce mortality and late complications such as heart failure. On the other hand, myocardial perfusion scan is a fully approved, standardized and accurate tool for examining myocardial function and diagnosing heart diseases, which is widely used in both diagnosis and prognosis of patients. Besides, its irradiation is very low. The diagnostic value of myocardial perfusion scan has been confirmed in many conditions, in diabetics, or those undergoing cardiac interventions (PCI or surgery) ( 12 – 14 ). The accuracy of myocardial perfusion scans is quite comparable to other diagnostic modalities and has a high sensitivity and specificity of about 90%. The prognostic value of cardiac scanning has been studied in many studies and if the result of cardiac scan is abnormal, the patient's prognosis will be worse. Moreover, it has also been shown that the treatment planning can be determined based on the results of myocardial perfusion scans ( 15 , 16 ). Diabetic patients who have risk indicators can be identified and treatment can be started faster before irreversible changes. The mortality of these patients can be reduced ( 17 ). Phase analysis is a computerized reproducible technique which is applied through different software to traditional gated SPECT MPI and provides incremental value to myocardial perfusion studies. It exploits the partial volume effect and Fourier first harmonics to evaluate the onset of myocardial contraction at different myocardial locations thereby determining myocardial synchrony. Type of gamma camera, reconstruction algorithm or injected dosage has no effect on the phase analysis( 18 ). Phase analysis can detect early signs of dyssynchrony in diabetic population before there is visual regional myocardial contractility dysfunction and can prevent diabetic cardiomyopathy( 19 ). Therefore, the aim of this study was to employ phase analysis to diagnose left ventricular mechanical dyssynchrony (LVMD) in asymptomatic patients with diabetes mellitus type 2 and normal perfusion study .This would help early diagnosis of patients who are prone to diabetic cardiomyopathy. Methods And Materials Patient selection This was a case control descriptive study. Ninety-three consecutive patients with known type 2 diabetes who referred to the Cardiovascular Clinic of Shohada Tajrish Hospital to undergo cardiac perfusion scans for preoperative evaluations were included. Inclusion criteria were patients aged between 35 and 80 years, systolic function above 55% in echocardiography and life expectancy of more than one year. Exclusion criteria were a history of valvular heart disease, ischemic heart disease, abnormal EKG, abnormal renal or hepatic failure, coronary intervention, arrhythmia, and perfusion defect and wall motion abnormality in gated stress SPECT-MPI. Moreover, 81 age- and gender- matched patients without diabetes who were candidates for myocardial perfusion scans were considered as the control group. The demographic characteristics of participants were recorded in a questionnaire. Gated SPECT MPI : All patients underwent a myocardial perfusion scan in two stages of rest and stress (two day protocol) with Dipyridamole /exercise testing. Myocardial perfusion scan technique was as follows; after injecting 740 MBq of Tc99m-Sestamibi, the patient was scanned with a gamma camera in supine and prone positions using the Gated SPECT method based on the American Society of Nuclear Cardiology (ASNC) guidelines (Collimator: low energy high resolution, Matrix: 64×64, Orbit: 180°, 32 frame from RAO till LPO, 15% window centered on 140-KeV photopeak, Gated: 8 frame per cardiac cycle: acceptance window:20%, Reconstruction with iterative metode,3D flash algorithm) The gamma camera used was the Evo-Voxel Siemens with Variable Angle Dual Heads. Interpretation : Patient scans were then interpreted by a nuclear medicine specialist.. All studies were checked for presence of perfusion defects both visually and semi-quantitatively using 4DM and QPS software and patients were perfusion defects were excluded. Then all studies were checked with QGS and 4DM software and mechanical systolic and diastolic function indices of the left ventricle were extracted. Severity of left ventricular dyssynchrony was determined based on phase analysis for each scan and assessment of phase standard deviation (PSD, unit: degree), peak phase, mean phase and phase bandwidth (PBW, unit: degree) which is defined as 95% of the histogram width that includes practically full phase bandwidth with the exclusion of likely outlier values. For outcome analysis, only PSD and PBW were used as quantitative indices of the LVMD. Statistical analysis Descriptive data was reported as mean and standard deviation for quantitative variables and number and percentage for qualitative variables. A T-test was used to compare the means between the two groups. For all tests, p value below 0.05 was considered as statistically significant. SPSS software (SPSS Inc., Chicago, Il, The USA) was used for data analysis. Kruskal Wallis analysis was also deployed to determine any probable relation between duration of DM and progression into LVDM. A written informed consent was obtained from all patients before entering the study. The study protocol was approved by the ethics committee of Shahid Beheshti University of Medical Sciences. Results A total of 174 patients (141 female) with a mean age of 58.50 ± 7.37 years (range: 36–80) were included in the study. There were 93 patients with diabetes mellitus type 2 in the case group with a mean age of 59.86 ± 8.63 years (77 females), and 81 age- and sex-matched non-diabetic individuals with mean age of 56.95 ± 6.31 years (64 female) in the control group. There were no statistically significant differences regarding demographic factors between the two groups (P > 0.05). CAD risk factors such as hypertension and dyslipidemia were more common in the case group compared to the controls (P = 0.002, p = 0.001). Mean duration of diabetes in patients was 8.56 ± 4.50 years. The baseline and clinical characteristics of the study participants are presented in Table 1 . Table 1 Demographic Characteristics of the Participants Variable DM (n = 93) Non-DM (n = 81) P-value * N (%) N (%) Gender Female 77 (82.79) 64 (79.01) 0.11 Male 16 (17.20) 17 (20.98) Total 93 (100) 81 (100) HTN Yes 31 (34.44) 47 (58.02) 0.002 No 59 (65.56) 34 (41.98) Total 90 (100) 81 (100) Duration of Diabetes < 5 years 27 (29.03) - - ≥ 5 years 66 (70.96) - Total 93 (100) - Smoking Yes 9 (10.11) 16 (19.75) 0.07 No 80 (89.89) 65 (80.25) Total 89 (100) 81 (100) HLP Yes 60 (64.51) 18 (22.22) < 0.001 No 33 (35.48) 63 (77.78) Total 93 (100) 81 (100) Type of stress Treadmill 17 (18.27) 16 (18.76) 0.78 DIP 76 (81.72) 65 (81.24) Total 93 (100) 81 (100) *Based on Chi-Square Test; HTN = hypertension; HLP = high lipid profit; DIP = dipyridamole. According to some previous studies, obtained values of QGS (QGS Mean 30.9–80.2, PBW QGS 24, SD 8.6, Entropy 31) and 4DM (Mean 4DM 6.1–10.2, PBW 24, PSD 9.4) were used as a cutoff value for the diagnosis of LVMD (left ventricular myocardial dyssynchrony)( 20 ). Considering PBW in QGS software, LVMD was detected in 8% of diabetics and 16% of non-diabetics (not statistically significant). In 4Dm software these measures were 23.9% and 8.6% for diabetics and non-diabetics respectively. Except for PBW and mean phase in of 4DM software, no statistically significant difference was observed between control and diabetic patients (P < 0.05) (Table 2 ). Based on Pearson's test poor correlation exists between findings of QGS and 4DM software (p = 0.04, r = 0.2). Moreover, the duration of DM was divided into four groups : below 5 years (26 patients), between 5–10 (34), between 10–15 ( 17 ) and more than 15 years ( 16 ). Kruskal Wallis analysis to assess the impact of duration of DM revealed that as the duration of diabetes is prolonged, especially more than 15 years, the probability of LVMD [PBW in both QGS and 4DM] is increased as well (p = 0.021). However, there were no statistical significant differences between diabetic patients below 15 years, and non-diabetic patients (Figs. 1).Samples of normal and LVDM are shown in Figs. 2 and 3 respectively. EDVs were in the range of 16 to 137 ml in control group compared to 6 to 95 ml for both diabetic patients and diabetic patients with LVMD, respectively. ESV ranges were 1 to 72, 0 to 40 and 10 to 42 ml for controls, diabetic patients without and with LVMD, respectively. However, no significant differences were observed for quantitative parameters (LVEF, EDV, ESV and LV mass) between these groups (P > 0.05). Univariate linear regression analysis showed an association between higher EDV-ESV and lower ejection fraction (EF) and presence of diabetes, obesity, smoking and older age (Table 2 ). Table 2 Distribution of Demographic and Clinical Characteristics in Diabetic and Non-Diabetic Patients Variable All (n = 174) DM (n = 93) Non-DM (n = 81) P-value * Mean (SD) Mean (SD) Mean (SD) Age (year) 58.50 (10.37) 59.86 (8.64) 56.95 (11.31) 0.21 Weight (kg) 76.34 (13.7) 77.32 (13.48) 75.23 (13.96) 0.35 Median (IQR) Median (IQR) Median (IQR) P-value ** QGS PBW 18 (12) 18 (9) 18 (12) 0.25 Entropy 21 (13) 20 (13.5) 23 (15) 0.17 PSD 3.7 (2.7) 3.35 (2.55) 3.9 (2.8) 0.27 mean 143 (22.2) 146.85 (22.95) 142 (19.8) 0.08 PFR 3.1 (1.57) 3.04 (2) 3.21 (1.37) 0.3 PFR2 1.52 (2.97) 1.85 (3.38) 0.91 (2.35) 0.11 MFR3 1.33 (0.82) 1.32 (0.93) 1.33 (0.61) 0.58 TTPF 163 (84.5) 152 (91) 171 (78) 0.23 PER -4.33 (0.99) -4.41 (0.98) -4.24 (1.01) 0.25 EDV 49 (27) 45.5 (24) 53 (30) 0.03 ESV 13 (12) 11 (9) 15 (13) 0.01 EF 75 (14) 76 (12) 73 (15) 0.02 4DM Mean 50.2 (14.6) 51.4 (13.9) 47.6 (14.4) 0.009 SD 7.32(3.1) 7.75(2.8) 6.85(3.3) 0.14 PBW 26 (16) 28 (17) 24 (12) 0.02 * Based on t-test; ** Based on Mann-Whitney test; IQR = Interquarter Range; QGS = Quantitative Gated SPECT; PBW = Phase Bandwidth; PSD = Phase Bandwidth; PFR = Peak Filling Rate; PFR2 = Second Pick Filling Rate; MFR3 = Mean Filling Rate in the first 1/3 of the diastole; TTPF = Time to Peak Filling Rate; PER = Peak Ejection Rate; EDV= End Diastolic Volume; ESV = End Systolic Volume; EF = Ejection Fraction Mean duration of DM was also higher in diabetic patients with LVMD compared to those without LVMD (P < 0.05). Discussion To our knowledge there are very few studies assessing LVMD in patients with type 2 diabetes with no defects in perfusion scan. In our study, LVMD was detected in 23% of patients with type II DM which is slightly lower than the rate reported by Dharmender et al. ( 19 ). These patients had LVDM without any defect in scan or ECG abnormalities, which highlights the importance of phase analysis in cardiac perfusion scan to detect LVDM. Moreover, LVDM was more found in those patients with diabetes mellitus of more than 15 years, which is consistent with many other studies showing increased risk of cardiac events with prolonged diabetes mellitus ( 21 – 23 ). Cardiac perfusion scan and the phase analysis provided a very useful tool to predict cardiac events in the future by revealing LVDM in our study. As seen there was a meaningful statistical difference regarding 4DM PBW, between the control and diabetic patients. This is in accordance with the Dharmender investigation, which claimed that phase analysis is a very helpful technique to predict LVMD without any additional study/radiation dose to the patients ( 19 ). Divergences(increased PBW) in left ventricular contractibility has been mentioned as the main pathophysiology of LVMD in diabetic patients in the recent studies ( 24 ). Many evidences indicate that LVMD is an independent risk factor for further cardiac complications in the diabetic patients ( 25 ), therefore, it necessitates detection of LVMD in patients by phase analysis. Despite the fact, phase analysis ability would be much more than the current definition of LVMD based on QRS width, as 12.5% of our patients had a normal QRS width but an abnormal phase analysis. Hence, it seems that phase analysis is more accurate for these patients who might be candidates for a resynchronization therapy. On the other hand, phase analysis is more accessible and easily performed compared to other evolving modalities such as Magnetic Resonance Imaging or newly discovered biomarkers, as there are very few evidence to support their values to predict further cardiac events. However, cardiac perfusion scan and phase analysis is performed faster, less affected by inhalation and beat variability. Moreover, it offers