Cardiac systolic and diastolic function in relation to cardiovascular risk factor distribution: a comparison of strain-rate imaging in Russian and Norwegian populations                                             Heart-to-Heart: Norwegian-Russian multilevel educational collaboration in cardiovascular disease epidemiology

Cardiac systolic and diastolic function in relation to cardiovascular risk factor distribution: a comparison of strain-rate imaging in Russian and Norwegian populations Heart-to-Heart: Norwegian-Russian multilevel educational collaboration in cardiovascular disease epidemiology | 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 Cardiac systolic and diastolic function in relation to cardiovascular risk factor distribution: a comparison of strain-rate imaging in Russian and Norwegian populations Heart-to-Heart: Norwegian-Russian multilevel educational collaboration in cardiovascular disease epidemiology Assami Rösner, Mikhail Kornev, Hatice Akay Caglayan, Sofia Malyutina, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5307004/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: Cardiovascular morbidity and mortality rates are high in Russia and it is likely that this reflects a similar impact on the general cardiac health of the population. The current study seeks to compare standard echocardiography and strain-based measurements between Russian and Norwegian populations, while also exploring their links to hemodynamic and risk factors. Methods: This study included echocardiographic measurements of 1,192 participants from Arkhangelsk and Novosibirsk, Russia, and 917 from the Tromsø Study population, Norway. The sample included men and women aged 40–69 years. Normalcy, defined as the absence of hypertension or indicators of CVD, was observed in 840 individuals. We performed conventional echocardiography and analysed two-dimensional speckle-tracking longitudinal strains, including systolic, early-, and late-diastolic SR values. The study population was divided into four groups: normal, controlled hypertension, hypertensive blood pressure, and cardiac disease. Echocardiographic parameters were compared between the Russian and Norwegian populations,adjusted for age, sex, height, body mass index, blood pressure, heart rate (HR), atrial fibrillation (AF), smoking, pulmonary hypertension, and serum values for total, LDL (low density lipoprotein), and HDL (high density lipoprotein) cholesterol; triglycerides; creatinine; high-sensitivity C-reactive protein; and HbA1C. Results: Russians showed a tendency towards lower longitudinal systolic functional parameters, which were most prominent in the normotensive group. However, these differences became insignificant after adjusting for parameters that influence pre- and after-loads. Russians also had a lower stroke volume, higher HR, higher left atrial volume, lower A, and higher E/A ratio, indicating a higher incidence of diastolic dysfunction in the Russian population that persisted after adjustments. Conclusion: After adjusting for factors that influence cardiac function, there were no differences in systolic functional parameters betweenthe Norwegian and Russian populations. However, differences in diastolic parameters, which persisted after adjusting for conventionally influential factors, indicated unexplained underlying causes of diastolic dysfunction in the Russian population. Figures Figure 1 Figure 2 Figure 3 Figure 4 Key message Russians had higher blood pressure, BMI, smoking rates and other cardiovascular risk factors vs Norwegians Unadjusted myocardial strain was lower in Russians, but differences disappeared after adjusting for cardiovascular risk factors Russians showed evidence of diastolic dysfunction not explained by conventional risk factors Systolic dysfunction in Russians was related to higher blood pressure and afterload Causes of Russian diastolic dysfunction remain unexplained and require further study. Strength and limitations of the study Large population-based sample sizes from Russia and Norway allowing multifactorial comparison between countries Extensive data collected including questionnaires, health examinations, blood samples, ECGs and echocardiograms Echocardiography protocols were harmonized between the Russian and Norwegian studies Single reader analysis for myocardial strain/strain rate to avoid inter-reader bias between countries Lack of detailed data on alcohol consumption patterns limits ability to account for this factor INTRODUCTION The cardiovascular morbidity and mortality rates are notably higher in Russia compared to developed Western European populations ( 1 ). The Heart-to-Heart project (H2H) was designed to explore underlying causes for the high premature mortality and cardiovascular risk in Russia ( 2 ). It draws comparisons between the Russian Know Your Heart Study (KYH) with the seventh survey of the Norwegian Tromsø Study (Tromsø7) using echocardiograms, examinations, and questionnaires with the Norwegian population representing the Western European lifestyle and healthcare standards. Previous H2H publications explored risk profiles with a range of factors including chronic obstructive pulmonary disease ( 3 ),diabetes ( 4 ), antihypertensive and lipid-lowering drugs ( 5 ), renal disease ( 6 ), alcohol consumption ( 7 ) and psychosocial factors ( 8 ). Findings from these studies highlighted the overrepresentation of cardiovascular risk factors such as obesity, smoking, diabetes, and hypertension in the Russian population ( 2 ) which correlates with reduced cardiac function ( 9 ). Known factors like blood pressure, diabetes, body mass index (BMI), age, and sex are known to affect both systolic and diastolic cardiac functions ( 10 , 11 ). However, the specific influence of these factors on cardiac function within Russian and Western European populations remains unclear. Strain-rate (SR) imaging by two-dimensional speckle-tracking echocardiography (STE) is sensitive and specific unmasking latent systolic and diastolic dysfunctions ( 12 ). In addition, global longitudinal strain (GLS) is a key measure often employed to detect early signs of heart failure ( 13 ). This study primarily aims to examine conventional echocardiography and S/SR parameters between the Norwegian and Russian populations. The Norwegian population mirrors the typical cardiovascular disease and mortality rates seen across Western Europe. Moreover, this research intends to explore the relationship of these parameters with possible factors causing variations in systolic and diastolic heart functions. This approach could provide valuable insights to better understand the cardiovascular health disparities between these populations. METHODS Study population This study was based on two cross-sectional population-based studies conducted at three locations. First, the KYH study on cardiovascular risk factors, cardiac structure, and function, which was conducted in Arkhangelsk and Novosibirsk between 2015 and 2018. The study recruited 5,088 women and men aged 35–69 years for the baseline interview, and 2,381 participants from Arkhangelsk and 2,161 from Novosibirsk underwent health checks. Second, the Tromsø7 study, which was conducted in 2015 and 2016. All citizens in the Tromsø municipality aged ≥40 years were invited, of which 21,083 (64.7%) women and men aged 40–99 years participated ( 14 ). The KYH and Tromsø7 studies were planned and conducted in parallel by collaborative research groups. During the development phase, the study questionnaires and procedural protocols were harmonised, including protocols for echocardiographic examinations. Among the participants who underwent echocardiography (2,340 from Tromsø and 4,521 from Russia), a random study sample of equal-sized age (40–49, 50–59, and 60–69 years) and sex groups was selected (N = 2,109), comprising residents of Arkhangelsk (N = 595), Novosibirsk (N = 597), and Tromsø (N = 917). The flowchart in Fig. 1 shows the selection of participants and their division into groups. Due to planning of the study in the time period 2012–2013, this research did not involve patients or public involvement in the study design, conduct, reporting, or dissemination plans. Data collection All selected participants of the KYH and Tromsø7 studies underwent medical examination with an echocardiography component, questionnaire, and biological sample collection. Transthoracic echocardiography was performed in the left lateral decubitus position using a commercially available GE Healthcare systems Vivid q equipped with a 1.5–3.6 MHz sector matrix transducer in Russia and a high-end machine E9 with a single crystal matrix sector probe of 1.5–4.6 MHz in Norway. In both studies, conventional two-dimensional grey-scale images and pulsed, continuous, and colour Doppler data were acquired from the parasternal and apical views. For the subsequent S/SR analysis, grayscale images were obtained at a frame rate of at least 50 fps. Additionally, EchoPAC (v.203, GE-Vingmed AS, Horten, Norway) was used for offline conventional and S/SR measurements. Trained echocardiography specialists performed all the examinations. Conventional measurements were regularly assessed within and between the reading laboratories in Novosibirsk, and Tromsø. To avoid systematic errors of parameters with high inter-observer variabilities between reading groups, we replaced left atrial (LA) diameter, basal tissue Doppler velocities, and MAPSE with LA volume, STE-derived basal velocities, and mitral annular displacement measurements from a single reader, respectively. Furthermore, we included other conventional parameters measured as part of each screening, such as heart rate (HR), stroke volume (SV) derived from Doppler in the left ventricular outflow tract, and Doppler measurements from the mitral valve (MV), such as E/e’, MV early diastole (E) velocity, MV atrial contraction (A) velocity, E/A ratio, and MV E deceleration time (DT). The ejection fraction was calculated using the Simpson’s method, and pulmonary hypertension was defined as an uncorrected tricuspid regurgitation gradient of > 35 mmHg. Strain and strain-rate analysis Ventricular strain measurement data were obtained in apical two- (2CH) and four-(4CH) chambers. The inter-reader-dependent variability of some conventional strain-derived parameters can be high, often introducing systematic errors. To avoid inter-reader bias between Russian and Norwegian reading laboratories, all S/SR analyses (Q-analysis, EchoPAC, and GE) were performed by a single reader. The myocardial borders were manually traced and the region of interest was corrected for myocardial thickness. Peak R was selected to define the time-point of end-diastole, while end-systole (ES) was defined as the time-point of aortic valve closure, which can be visualised by detecting the closure click on the spectral tracing of trans-aortic Doppler flow. Additionally, left ventricular systole was measured from the peak of the R-wave to aortic valve closure (AVC), and diastole was defined as the time between AVC and peak R. The automatically administered time-points for AVC were manually corrected when necessary. After automated tracking, the software extracted the longitudinal ES segmental mid-myocardial strain, peak systolic SR (SR S), peak diastolic SR E (at early diastole), and SR A (during atrial contraction), and after discarding segments with strain-curve artefacts ( 9 , 15 ), segmental S/SR averages were calculated for each heart. The STE-derived peak velocity and displacement values of the four basal segments were averaged to calculate the basal displacement and basal peak velocities during systole (s’), early diastole (e’), and atrial contraction (a’). Artefact detection was performed by a second independent reader using screenshots of the strain curves generated using EchoPAC software, and images with detected artefacts (apical foreshortening and curved artefacts) were excluded from the analyses. Strain-curve artefacts were defined as curves that were deviated in diastole, blunted curves that showed reduced strain with missing post-systolic strain (PSS), or floating curves with deformations unrelated to the curves of other segments ( 9 ). Definition of subgroups The study sample was divided into the following four subgroups: participants with cardiac disease, participants with uncontrolled hypertension, participants with controlled hypertension, and normotensive participants (Fig. 1 ). Cardiac disease was defined as the presence of the following criteria: elevated NT-proBNP levels according to age- and sex-specific limits ( 16 ), Q-wave (classes 1.1–1.2.7. of the Minnesota Code), left bundle branch block on the electrocardiogram (ECG), history of myocardial infarction or heart attack, ejection fraction (EF) 25 mmHg), and peak tricuspid regurgitation gradient > 30 mmHg. Valvular stenosis was graded by valvular gradients and areas, and we used a multiparametric, semiquantitative approach for valvular regurgitation, as recommended in the guidelines ( 17 ). Following the 2018 ESC/ESH ( 18 ) and 2023 ESH guidelines ( 19 ) hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, as measured during the health check. Controlled hypertension was defined as a blood pressure below these thresholds, in combination with self-reported use of antihypertensive medications such as diuretics, renin-angiotensin system medications, beta-blockers, or calcium antagonists. In contrast, healthy normotensive participants were those for whom the criteria for cardiac disease or hypertension did not apply. Statistical analyses We performed one-way analysis of variance (ANOVA) with Bonferroni post-hoc tests to assess between-group differences in continuous echocardiographic parameters. A χ 2 test was used for group comparisons of categorical variables. In contrast, for comparisons of continuous variables between Russian and Norwegian populations, we used linear regression analysis with and without adjustment for the following co-variates: age, sex, height, BMI, systolic and diastolic BP, HR, AF, smoking, pulmonary hypertension, and serum values for total, LDL, and HDL cholesterol; triglycerides; creatinine; high-sensitivity C-reactive protein; and HbA1C. All statistical analyses