an automated evaluation and is less operator dependent. Mahfouz et al in a recent 2020 study evaluated resting left ventricular dyssynchrony and mechanical reserve in asymptomatic normotensive subjects with early type 2 diabetes mellitus. They concluded that The LV dyssynchrony was a strong predictor of defective LV mechanical reserve and could be considered as a primary marker to reveal subclinical cardiac dysfunction in patients with asymptomatic diabetes mellitus with a normal LVEF. They also claimed that HbA1c, high-sensitivity C-reactive protein and left atrial volume index were inversely correlated with LVMR. This is in line with our study to show the importance of early detection of ventricular dyssynchrony in early stages to detect those diabetic patients who would benefit from early resynchronization therapy ( 25 ). Other investigations also indicated some evidences of LVMD in asymptomatic diabetic patients ( 11 ). Early diagnosis of LVMD in asymptomatic diabetic patients helps to prevent dilated cardiomyopathy and irreversible fibrotic changes, which enhance the life expectancy of patients to a great extent. Some other studies also found an association between obesity or hyperlipidemia and LVMD, which is consistent with our findings ( 26 ). These have been rarely evaluated in previous studies and could be considered as a novelty of our investigation. However, more studies are necessary to find the exact role of patient confounding factors in the progression of LVMD. We had some limitations in performing the study. We would like to perform perfusion scans 2–3 times more to evaluate changes in phase analysis parameters, especially in those who underwent early resynchronization therapy. Despite the fact, it was not possible due to limitations in expenses and time. However, it is recommended to be considered in further studies. Furthermore, the exact effects of some confounding factors such as underlying diseases, age, or smoking was not assessed here. It is recommended to be evaluated in larger prospective clinical trials to clarify the exact role of underlying patients’ conditions. However, it would help to better understand the pathophysiology of LVMD in asymptomatic diabetic patients. We found poor correlation between 4DM and QGS software. Difference between PBW in diabetics and non-diabetics was only statistically significant in 4DM and QGS seemed insensitive to access some dyssynchrony. These findings were noted in some studies before ( 27 , 28 ), however some other studies found 4DM and QGS both helpful in detection of LVDM ( 29 , 30 ). Conclusion Asymptomatic diabetics, especially those with prolonged diabetes, with normal myocardial perfusion might have some degrees of LVMD, which might progress to dilated cardiomyopathy if not considered appropriately. Phase analysis helped us to detect this in our study. These patients might benefit from different preventive or therapeutic options like better glycemic control or resynchronization therapy to avoid unwanted and irreversible cardiac events and enhance their overall life expectancy. We recommend that phase analysis should be done for all patients for early diagnosis of LVDM. Declarations Acknowledgment We would like to thank the staff of nuclear medicine ward to help collecting relevant data. Funding and support None Financial disclosure None Conflict of interest No conflict of interest. References Mellitus D (2005) Diagnosis and classification of diabetes mellitus. Diabetes Care 28(S37):S5–S10 Mellitus D (2006) Diagnosis and classification of diabetes mellitus. Diabetes Care 29:S43 Moghaddam MG, Ansari I, Roghani M, Moradi M. The Effects of Origanum Majoranaon Oxidative Stress and Histopathology of Renal Tissue among Streptozotocin-Induced Diabetic Rats. 2013 Vered Z, Battler A, Segal P, Liberman D, Yerushalmi Y, Berezin M et al (1984) Exercise-induced left ventricular dysfunction in young men with asymptomatic diabetes mellitus (diabetic cardiomyopathy). The American journal of cardiology 54(6):633–637 Hamby RI, Zoneraich S, Sherman L (1974) Diabetic cardiomyopathy. Jama 229(13):1749–1754 Zarich SW, Nesto RW (1989) Diabetic cardiomyopathy. 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EJNMMI research 7(1):1–9 Okuda K, Nakajima K, Matsuo S, Soichiro K, Yoneyama H, Shibutani T et al (2017) Evaluation of left ventricular dyssynchrony by phase analysis in gated myocardial perfusion SPECT: Comparison of four software programs. J Nucl Med 58(supplement 1):231- Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2021 Read the published version in The International Journal of Cardiovascular Imaging → Version 1 posted Editor invited by journal 24 Jun, 2021 Reviews received at journal 09 May, 2021 Reviewers invited by journal 09 May, 2021 Editor assigned by journal 08 May, 2021 First submitted to journal 08 May, 2021 Editorial decision: Major revisions 06 May, 2021 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-497675","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":26010102,"identity":"b11b6bfa-f891-45f1-ab2d-97697569b3db","order_by":0,"name":"Elinaz Hosseinzadeh","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elinaz","middleName":"","lastName":"Hosseinzadeh","suffix":""},{"id":26010103,"identity":"0ffc0347-e588-47b9-bfbc-b31a6eef3df8","order_by":1,"name":"Mohammadali Ghodsirad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACCRAyADIOMDA+YGyAiiYQqYXZgAQtDGAtbBJwLfiAfHTzwZs/Cu7J8107/Kzy545tiQ3shx8wPNyDW4vhnWPJ1jwGxYYzb6eZ3eY9czuxgSfNgCHhGR4tM3LMpBkMEhg33E4wu83YBtTCkAP0ywH8WiR/GCTYb7id/q3wJ0gL/xv8WuQlcswkeAwSEjfczjFj4AVpkSBgi4FEGsgvCckzb+cUSwP9Ytwm8czgAF5bZiQDQ+xPgm3f7fSNH3/uuC3bz5/88OEPfLZgyLExgOIID5BvwCc7CkbBKBgFowAEAMJVWT5YDkRVAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3557-8641","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohammadali","middleName":"","lastName":"Ghodsirad","suffix":""},{"id":26010104,"identity":"f7638582-f7b0-43dc-92de-ccfc93fef375","order_by":2,"name":"Toktam Alirezaei","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Toktam","middleName":"","lastName":"Alirezaei","suffix":""},{"id":26010105,"identity":"bc7700c8-fcfd-4ebf-9e4a-4e1692c43958","order_by":3,"name":"Maryam Arefnia","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Arefnia","suffix":""},{"id":26010106,"identity":"6291883f-4ac5-4491-a8c5-61046b839edc","order_by":4,"name":"Elahe Pirayesh","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elahe","middleName":"","lastName":"Pirayesh","suffix":""},{"id":26010107,"identity":"381e767f-2810-41b9-934a-7bd2c1adab88","order_by":5,"name":"Mahasti Amoui","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahasti","middleName":"","lastName":"Amoui","suffix":""},{"id":26010108,"identity":"2f29f055-0023-4cc9-9fe8-c077fb336249","order_by":6,"name":"Ghazal Norouzi","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ghazal","middleName":"","lastName":"Norouzi","suffix":""}],"badges":[],"createdAt":"2021-05-06 03:26:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-497675/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-497675/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10554-021-02358-1","type":"published","date":"2021-07-27T15:00:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9353676,"identity":"96fd5955-e860-4622-a62a-a764828f0369","added_by":"auto","created_at":"2021-05-19 18:39:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151150,"visible":true,"origin":"","legend":"LVMD in Different Diabetic Groups, figures in vertical axis shows number of patients, color bars indicates duration of diabetes in year.; LVDM=Left Ventricular Mechanical Dyssynchrony","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-497675/v1/a35f8d8d1f6c0ec5a1b931e6.png"},{"id":9353619,"identity":"3a33f008-4af8-4934-8e23-64b276c0a286","added_by":"auto","created_at":"2021-05-19 18:36:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":866258,"visible":true,"origin":"","legend":"Sample of synchronous LV contraction. 4Dm (left) and QGS (right) both show normal Phase analysis. Measured bandwidth (PBW) in both software are within normal limits (calculated BW is 30 for 4DM and 12 for QGS); QGS=quantitative gated SEPCT","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-497675/v1/fe092e291ae1a851dae27b5e.png"},{"id":9353617,"identity":"d7902a4c-9916-499f-b5a6-c791d94ae1bc","added_by":"auto","created_at":"2021-05-19 18:36:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":866591,"visible":true,"origin":"","legend":"Sample of dyssynchronous LV contraction. The BW is significantly increased in 4DM (measured BW is 52); apex and septum show out of order contraction. In QGS, PBW is moderately increased (measured BW is 24 and anterior wall seems to have mild dyssynchrony. ","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-497675/v1/3fbf8124e97be3eff32f4283.png"},{"id":13693760,"identity":"333b3aee-ee98-4ff4-9649-8a3684c7c040","added_by":"auto","created_at":"2021-09-17 12:49:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1923041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-497675/v1/ff57bcf1-ae86-47d3-8080-630547489d82.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eComparing Left Ventricular Mechanical Dyssynchrony between Diabetic and Non-Diabetic Patients with Normal Gated SPECT MPI\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eDiabetes is the most common human metabolic disease and its incidence is increasing in different societies (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Cardiovascular complications include cardiomyopathies, ischemia, or asymptomatic infarction or finally heart failure. Diabetic cardiomyopathy (DCM) was first described by Rubler et al. indicating a wide range of pathologic changes in the myocardium of diabetic patients (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderlying reasons for diabetic cardiomyopathy include increased oxidative stress, mitochondrial dysfunction, impairment in calcium handling and extracellular matrix remodeling which lead to a defect in myocardial contractility and finally DCM progression (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Nonetheless, the exact mechanisms are not well elucidated. According to the literature the prevalence of DCM has been reported around 12% in patients with diabetes which could increase up to 22% in elderly patients. DCM would lead to a preclinical left ventricular diastolic dysfunction, which might develop to a clinical heart failure without any evident coronary artery disease or valvular involvement (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Korosoglou et al. described left ventricular mechanical dyssynchrony (LVMD) in patients with DM even without any signs for LV diastolic dysfunction. They finally indicated that LVMD could occur as the first pathologic changes during the course of diabetic cardiomyopathy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevention and early diagnosis of cardiovascular disease in the early stages can reduce mortality and late complications such as heart failure. On the other hand, myocardial perfusion scan is a fully approved, standardized and accurate tool for examining myocardial function and diagnosing heart diseases, which is widely used in both diagnosis and prognosis of patients. Besides, its irradiation is very low. The diagnostic value of myocardial perfusion scan has been confirmed in many conditions, in diabetics, or those undergoing cardiac interventions (PCI or surgery) (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe accuracy of myocardial perfusion scans is quite comparable to other diagnostic modalities and has a high sensitivity and specificity of about 90%. The prognostic value of cardiac scanning has been studied in many studies and if the result of cardiac scan is abnormal, the patient's prognosis will be worse. Moreover, it has also been shown that the treatment planning can be determined based on the results of myocardial perfusion scans (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Diabetic patients who have risk indicators can be identified and treatment can be started faster before irreversible changes. The mortality of these patients can be reduced (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Phase analysis is a computerized reproducible technique which is applied through different software to traditional gated SPECT MPI and provides incremental value to myocardial perfusion studies. It exploits the partial volume effect and Fourier first harmonics to evaluate the onset of myocardial contraction at different myocardial locations thereby determining myocardial synchrony. Type of gamma camera, reconstruction algorithm or injected dosage has no effect on the phase analysis(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Phase analysis can detect early signs of dyssynchrony in diabetic population before there is visual regional myocardial contractility dysfunction and can prevent diabetic cardiomyopathy(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to employ phase analysis to diagnose left ventricular mechanical dyssynchrony (LVMD) in asymptomatic patients with diabetes mellitus type 2 and normal perfusion study .This would help early diagnosis of patients who are prone to diabetic cardiomyopathy.