were performed using SPSS v.28.0 (IBM Corp., Armonk, N.Y., USA), and statistical significance was set at a two-sided p < 0.05. Continuous data are presented as mean (M) ± standard deviation (SD), and NT-proBNP is presented as a skewed variable as the median (Me) with upper and lower quartiles (Q1; Q3). Furthermore, categorical characteristics are presented as absolute numbers (Abs) and proportions (%). Intra-observer variability in strain and SR measurements was calculated from 135 randomly selected echocardiographic records comprising 1,620 segments. These were repeatedly analysed by the same observer 6–12 months after the initial analysis. Subsequently, a second observer trained in the same echocardiography laboratory re-analysed the same images for inter-observer variability. Both observers performed at least 500 readings before performing the intra- and inter-observer studies. For the inter-observer variability of conventional systolic and diastolic parameters, 40 randomly selected echocardiograms from Russia and Norway were re-analysed by the main readers of the Russian and Norwegian reading laboratories who were blinded to the compared results. Subsequently, intra- and inter-observer variability were assessed as the limits of agreement derived from Bland-Altman plots, and intra-class correlation was calculated. Two previous publications based on the same dataset have described the intra- and inter-observer variability assessments in more detail ( 15 , 20 ). RESULTS Table 1 presents general demographic differences between the Russian and Norwegian population. The supplemental Table S1 illustrates additionally differences between the two Russian populations from Arkhangelsk and Novosibirsk. Although the dataset was stratified by sex and age groups, Norwegian participants were slightly, but not significantly, older than Russian participants. Additionally, Norwegian participants were significantly taller, with a lower percentage showing an elevated BMI, which was highest in the Novosibirsk group. Russian participants had higher systolic and diastolic BP, as well as a higher prevalence of elevated systolic and diastolic BP (Fig. 2 ). Concerning other cardiovascular risk factors, Norwegians displayed lower LDL cholesterol levels and the percentage of lipid-lowering drug usage was significantly lower (Table 1 ). Furthermore, diabetes, daily smoking, renal failure, and elevated pro-BNP levels were less prevalent in the Norwegian population. Table 1 Characteristics of Norwegian or Russian participants Russian Norwegian Mean ± SD or n (%) Mean ± SD or n (%) p -value Group n 1192 917 Women 594 (50) 460 (50) 0.987 Men 598 (50) 457 (50) Age (years) 54.9 ± 8.5 56.0 ± 8.5 0.012 Height (cm) 168 ± 9.5 172 ± 9 < 0.001 Weight (kg) 80 ± 17 80 ± 16 0.286 BMI (kg/m2) 28.1 ± 5.7 27.2 ± 4.5 < 0.001 High BMI (n) 385 (32.3) 214 (23.3) < 0.001 Systolic BP (mmHg) 133 ± 20 129 ± 20 < 0.001 Diastolic BP (mmHg) 84 ± 11 76 ± 11 < 0.001 LDL Cholesterol (mmol/l) 3.73 ± 0.91 3.6 ± 0.9 0.001 High LDL Cholesterol (n) 374 (31.4) 269 (29.3) 0.616 Cholesterol (mmol/l) 5.5 ± 1.1 5.5 ± 1.0 0.311 Triglycerides 1.58 ± 1.2 1.48 ± 0.9 0.039 High Triglycerides (n) 186 (15.6) 132 (14.4) 0.035 Lipid Lowering drugs (n) 256 (21.5) 147 (16.3) < 0.001 HbA1C (%) 5.6 ± 0.5 5.6 ± 0.5 0.977 High HbA1C (n) 67 (5.6) 40 (4.4) 0.282 Diabetes (n) 82 (6.9) 37 (4.0) 0.019 Smoking daily § (n) 302 (25.4) 121 (13.2) < 0.001 Creatinin (mmol/l) 87 ± 29 74 ± 15 < 0.001 Creatinin high (n) 86 (7.2) 18 (2.0) < 0.001 HS CRP (mg/l) 3.6 ± 7.3 1.7 ± 2.0 < 0.001 Hx of renal failure (n) 241 (20.2) 34 (3.8) < 0.001 Hx of Cancer (n) 81 (6.7) 66 (7.4) 0.873 Hx of Asthma (n) 60 (5.0) 109 (12.2) < 0.001 Hx of Stroke (n) 38 (3.2) 21 (2.3) 0.049 NT-proBNP (pmol/l) 172 (14.4) 46 (28/59) 0.005 High NT-proBNP (n) 202 (16.9) 25 (2.7) 0.005 Valvular heart disease ≥ grade II (n) 7 (0.6) 2 (0.2) 0.396 LA volume index high (n) 160 (13.4) 91 (9.9) < 0.001 BMI: body mass index; BP: blood pressure; LDL: low density lipoproteins; HDL: high density lipoproteins; LV EF: left ventricle ejection fraction. §Refers to active current smoking; NT-proBNP in median (lower quartiles/upper quartiles) The Norwegian population also had less participants with a history of angina, heart attack, or heart failure. However, the highest percentages of AF were registered among Norwegians, as shown in Fig. 2 . However, atrial volume index was significantly higher in the Russian population, which had the lowest number of patients with AF. The differences in antihypertensive drug use are shown in Fig. 2 . Notably, Arkhangelsk had the highest prescription or self-medication rate, followed by Novosibirsk and Tromsø (the lowest rate). To assess the cardiac functional parameters, we compared the Russian and Norwegian subpopulations in their respective subgroups. The differences between the subgroups are presented in Table 2 . The supplemental Table S2 shows the mean values of these measurements. Notably, in the normal and hypertensive subgroups, the mean heart rate was higher in the Russian participants than in the Norwegian participants while most of the adjusted systolic parameters like stroke volume longitudinal displacement, velocity and strain were not significantly different between the Russian and the Norwegian participants. As shown in Fig. 3 of the supplements, the unadjusted longitudinal systolic strain was slightly lower in the Russian population than in the Norwegian population, and only the two hypertension groups showed significant differences. Figure 4 demonstrates that SR S, SR E and SR A in most subgroups were equal in the Russian and Norwegian populations, whereas unadjusted SR E was slightly but still significantly lower in the group with elevated BP than in the other groups. Left ventricular (LV) diastolic functional parameters based on conventional Doppler indices (Table 2 ) showed longer MV E DT and lower e’ as possible indicators for impaired relaxation in the normal and hypertensive groups of the Russian population, with a longer MV E DT and higher LA volume index. Some indicators for elevated filling pressures like higher E/e’ and higher E velocities were not significantly different between the adjusted groups. However, all parameters based on MV A and longitudinal function in the atrial contraction phase (i.e. SR A, a’) were significantly lower in some groups of the Russian population. Table 2 Mean difference between Norwegians and Russians for systolic and diastolic functional parameters Healthy Normotensives Group A Hypertension Group B Controlled Hypertension Group C Cardiac Disease Group D Group n (Norwegians/Russians) 441/351 251/374 62/150 163/317 Mean difference Norwegians - Russians (95% CI) Longitudinal ES strain (%) Unadjusted -0.15 (-0.49 to 0.19) -0.89 (-1.37 to -0.41) -0.90 (-1.66 to -0.13) 0.06 (-0.66 to 0.79) Adjusted 0.16 (-0.35 to 0.68) -0.25 (-0.50 to 1.00) -0.38 (-1.44 to 0.67) -0.23 (-0.76 to 1.23) Longitudinal peak SR S Unadjusted 0.00 (-0.03 to 0.03) -0.03 (-0.07 to 0.00) -0.05 (-0.11 to 0.00) 0.01 (-0.04 to 0.05) Adjusted -0.01 (-0.05 to 0.03) -0.02 (-0.07 to 0.04) -0.02 (-0.07 to 0.10) -0.34 (-0.10 to 0.04) Longitudinal peak SR E Unadjusted -0.02 (-0.07 to 0.03) 0.06 (-0.01 to 0.11) -0.01 (-0.10 to 0.09) -0.01 (-0.08 to 0.07) Adjusted -0.00 (-0.06 to 0.08) -0.05 (-0.13 to 0.04 -0.06 (-0.20 to 0.08) 0.04 (-0.06 to 0.13) Longitudinal peak SR A Unadjusted -0.02 (-0.02 to 0.06) -0.02 (-0.03 to 0.07) 0.03 (-0.05 to 0.10) -0.02 (-0.08 to -0.04) Adjusted 0.04 (0.03 to 0.09) 0.08 (0.02 to 0.14) 0.07 (0.03 to 0.16) -0.17 (-0.09 to 0.06) Longitudinal displacement (mm) Unadjusted 0.81 (0.47 to 1.14) 1.14 (0.64 to 1.63) 1.41 (0.61 to 2.21) 0.37 (-0.27 to 1.00) Adjusted 0.06 (-0.45 to 0.56) 0.06 (-0.71 to 0.84) -0.29 (-0.9to 0.1) -0.09 (-0.97 to 0.79) Peak velocity s´ (cm/s) Unadjusted -0.03 (-0.22 to 0.15) 0.07 (-0.21 to 0.36) -0.35 (-0.14 to 0.84) -0.04 (-0.29 to 0.37) Adjusted -0.16 (-0.43 to 0.12) -0.00 (-0.44 to 0.44) 0.09 (0.54 to 0.72) -0.01 (-0.42 to -0.40) Peak velocity e´ (cm/s) Unadjusted 0.28 (-0.02 to 0.54) -0.15 (-0.51 to 0.22) 0.24 (-0.29to 0.77) -0.08 (-0.53 to 0.37) Adjusted 0.49 (0.16 to 0.81) 0.28 (-0.25 to 0.81) 0.01 (-0.71 to 0.73) -0.41 (-0.95 to 0.13) Peak velocity a´ (cm/s) Unadjusted -0.31 (-0.50 to -0.12) -0.08 (-0.32 to 0.16) -0.45 (-0.85 to -0.04) 0.01 (-0.29 to 0.31) Adjusted -0.25 (-0.51 to-0.002) -0.23 (-0.57 to 0.11) -0.53 (-1.1 to -0.02) -0.05 (-0.37 to 0.48) LA volume Index (ml/m 2 ) Unadjusted 2.01 (1.03 to 2.99) -1.31 (-2.99 to -1.03) -1.31 (-2.65 to 0.03) -0.57 (--2.45 to 1.32) Adjusted -0.05 (-0.12 to 0.01) -0.08 (-0.17 to -0.01) -0.10 (-0.25 to 0.05) -0.12 (-0.22 to -0.02) Heart rate (1/min) Unadjusted -4.30 (-5.56 to -3.04) -2.54 (-4.28 to -0.80) -3.23 (-5.98 to -0.49 -3.53 (-5.64 to -1.41) Adjusted -4.77 (-6.69 to -2.85) -0.32 (-2.97 to 2.32) -4.18 (-8.10 to- 0.28) -2.03 (-4.78 to 0.73) Stroke Volume (ml) Unadjusted 7.36 (4.44 to 10.3) 6.48 (2.76 to 10.21) 11.16 (5.46 to 16.86) 7.44 (3.04 to 11.84) Adjusted 3.04 (-1.21 to 7.29) 4.21 (-1.42 to 9.83) 3.23 (-4.97 to 11.42) 2.39 (-4.13 to 8.90) MV E/e´(1/1) Unadjusted 0.05 (-0.48 to 0.57) 0.52 (-1.61 to 2.65) 0.93 (-0.40 to 2.25) 0.22 (-2.16 to 2.59) Adjusted -0.64 (-1.49 to 0.20) -0.07 (-3.53 to 3.39) -1.38(-0.75 to 3.50) -0.95 (-2.55 to 4.45) MV E velocity (cm/s) Unadjusted -0.02 (-0.04 to 0.01) -002 (-0.04 to 0.01) -0.06 (-0.11 to -0.01) 0.00 (-0.03 to 0.04) Adjusted -0.09 (-0.02 to -0.04) -0.02 (-0.06 to 0.02) -0.04 (-0.10 to 0.03) -0.00 (-0.05 to 0.05) MV A velocity (cm/s) Unadjusted 0.03 (0.01 to 0.05) 0.04 (0.01 to 0.06) 0.02 (-0.03 to 0.07) 0.01 (-0.02 to 0.04) Adjusted 0.05 (0.03 to 0.08) 0.04 (0.01 to 0.08) 0.09 (0.03 to 0.15) 0.01 (-0.03 to 0.05) E/A ratio (1/1) Unadjusted -0.08 (-0.13 to -0.02) -0.07 (-0.12 to -0.02) -0.10 (-0.20 to 0.00) 0.00 (-0.08 to 0.08) Adjusted -0.10 (-0.15 to -0.02) -0.09 (-0.16 to -0.02) -0.16 (-0.30 to -0.03) 0.02 (-0.12 to 0.09) MV E deceleration time (ms) Unadjusted -30.8 (-36.4 to-25.1) -32.5 (-40.4 to -24.5) -32.8 (-46.4 to-19.1) -38.3 (-46.5 to -20.2) Adjusted -32.4 (-41.2 to -23.6) -32.0 (-44.2 to -19.7) -43.2 (-63.7 to -22.7) -38.8 (-49.6 to -26.1) Ejection fraction (%) Unadjusted -0.81 (-0.76 to 0.60) -1.71 (-2.85 to 0.57) -1.68 (-3.21 to -0.15) -4.27 (-5.92 to -2.62) Adjusted -0.61 (-1.68 to 0.46) -3.18 (-4.93 to -1.42) -1.87 (-4.03 to 0.29) -3.68 (-5.99 to -1.38) Difference between Norwegians and Russians (N-R) unadjusted and adjusted for age, sex, BMI, height, systolic blood pressure (BP), diastolic BP, heart-rate, atrial fibrillation, smoking, pulmonary artery pressure, serum values for total, LDL and HDL cholesterol, triglycerides, creatinine, high sensitive C-reactive protein and HBA1C age. sex. height. body mass index. blood pressure and heart-rate. atrial fibrillation and pulmonary hypertension; Heart rate was not adjusted for heart rate. ES: endsystolic; SR: strain rate; S: systolic; E: early diastolic; A: at atrial contraction; LA: left atirial; MV: mitral valve Table 3 presents the unadjusted and adjusted linear regressions for speckle tracking and conventional systolic and diastolic parameters. After adjustment, the systolic longitudinal functional parameters and SR-E did not differ between the populations, while Russians still displayed reduced atrial SR and velocities, as well as a higher LA volume. Regarding conventional parameters, MV A velocity, E/A ratio, HR, and EF were higher, E DT was longer and SV was lower. Table 3 Comparison of functional systolic and diastolic parameters between Russians and Norwegians Unadjusted Linear Regression Adjusted Linear Regression Norwegians Russians Norwegians Russians Unadjusted Mean ± SD Unadjusted Mean ± SD p-value Adjusted mean Adjusted mean p-value Adjusted Mean Difference (CI) n 828 1044 788 676 Longitudinal ES strain (%) -20.4 ± 2.8 -19.7 ± 2.9 < 0.001 -20.2 -20.2 0.892 -0.03 (-0.05 to 0.001) Longitudinal peak SR S -1.20 ± 0.20 -1.18 ± 0.20 0.06 -1.20 -1.17 0.063 -0.03 (-0.05 to 0.001) Longitudinal peak SR E 1.57 ± 0.33 1.53 ± 0.35 0.005 1.57 1.55 0.933 0.00 (-0.04 to 0.04) Longitudinal peak SR A 1.11 ± 0.27 1.11 ± 0.27 0.893 1.13 1.07 < 0.001 0.06 (0.03 to 0.09) Longitudinal displacement (mm) 16.5 ± 2.7 15.5 ± 2.8 < 0.001 16.2 16.0 0.295 0.19 (-0.16 to 0.54) Peak velocity s´ (cm/s) -5.4 ± 1.4 -5.3 ± 1.6 0.112 5.37 5.30 0.613 -0.05 (-0.23 to 0.14) Peak velocity e´ (cm/s) -6.4 ± 2.0 -6.2 ± 2.1 0.053 -6.39 -6.30 0.122 0.18 (-0.05 to 0.40) Peak velocity a´ (cm/s) -6.3 ± 1.4 -6.2 ± 1.4 0.032 -6.34 -6.25 0.003 -0.25 (-0.42 to -0.09) LA volume Index (ml/m 2 ) 24.7 ± 7.8 24.8 ± 8.8 0.689 24.7 25.7 0.003 -1.01 (-2.01 to -0.01) n 914 1165 872 766 Heart rate‡ 61.1 ± 10.0 65.0 ± 10.4 < 0.001 61.0 64.1 < 0.001 -2.52 (-3.74 to -1.31) Stroke volume (ml) 89.1 ± 27.6 82.9 ± 27.6 < 0.001 89.1 83.1 0.020 3.20 (0.51 to 5.90) MV E/e´ (1/1) -11.0 ± 9.1 -11.7 ± 10.2 0.096 -10.9 -11.3 0.955 0.04 (-1.21 to 1.28) MV E velocity (m/s) 0.65 ± 17 0.67 ± 17 0.148 0.67 0.68 0.406 -0.01 (-0.03 to 0.01) MV A velocity (m/s) 0.65 ± 16 0.64 ± 15 0.044 0.65 0.63 < 0.001 0.04 (0.03 to 0.06) E/A ratio () 1.07 ± 0.39 1.10 ± 0.36 0.059 1.07 1.13 < 0.001 -0.08 (-0.12 to -0.04) MV E deceleration time (ms) 172 ± 45 207 ± 44 < 0.001 173 207 < 0.001 -36-3 (-41.9 to -30.7) Ejection fraction (%) 54.9 ± 7.4 55.8 ± 6.2 0.004 55.0 55.9 0.001 -2.37 (-3.07 to -1.67) Linear Regression Model adjusted for age, sex, BMI, height, systolic BP (blood pressure), diastolic BP, heart-rate, atrial fibrillation, smoking, pulmonary artery pressure, serum values for total, LDL and HDL cholesterol, triglycerides, creatinine, high-sensitive C-reactive protein and HbA1C. ‡ Not corrected for Heart Rate. ES: end-systolic; SR: strain rate; S: systolic; E: early diastolic; A: at atrial contraction; PSS: post-systolic shortening; MV: mitral valve; LA: left atrial; TR: tricuspid regurgitation The intra-observer variability is relevant for speckle tracking-derived parameters. The supplemental Table S3 shows good inter-observer variability for global S/SR measurements, without significant differences between the readings of the different populations. However, Table S4 in supplements demonstrates that the inter-observer variability for the conventional echocardiographic parameters introduced a significant error