\u003c/p\u003e "},{"header":"Methods And Materials","content":" \u003cp\u003e \u003cstrong\u003ePatient selection\u003c/strong\u003e \u003cp\u003eThis was a case control descriptive study. Ninety-three consecutive patients with known type 2 diabetes who referred to the Cardiovascular Clinic of Shohada Tajrish Hospital to undergo cardiac perfusion scans for preoperative evaluations were included. Inclusion criteria were patients aged between 35 and 80 years, systolic function above 55% in echocardiography and life expectancy of more than one year. Exclusion criteria were a history of valvular heart disease, ischemic heart disease, abnormal EKG, abnormal renal or hepatic failure, coronary intervention, arrhythmia, and perfusion defect and wall motion abnormality in gated stress SPECT-MPI. Moreover, 81 age- and gender- matched patients without diabetes who were candidates for myocardial perfusion scans were considered as the control group. The demographic characteristics of participants were recorded in a questionnaire.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGated SPECT MPI\u003c/b\u003e: All patients underwent a myocardial perfusion scan in two stages of rest and stress (two day protocol) with Dipyridamole /exercise testing. Myocardial perfusion scan technique was as follows; after injecting 740 MBq of Tc99m-Sestamibi, the patient was scanned with a gamma camera in supine and prone positions using the Gated SPECT method based on the American Society of Nuclear Cardiology (ASNC) guidelines (Collimator: low energy high resolution, Matrix: 64\u0026times;64, Orbit: 180\u0026deg;, 32 frame from RAO till LPO, 15% window centered on 140-KeV photopeak, Gated: 8 frame per cardiac cycle: acceptance window:20%, Reconstruction with iterative metode,3D flash algorithm) The gamma camera used was the Evo-Voxel Siemens with Variable Angle Dual Heads.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInterpretation\u003c/b\u003e: Patient scans were then interpreted by a nuclear medicine specialist.. All studies were checked for presence of perfusion defects both visually and semi-quantitatively using 4DM and QPS software and patients were perfusion defects were excluded. Then all studies were checked with QGS and 4DM software and mechanical systolic and diastolic function indices of the left ventricle were extracted. Severity of left ventricular dyssynchrony was determined based on phase analysis for each scan and assessment of phase standard deviation (PSD, unit: degree), peak phase, mean phase and phase bandwidth (PBW, unit: degree) which is defined as 95% of the histogram width that includes practically full phase bandwidth with the exclusion of likely outlier values. For outcome analysis, only PSD and PBW were used as quantitative indices of the LVMD.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical analysis\u003c/strong\u003e \u003cp\u003eDescriptive data was reported as mean and standard deviation for quantitative variables and number and percentage for qualitative variables. A T-test was used to compare the means between the two groups. For all tests, p value below 0.05 was considered as statistically significant. SPSS software (SPSS Inc., Chicago, Il, The USA) was used for data analysis. Kruskal Wallis analysis was also deployed to determine any probable relation between duration of DM and progression into LVDM.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e A written informed consent was obtained from all patients before entering the study. The study protocol was approved by the ethics committee of Shahid Beheshti University of Medical Sciences.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003eA total of 174 patients (141 female) with a mean age of 58.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37 years (range: 36\u0026ndash;80) were included in the study. There were 93 patients with diabetes mellitus type 2 in the case group with a mean age of 59.86\u0026thinsp;\u0026plusmn;\u0026thinsp;8.63 years (77 females), and 81 age- and sex-matched non-diabetic individuals with mean age of 56.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.31 years (64 female) in the control group. There were no statistically significant differences regarding demographic factors between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). CAD risk factors such as hypertension and dyslipidemia were more common in the case group compared to the controls (P\u0026thinsp;=\u0026thinsp;0.002, p\u0026thinsp;=\u0026thinsp;0.001). Mean duration of diabetes in patients was 8.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50 years. The baseline and clinical characteristics of the study participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of the Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDM (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-DM (n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (82.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (79.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (17.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (20.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (34.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (58.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (65.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (41.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDuration of Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (29.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (70.96)\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=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (100)\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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (10.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (19.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (89.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (80.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (64.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (22.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (35.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (77.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreadmill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (18.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (81.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (81.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Based on Chi-Square Test; HTN\u0026thinsp;=\u0026thinsp;hypertension; HLP\u0026thinsp;=\u0026thinsp;high lipid profit; DIP\u0026thinsp;=\u0026thinsp;dipyridamole.\u003c/p\u003e \u003cp\u003eAccording to some previous studies, obtained values of QGS (QGS Mean 30.9\u0026ndash;80.2, PBW QGS 24, SD 8.6, Entropy 31) and 4DM (Mean 4DM 6.1\u0026ndash;10.2, PBW 24, PSD 9.4) were used as a cutoff value for the diagnosis of LVMD (left ventricular myocardial dyssynchrony)(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Considering PBW in QGS software, LVMD was detected in 8% of diabetics and 16% of non-diabetics (not statistically significant). In 4Dm software these measures were 23.9% and 8.6% for diabetics and non-diabetics respectively. Except for PBW and mean phase in of 4DM software, no statistically significant difference was observed between control and diabetic patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on Pearson's test poor correlation exists between findings of QGS and 4DM software (p\u0026thinsp;=\u0026thinsp;0.04, r\u0026thinsp;=\u0026thinsp;0.2).\u003c/p\u003e \u003cp\u003eMoreover, the duration of DM was divided into four groups : below 5 years (26 patients), between 5\u0026ndash;10 (34), between 10\u0026ndash;15 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and more than 15 years (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Kruskal Wallis analysis to assess the impact of duration of DM revealed that as the duration of diabetes is prolonged, especially more than 15 years, the probability of LVMD [PBW in both QGS and 4DM] is increased as well (p\u0026thinsp;=\u0026thinsp;0.021). However, there were no statistical significant differences between diabetic patients below 15 years, and non-diabetic patients (Figs.\u0026nbsp;1).Samples of normal and LVDM are shown in Figs.\u0026nbsp;2 and 3 respectively.\u003c/p\u003e \u003cp\u003eEDVs were in the range of 16 to 137 ml in control group compared to 6 to 95 ml for both diabetic patients and diabetic patients with LVMD, respectively. ESV ranges were 1 to 72, 0 to 40 and 10 to 42 ml for controls, diabetic patients without and with LVMD, respectively. However, no significant differences were observed for quantitative parameters (LVEF, EDV, ESV and LV mass) between these groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eUnivariate linear regression analysis showed an association between higher EDV-ESV and lower ejection fraction (EF) and presence of diabetes, obesity, smoking and older age (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Demographic and Clinical Characteristics in Diabetic and Non-Diabetic Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;174)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDM (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-DM (n\u0026thinsp;=\u0026thinsp;81)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.50 (10.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.86 (8.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.95 (11.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.34 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.32 (13.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.23 (13.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQGS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35 (2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.85 (22.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 (1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85 (3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.33 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.41 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.24 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.5 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.03\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.02\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4DM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.2 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.6 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.32(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.75(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.85(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e0.02\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Based on t-test; ** Based on Mann-Whitney test; IQR\u0026thinsp;=\u0026thinsp;Interquarter Range;\u003c/p\u003e \u003cp\u003eQGS\u0026thinsp;=\u0026thinsp;Quantitative Gated SPECT; PBW\u0026thinsp;=\u0026thinsp;Phase Bandwidth; PSD\u0026thinsp;=\u0026thinsp;Phase Bandwidth;\u003c/p\u003e \u003cp\u003ePFR\u0026thinsp;=\u0026thinsp;Peak Filling Rate; PFR2\u0026thinsp;=\u0026thinsp;Second Pick Filling Rate; MFR3\u0026thinsp;=\u0026thinsp;Mean Filling Rate in the first\u003c/p\u003e \u003cp\u003e1/3 of the diastole; TTPF\u0026thinsp;=\u0026thinsp;Time to Peak Filling Rate; PER\u0026thinsp;=\u0026thinsp;Peak Ejection Rate; EDV=\u003c/p\u003e \u003cp\u003eEnd Diastolic Volume; ESV\u0026thinsp;=\u0026thinsp;End Systolic Volume; EF\u0026thinsp;=\u0026thinsp;Ejection Fraction\u003c/p\u003e \u003cp\u003e Mean duration of DM was also higher in diabetic patients with LVMD compared to those without LVMD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eTo our knowledge there are very few studies assessing LVMD in patients with type 2 diabetes with no defects in perfusion scan. In our study, LVMD