with the “overestimation” of EF, MV deceleration time, and SV in the Russian population, while the readings of MV E, A, and E/A ratio showed reliable intra-class correlation with insignificant deviations. Table S5 in supplements shows the significant covariates of the linear regression model, where systolic parameters were mainly influenced by after-load-relevant factors such as body height, BMI, BP, HR, AF, and pulmonary hypertension, while general cardiovascular risk factors such as LDL, HDL, creatinine, high-sensitivity C-reactive protein levels (HS-CRP), and HBA1C were significant factors influencing diastolic function. DISCUSSION Main findings of the study In this study, we found significantly different cardiac functional properties between the Russian and Norwegian populations. After adjustment for co-variates in the linear regression analysis, Russian participants still showed a lower SV and higher HR than Norwegian participants. In the Russian population, diastolic properties with higher MV E DT and larger left atria indicated impaired relaxation, while higher MV velocity E, E/A, and E/e´ indicate higher filling pressures. In contrast to longitudinal systolic parameters, diastolic properties remained reduced in the Russian population after adjusting for factors known to influence systolic and diastolic cardiac function. Russians also tended to have lower systolic functional parameters; however, longitudinal strain and tissue velocities did not differ after adjustment. Differences in cardiovascular risk factors and diseases The Russian population has considerably higher cardiovascular morbidity and mortality rates than developed Western European populations ( 21 – 24 ). The study was conducted as a part of the Heart to Heart collaboration( 2 ) which was established to acquire new knowledge about the considerably higher premature mortality rate from cardiovascular diseases in Russia compared to Norway (735 vs 89 per 100.000 men and 239 vs 33 per 100.000 women, respectively, during 2012–2016). In 2018, cardiovascular mortality rates per 100,000 inhabitants were 167 in Germany, 86 in France, 153 in Italy, 97 in Spain, 114 in Norway, and 431 in Russia. The average age of cardiovascular death was 81 years in Western European countries, compared to 80 years in Norway and 71 years in Russia( 24 ). The KYH and H2H studies aimed to compare two representative populations to gain more knowledge regarding cardiac function and its dependency on known influential factors ( 25 ). This study confirms the previously described unfavourable risk profile of the Russian population. The Russian participants had higher diastolic and systolic BP, high-sensitivity C-reactive protein levels, BMI, prevalence of diabetes, active smoking, and renal failure ( 4 , 6 , 25 , 26 ). Only serum cholesterol levels were similar in both populations; however, triglyceride levels were higher in the Russian population, possibly due to the higher incidence of diabetes. Furthermore, higher percentages of heart attacks, Q waves on the ECG, elevated NT-proBNP, stroke, and pulmonary hypertension reflect a higher prevalence of cardiovascular disease and heart failure in Russia ( 21 ). AF, assessed through a questionnaire, was found to be paradoxically more prevalent in the Norwegian population, which showed lower atrial volumes—an objective measure performed by a single reader in both populations. The diagnosis of AF is influenced by a low threshold for detection and the availability of medical consultations, which may be more accessible in Norway than in Russia. Generally, AF is known to be underdiagnosed, and the number of reported cases in an epidemiological study reflects several factors, including the accessibility of medical care, patient awareness, in addition to the true prevalence of the condition. A study on the Novosibirsk population confirmed that the prevalence of AF based on resting ECGs was generally comparable to that of North American or European populations. Moreover, the prevalence of AF was even higher in the Novosibirsk population compared to these countries in age-groups over 65 years( 27 ). Specifically, the proportions of paroxysmal AF and persistent AF were reported to be approximately 40% and 20%, respectively, in a Russian population cohort and these conditions might be underdiagnosed. Additionally, individuals with AF are prone to both fatal and non-fatal CVD events, and could be underrepresented in the survey sample due to selection bias. This might explain the paradoxically low prevalence of AF reported in the Russian population. Systolic functional parameters, including GLS and GLSR Several physiological factors, such as after-load, HR, and filling properties, influence systolic GLS and GLSR. After-load depends on BP, ventricular size, and vascular resistance ( 28 ), which explains the relationship between S/SR and body size and, thus, height and sex. In fact, BMI directly influences vascular resistance and blood pressure ( 28 ). Additionally, age is known to reduce longitudinal function ( 9 ), but not significantly in the age range of 40–69 years ( 9 ). In a previous study, we showed that normotensive individuals taking antihypertensive drugs had lower global strain than normotensive controls ( 20 ). Furthermore, hypertensives had a higher HR and SV than normotensives, while longitudinal S/SR and displacement were reduced, indicating that a higher SV in hypertension is generated by increased radial contraction. In the present study, the Russian population had a higher HR and lower SV. This indicates a higher sympathicotonia combined with cardio-depressive factors in Russians. As shown in Figs. 2 and 3 , Russian participants had slightly reduced strain and systolic SR in most groups; however, the uncorrected differences were marginal. The combination of increasing S/SR with a higher sympathicotonia and longitudinal S/SR reducing factors—such as higher BP and BMI and lower ventricular filling time at higher HRs—diminished differences in longitudinal strain between the populations. Thus, differences in age, sex, height, BMI, HR, and BP could explain lower cardiac function in the Russian population. Moreover, most systolic longitudinal functional S/SR ratios were not significantly different after adjustment. Other factors such as cholesterol, triglycerides, renal function, and HS-CRP had no direct influence on LV longitudinal function. Therefore, as the uncorrected strains showed the highest differences in the hypertensive groups, the main factor for reduced function in the Russian population may have been the effect of BP on longitudinal systolic and diastolic function. Diastolic functional parameters Higher E-wave velocity and E/A ratios indicate higher filling pressures congruent with higher HR and lower SV. Additionally, higher e´ and SR E may be caused by higher E wave velocities and filling pressures, while lower e´ or SR E may be caused by impaired relaxation. Therefore, the opposite effect of reduced relaxation and increased filling pressures might be the reason for the small effect of population differences on SR E. Beyond age, sex, BMI, BP, HR, and AF HR, which showed a significant influence on systolic longitudinal parameters, cholesterol, triglycerides, creatinine, HS-CRP, and HBA1C showed significant effects on diastolic functional parameters, EF, SV, and HR. However, the remaining differences after adjustment indicate the influence of factors that were not investigated in the present study. Interestingly, diastolic function seems to be equally affected by factors connected with markers for diabetes, renal function, or inflammatory processes, such as the influence of pre- and after-loads. This study was not designed to investigate the effects of alcohol consumption on myocardial function parameters. However, there is a possible prominent difference in alcohol intake between the Russian and Norwegian populations, which has not been considered. Alcohol consumption is associated with impaired relaxation and high filling pressures ( 26 ). In fact, a recent KYH sub-study of patients diagnosed with chronic alcohol usage showed the dependency of diastolic functional parameters such as e´, E/e’, and LA volume on alcohol consumption ( 26 ) in a subclinical setting. In another KYH sub-analysis, the association between measures of diastolic function, alcohol amount per session, and binge drinking (positive for E/e’ and negative for e’) was reported in a general population sample ( 29 ). Furthermore, other factors such as the psychosocial environment in relation to simpatico tone may also play a role in these differences. The Novosibirsk population includes less than 5% other than Russian or Ukrainian ethnicity, a percentage too small to consider ethnicity as a major confounder for diastolic function. Methodological considerations on inter-reader variability Conventional diastolic functional parameters are primarily based on MV Doppler assessment. To avoid bias between laboratories at different locations, the S/SR analyses and atrial volumes were read by a single reader. Similarly, the TDI velocities were replaced with the STE peak values of the basal segmental velocities. The conventional Doppler parameters were measured in different laboratories in Russia and Norway. Theoretically, this might introduce a significant bias. However, as shown in Table 5, the inter-observer variability for MV Doppler-based parameters was excellent; therefore, this was unlikely to be a problem. Furthermore, the MV DT reduced by the systematic error would still be significantly longer in the Russian population than in the Norwegian population. This is congruent with larger LA volumes in the Russian population (which was read by a single reader), indicating delayed relaxation. Additionally, Doppler readings with large differences between MV A values between the populations correlated well with the findings of A velocities and SR A, indicating higher filling pressures. Therefore, we interpreted that the differences in conventional diastolic parameters between populations were not reader-related. Furthermore, applying the systematic reading errors of the laboratories (Table 5) to the results in Table 3 , it is possible that the EF was significantly lower in the Russian population. It is also possible that the lower SV of the Russian population was underestimated. Lower EF in the Russian population correlated well with lower SV; however, these two parameters displayed the highest inter-reader variability, and should therefore be interpreted with caution. Limitations Ventricular dimensions derived from M-mode echocardiograms, such as septal thickness, myocardial mass, end-diastolic diameter, and tissue Doppler measurements, showed high systematic differences between the Russian and Norwegian reading groups and were therefore not included in the present study. However, we included EF, SV, and MV DT in the study, which are parameters with high inter-reader variability between the reading groups. Nonetheless, the analyses of these parameters should be interpreted with caution. EF and SV were the only systolic parameters with significant differences between the groups, which may indicate a systematic reading error instead of a cardiac functional difference. Previously, Iankuchykova et al. investigated differences in biochemical markers between these populations and suggested adjustments for different laboratories; however, we used the original values for biochemical markers ( 30 ). The biomarkers were used only for adjustment and description; therefore, it is highly unlikely that the correction would have changed the outcome. Hypertension thresholds were defined following the 2018 ESC/ESH guidelines which are unchanged in the 2023 ESH guidelines. Blood pressure strongly influences cardiac systolic and diastolic function and might also affect participants with high normal blood pressure (130–139 and 85–89 mmHg), which were categorized as normal in the present study. However, we adjusted for blood pressure as a continuous variable across all four functional groups. The World Health Organisation reports a per-capita alcohol consumption of 5.8 and 18.7 litres in women and men, respectively, in Russia, while that in Norway is 3.2 and 11.6 litres for women and men, respectively ( 7 , 31 ). Alcohol consumption and drinking behaviour in the Russian population may be important factors for higher mortality and heart failure rates. However, objective information on drinking patterns is difficult to obtain from questionnaire-based population studies; therefore, this might be important missing information ( 7 ). In the present sub-study, only one question regarding regular alcohol consumption during the past year was available, which was answered positively by 93% of the Norwegian population and 82% of the Russian population. Owing to the lack of relevance, we decided not to include this risk factor in our analysis. We assume that alcohol consumption and drinking patterns might have had a prominent effect in the present study, especially on diastolic cardiac function ( 26 ). The extent and significance of differences between diastolic measurements across groups were not uniform. However, when comparing the Norwegian and Russian populations within each of the normal and hypertensive groups, we observed a consistent pattern of differences. This indicates that despite some variability, meaningful conclusions can be drawn. CONCLUSION Using conventional and S/SR measures to compare the two populations, we found that the Russian population showed a lower systolic and diastolic cardiac function than the Norwegian population. Higher BP, HR, BMI, sex, and age were the most important factors explaining lower longitudinal function in the Russian population. However, even after further adjustment for known risk factors for suppressed longitudinal strain, including diabetes, cholesterol, and HS-CRP, a significant difference between the populations remained, indicating that other risk factors are important. Future studies are required to identify these risk factors. Declarations Contributorship statement ARo: study design, funding of the PhD project, statistical analyses, and writing of the manuscript. MK: data-collection, strain-analysis, statistical analysis, and writing of the manuscript; HAC: artefact analysis and strain-analysis for inter-observer-variability; SM and ARy: funding, conduction of the data-collection in Russia, and critical revision of the manuscript for important intellectual content; HS: design funding and conduction of the Tromsø7 study and critical revision of the manuscript. AVK: conduction of the data collection in Arkhangelsk and critical revision of the manuscript for important intellectual content. All authors reviewed the manuscript. Acknowledgements We would like to acknowledge the contributions of the KYH and Tromsø7 study participants. We would also like to thank Editage (www.editage.com) for the English language editing. Competing interests HS has received lecture fees from Amgen and Novartis. Funding The KYH study was a component of the International Project on CVD in Russia (IPCDR), funded by the Welcome Trust Strategic Award (No. 100 217), UiT The Arctic University of Norway, the Norwegian Institute of Public Health, and the Norwegian Ministry of Health and Social Affairs. SM and Ary were supported by the Russian Academy of Science, State Target (grant number: № FWNR-2024-0002). The study sponsor/funder was not involved in the design of the study; the collection, analysis, and interpretation of data; or the writing of the report; and no restrictions were imposed on the publication of the report. The first author received a PhD grant from Helse Nord RHF (HNF 1458-19). Availability of data and materials The data that support the findings of this study are available from the Know Your Heart and Tromsø Studies; however, restrictions apply to the availability of these data, which were used under licence for the current study and are not publicly available. However, data from this sub-study are available from the corresponding authors upon reasonable request, with permission from the Know Your Heart Steering Group and the corresponding author of this manuscript. All such KYH data requests will be guided by protecting personal information, confidentiality agreement with participants, and informed consent. Ethics approval and consent to participate This study complied with the principles of the Declaration of Helsinki. The Know Your Heart study was approved by the ethics committees of the London School of Hygiene and Tropical Medicine (approval number: 8808, received 24/02/2015), Novosibirsk State Medical University (approval number: 75, received 21/05/2015), the Institute of Preventative Medicine, Novosibirsk (no approval number, approval received 26/12/2014), and the Northern State Medical University, Arkhangelsk (approval number: 01/01–15, received 27/01/2015). Written informed consent was obtained from all study participants. Clinical trial number : not applicable References Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J. 2016;37(42):3232-45. Cook S, Malyutina S, Kudryavtsev AV, Averina M, Bobrova N, Boytsov S, et al. Know Your Heart: Rationale, design and conduct of a cross-sectional study of cardiovascular structure, function and risk factors in 4500 men and women aged 35-69 years from two Russian cities, 2015-18. Wellcome Open Research. 2018;3. Cook S, Eggen AE, Hopstock LA, Malyutina S, Shapkina M, Kudryavtsev AV, et al. Chronic Obstructive Pulmonary Disease (COPD) in Population Studies in Russia and Norway: Comparison of Prevalence, Awareness and Management. Int J Chron Obstruct Pulmon Dis. 2021;16:1353-68. Iakunchykova O, Averina M, Wilsgaard T, Malyutina S, Kudryavtsev AV, Cook S, et al. What factors explain the much higher diabetes prevalence in Russia compared with Norway? Major sex differences in the contribution of adiposity. BMJ Open Diabetes Res Care. 2021;9(1). Cook S, Hopstock LA, Eggen AE, Bates K, Iakunchykova O, Kontsevaya A, et al. 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The Determinants of the 13-Year Risk of Incident Atrial Fibrillation in a Russian Population Cohort of Middle and Elderly Age. J Pers Med. 2022;12(1). Rösner A, Bijnens B, Hansen M, How OJ, Aarsaether E, Müller S, et al. Left ventricular size determines tissue Doppler-derived longitudinal strain and strain rate. Eur J Echocardiogr. 2009;10(2):271-7. Malyutina; SK, Voronina; E, Guseva; V, Yasukevich; N, Palekhina; Y, Shakhmatov; S, et al. Echocardiographic parameters in relation to patterns of alcohol intake: interim analysis. Eur J Prev Cardiol2019. p. S95. Lakunchykova O, Averina M, Wilsgaard T, Watkins H, Malyutina S, Ragino Y, et al. Why does Russia have such high cardiovascular mortality rates? Comparisons of blood-based biomarkers with Norway implicate non-ischaemic cardiac damage. J Epidemiol Community Health. 2020;74(9):698-704. Organization WH. Global status report on alcohol and health https://apps.who.int/iris/handle/10665/274603]2018 [ Additional Declarations Competing interest reported. HS has received lecture fees from Amgen and Novartis. Supplementary Files SupplementsTables.docx 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-5307004","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374181341,"identity":"078a90c8-473a-4b33-af33-db99bbd88b61","order_by":0,"name":"Assami Rösner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNACAxijgnQtZ0i2jbGNCEX8/ccffrpRwJDPz3/24OPKeYfzzBl4Dz7Ap0XiRo6xdI4Bg+XMGXnJhme3HS62bOBLNsCnheEGDwNIi4HBDR4zycZthxM3HOAxk8CnQ/788ce/wVrOnzH/2TiHCC0GBxLMILYcyDFjbGwgQovhjRwz6xwDCQPJGTnGkg3H0hM3HOYxxusXOaDDbuf8sTHg5z9j+LGhxjpxw/Eewwf4tEABskuYiVA/CkbBKBgFowA/AADHfEV/J7rZ2AAAAABJRU5ErkJggg==","orcid":"","institution":"UiT The Arctic University of Norway","correspondingAuthor":true,"prefix":"","firstName":"Assami","middleName":"","lastName":"Rösner","suffix":""},{"id":374181342,"identity":"922b9a9b-ad5d-48e5-afd8-f8f2b9b5dc7d","order_by":1,"name":"Mikhail Kornev","email":"","orcid":"","institution":"University Hospital of North Norway","correspondingAuthor":false,"prefix":"","firstName":"Mikhail","middleName":"","lastName":"Kornev","suffix":""},{"id":374181343,"identity":"91ac8e0d-d7bc-4496-aca5-f85ea0a7c985","order_by":2,"name":"Hatice Akay Caglayan","email":"","orcid":"","institution":"University Hospital of North Norway","correspondingAuthor":false,"prefix":"","firstName":"Hatice","middleName":"Akay","lastName":"Caglayan","suffix":""},{"id":374181344,"identity":"db39aea2-d258-4f3b-b4db-31b2eee45204","order_by":3,"name":"Sofia Malyutina","email":"","orcid":"","institution":"Russian Academy of Sciences Siberian Branch","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Malyutina","suffix":""},{"id":374181345,"identity":"0a5ed134-6946-45d0-bc53-ee95f1c7747c","order_by":4,"name":"Andrew Ryabikov","email":"","orcid":"","institution":"Russian Academy of Sciences Siberian Branch","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Ryabikov","suffix":""},{"id":374181346,"identity":"56d6786f-8587-4109-8cdc-ab9dd36bd5d2","order_by":5,"name":"Henrik Schirmer","email":"","orcid":"","institution":"Akershus Universitetssykehus HF","correspondingAuthor":false,"prefix":"","firstName":"Henrik","middleName":"","lastName":"Schirmer","suffix":""},{"id":374181347,"identity":"d5ea026b-6e5d-4622-86c6-e45afc5802a1","order_by":6,"name":"Alexander