was detected in 23% of patients with type II DM which is slightly lower than the rate reported by Dharmender et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These patients had LVDM without any defect in scan or ECG abnormalities, which highlights the importance of phase analysis in cardiac perfusion scan to detect LVDM. Moreover, LVDM was more found in those patients with diabetes mellitus of more than 15 years, which is consistent with many other studies showing increased risk of cardiac events with prolonged diabetes mellitus (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCardiac perfusion scan and the phase analysis provided a very useful tool to predict cardiac events in the future by revealing LVDM in our study. As seen there was a meaningful statistical difference regarding 4DM PBW, between the control and diabetic patients. This is in accordance with the Dharmender investigation, which claimed that phase analysis is a very helpful technique to predict LVMD without any additional study/radiation dose to the patients (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Divergences(increased PBW) in left ventricular contractibility has been mentioned as the main pathophysiology of LVMD in diabetic patients in the recent studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Many evidences indicate that LVMD is an independent risk factor for further cardiac complications in the diabetic patients (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), therefore, it necessitates detection of LVMD in patients by phase analysis. Despite the fact, phase analysis ability would be much more than the current definition of LVMD based on QRS width, as 12.5% of our patients had a normal QRS width but an abnormal phase analysis. Hence, it seems that phase analysis is more accurate for these patients who might be candidates for a resynchronization therapy. On the other hand, phase analysis is more accessible and easily performed compared to other evolving modalities such as Magnetic Resonance Imaging or newly discovered biomarkers, as there are very few evidence to support their values to predict further cardiac events. However, cardiac perfusion scan and phase analysis is performed faster, less affected by inhalation and beat variability. Moreover, it offers an automated evaluation and is less operator dependent.\u003c/p\u003e \u003cp\u003eMahfouz et al in a recent 2020 study evaluated resting left ventricular dyssynchrony and mechanical reserve in asymptomatic normotensive subjects with early type 2 diabetes mellitus. They concluded that The LV dyssynchrony was a strong predictor of defective LV mechanical reserve and could be considered as a primary marker to reveal subclinical cardiac dysfunction in patients with asymptomatic diabetes mellitus with a normal LVEF. They also claimed that HbA1c, high-sensitivity C-reactive protein and left atrial volume index were inversely correlated with LVMR. This is in line with our study to show the importance of early detection of ventricular dyssynchrony in early stages to detect those diabetic patients who would benefit from early resynchronization therapy (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther investigations also indicated some evidences of LVMD in asymptomatic diabetic patients (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Early diagnosis of LVMD in asymptomatic diabetic patients helps to prevent dilated cardiomyopathy and irreversible fibrotic changes, which enhance the life expectancy of patients to a great extent.\u003c/p\u003e \u003cp\u003eSome other studies also found an association between obesity or hyperlipidemia and LVMD, which is consistent with our findings (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These have been rarely evaluated in previous studies and could be considered as a novelty of our investigation. However, more studies are necessary to find the exact role of patient confounding factors in the progression of LVMD.\u003c/p\u003e \u003cp\u003eWe had some limitations in performing the study. We would like to perform perfusion scans 2\u0026ndash;3 times more to evaluate changes in phase analysis parameters, especially in those who underwent early resynchronization therapy. Despite the fact, it was not possible due to limitations in expenses and time. However, it is recommended to be considered in further studies. Furthermore, the exact effects of some confounding factors such as underlying diseases, age, or smoking was not assessed here. It is recommended to be evaluated in larger prospective clinical trials to clarify the exact role of underlying patients\u0026rsquo; conditions. However, it would help to better understand the pathophysiology of LVMD in asymptomatic diabetic patients.\u003c/p\u003e \u003cp\u003eWe found poor correlation between 4DM and QGS software. Difference between PBW in diabetics and non-diabetics was only statistically significant in 4DM and QGS seemed insensitive to access some dyssynchrony. These findings were noted in some studies before (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), however some other studies found 4DM and QGS both helpful in detection of LVDM (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eAsymptomatic diabetics, especially those with prolonged diabetes, with normal myocardial perfusion might have some degrees of LVMD, which might progress to dilated cardiomyopathy if not considered appropriately. Phase analysis helped us to detect this in our study. These patients might benefit from different preventive or therapeutic options like better glycemic control or resynchronization therapy to avoid unwanted and irreversible cardiac events and enhance their overall life expectancy. We recommend that phase analysis should be done for all patients for early diagnosis of LVDM.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the staff of nuclear medicine ward to help collecting relevant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMellitus D (2005) Diagnosis and classification of diabetes mellitus. 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Journal of Nuclear Cardiology: Official Publication of the American Society of Nuclear Cardiology. 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahfouz RA, Seaoud EA, Elbelbesy RA, Shehata IE (2020) Resting Left Ventricular Dyssynchrony and Mechanical Reserve in Asymptomatic Normotensive Subjects with Early Type 2 Diabetes Mellitus. Pulse 8(1\u0026ndash;2):46\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoliman MA, Yaseen RI, Ahmed MA (2014) Left ventricular dyssynchrony in hypertensive patients with normal systolic function: tissue synchronization imaging study. Menoufia Medical Journal 27(2):407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirozzi S, Nelson A, Nelson D, Piper J, Traughber B, Faulhaber P et al (2012) Comparison of QGS, 4DM, and MIMcardiac for the evaluation of left ventricular functional parameters for gated myocardial SPECT. J Nucl Med 53(supplement 1):2297-\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Souza Filho EM, Mesquita CT, Gismondi RA, de Amorim Fernandes F, Verberne HJ (2019) Are there normal values of phase analysis parameters for left ventricular dyssynchrony in patients with no structural cardiomyopathy?: a systematic review. Nucl Med Commun 40(10):980\u0026ndash;985\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkuda K, Nakajima K, Matsuo S, Kashiwaya S, Yoneyama H, Shibutani T et al (2017) Comparison of diagnostic performance of four software packages for phase dyssynchrony analysis in gated myocardial perfusion SPECT. EJNMMI research 7(1):1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkuda K, Nakajima K, Matsuo S, Soichiro K, Yoneyama H, Shibutani T et al (2017) Evaluation of left ventricular dyssynchrony by phase analysis in gated myocardial perfusion SPECT: Comparison of four software programs. J Nucl Med 58(supplement 1):231-\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-cardiovascular-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caim","sideBox":"Learn more about [The International Journal of Cardiovascular Imaging](https://www.springer.com/journal/10554)","snPcode":"10554","submissionUrl":"https://submission.nature.com/new-submission/10554/3","title":"The International Journal of Cardiovascular Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Diabetes mellitus, Cardiomyopathy, Phase analysis, SPECT-MPI, phase standard deviation, phase bandwidth, LVMD ","lastPublishedDoi":"10.21203/rs.3.rs-497675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-497675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction; \u003c/strong\u003ethe aim of this study was to employ \u0026nbsp;phase analysis to diagnose left ventricular mechanical dyssynchrony (LVMD)in asymptomatic patients with diabetes mellitus type 2 and normal perfusion study\u0026nbsp;to prevent diabetic cardiomyopathy.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods \u0026amp; materials; \u003c/strong\u003eNinety-three consecutive patients with known type 2 diabetes and 81 age- and gender- matched patients without diabetes who were candidates for SPECT-MPI were considered as the control group. The presence of LVMD as an indicator of cardiomyopathy- was determined using phase analysis for each scan with quantitative gated SPECT (QGS) and corridor4DM (4DM) software. All outcomes such as phase bandwidth (PBW) and phase standard deviation (PSD) were compared between the two groups. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults; \u003c/strong\u003eA total of 174 patients were included in the study. There were no statistically significant difference regarding demographic factors between the two groups (P\u0026gt;0.05). PBW showed statistically significant differences (increased in diabetics) between the control and diabetic patients (P \u0026lt; 0.05). Kruskal Wallis analysis revealed that as the duration of diabetes is prolonged, especially more than 15 years, the probability of LVMD is increased as well (p=0.021). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDiscussion;\u003c/strong\u003e Fraction of asymptomatic diabetic patients with normal ejection fraction and gated SPECT MPI-especially those with prolonged diabetes- might have some degrees of LVMD. Phase analysis can detect this which in turn would prevent progress into heart failure.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Comparing Left Ventricular Mechanical Dyssynchrony between Diabetic and Non-Diabetic Patients with Normal Gated SPECT MPI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-19 18:36:16","doi":"10.21203/rs.3.rs-497675/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"The International Journal of Cardiovascular Imaging","date":"2021-06-25T00:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-05-09T07:32:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-05-09T06:57:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-05-08T04:53:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Cardiovascular Imaging","date":"2021-05-08T04:29:30+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revisions","date":"2021-05-06T13:48:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-cardiovascular-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caim","sideBox":"Learn more about [The International Journal of Cardiovascular Imaging](https://www.springer.com/journal/10554)","snPcode":"10554","submissionUrl":"https://submission.nature.com/new-submission/10554/3","title":"The International Journal of Cardiovascular Imaging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36db043d-0af3-4a27-8c35-1f145a0b9520","owner":[],"postedDate":"May 19th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4422670,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2021-08-22T15:13:56+00:00","versionOfRecord":{"articleIdentity":"rs-497675","link":"https://doi.org/10.1007/s10554-021-02358-1","journal":{"identity":"the-international-journal-of-cardiovascular-imaging","isVorOnly":false,"title":"The International Journal of Cardiovascular Imaging"},"publishedOn":"2021-07-27 15:00:34","publishedOnDateReadable":"July 27th, 2021"},"versionCreatedAt":"2021-05-19 18:36:16","video":"","vorDoi":"10.1007/s10554-021-02358-1","vorDoiUrl":"https://doi.org/10.1007/s10554-021-02358-1","workflowStages":[]},"version":"v1","identity":"rs-497675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-497675","identity":"rs-497675","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}