V Kudryavtsev","email":"","orcid":"","institution":"Northern State Medical University of the Ministry of Health of the Russian Federation","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"V","lastName":"Kudryavtsev","suffix":""}],"badges":[],"createdAt":"2024-10-21 21:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5307004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5307004/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70953229,"identity":"edbdf5b9-32af-459f-b869-4271388ec505","added_by":"auto","created_at":"2024-12-09 13:54:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84673,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart about the inclusion and exclusion procedures\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5307004/v1/f336e1ecb12655a248c5e0c7.png"},{"id":70953234,"identity":"b0b419f5-b419-4d2e-8936-20118ae87ab1","added_by":"auto","created_at":"2024-12-09 13:54:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83156,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent prevalence of elevated blood pressure. History of cardiac diseases and antihypertensive medication comparing the three study locations.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5307004/v1/cc09a8c81818fe4cbe57676f.png"},{"id":70953230,"identity":"8787524f-79d4-4abe-a9e1-4300a334f63a","added_by":"auto","created_at":"2024-12-09 13:54:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87765,"visible":true,"origin":"","legend":"\u003cp\u003eEnd-systolic (ES) Global longitudinal strain comparing Russians and Norwegians in groups defined by blood-pressure or heart-disease. Elevated blood pressure (BP) was defined as systolic BP \u0026gt;140 mmHg or diastolic BP \u0026gt;90mmHg. * p\u0026lt;0.05 for unadjusted difference between Russians and Norwegians.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5307004/v1/2424f2094e41960b3a2888ed.png"},{"id":70953231,"identity":"f6478f31-1b09-4e13-8298-a6d54ab24d18","added_by":"auto","created_at":"2024-12-09 13:54:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104037,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal longitudinal early (E) diastolic strain rate (SR) and during atrial contraction (A) comparing Russians and Norwegians in blood pressure and heart-disease groups. * p\u0026lt;0.05 for unadjusted difference between Russians and Norwegians.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5307004/v1/f624e0766f78e667c39f0747.png"},{"id":96709480,"identity":"e64e751f-3fca-4d44-bbd3-f2d1c546b77b","added_by":"auto","created_at":"2025-11-25 10:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2228686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5307004/v1/865c816d-7bc1-40bf-9a1e-cf569746cfa3.pdf"},{"id":70953235,"identity":"69cbe3f6-9c59-413a-be12-0bddb96f2a47","added_by":"auto","created_at":"2024-12-09 13:54:28","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":38599,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementsTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5307004/v1/63c36d1b87116595816ac559.docx"}],"financialInterests":"Competing interest reported. HS has received lecture fees from Amgen and Novartis.","formattedTitle":"Cardiac systolic and diastolic function in relation to cardiovascular risk factor distribution: a comparison of strain-rate imaging in Russian and Norwegian populations Heart-to-Heart: Norwegian-Russian multilevel educational collaboration in cardiovascular disease epidemiology","fulltext":[{"header":"Key message","content":"\u003cul\u003e\n \u003cli\u003eRussians had higher blood pressure, BMI, smoking rates and other cardiovascular risk factors vs Norwegians\u003c/li\u003e\n \u003cli\u003eUnadjusted myocardial strain was lower in Russians, but differences disappeared after adjusting for cardiovascular risk factors\u003c/li\u003e\n \u003cli\u003eRussians showed evidence of diastolic dysfunction not explained by conventional risk factors\u003c/li\u003e\n \u003cli\u003eSystolic dysfunction in Russians was related to higher blood pressure and afterload\u003c/li\u003e\n \u003cli\u003eCauses of Russian diastolic dysfunction remain unexplained and require further study.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Strength and limitations of the study","content":"\u003cul\u003e\n \u003cli\u003eLarge population-based sample sizes from Russia and Norway allowing multifactorial comparison between countries\u003c/li\u003e\n \u003cli\u003eExtensive data collected including questionnaires, health examinations, blood samples, ECGs and echocardiograms\u003c/li\u003e\n \u003cli\u003eEchocardiography protocols were harmonized between the Russian and Norwegian studies\u003c/li\u003e\n \u003cli\u003eSingle reader analysis for myocardial strain/strain rate to avoid inter-reader bias between countries\u003c/li\u003e\n \u003cli\u003eLack of detailed data on alcohol consumption patterns limits ability to account for this factor\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eThe cardiovascular morbidity and mortality rates are notably higher in Russia compared to developed Western European populations (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The Heart-to-Heart project (H2H) was designed to explore underlying causes for the high premature mortality and cardiovascular risk in Russia (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It draws comparisons between the Russian Know Your Heart Study (KYH) with the seventh survey of the Norwegian Troms\u0026oslash; Study (Troms\u0026oslash;7) using echocardiograms, examinations, and questionnaires with the Norwegian population representing the Western European lifestyle and healthcare standards.\u003c/p\u003e \u003cp\u003ePrevious H2H publications explored risk profiles with a range of factors including chronic obstructive pulmonary disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e),diabetes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), antihypertensive and lipid-lowering drugs (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), renal disease (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), alcohol consumption (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and psychosocial factors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Findings from these studies highlighted the overrepresentation of cardiovascular risk factors such as obesity, smoking, diabetes, and hypertension in the Russian population (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) which correlates with reduced cardiac function (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKnown factors like blood pressure, diabetes, body mass index (BMI), age, and sex are known to affect both systolic and diastolic cardiac functions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, the specific influence of these factors on cardiac function within Russian and Western European populations remains unclear.\u003c/p\u003e \u003cp\u003eStrain-rate (SR) imaging by two-dimensional speckle-tracking echocardiography (STE) is sensitive and specific unmasking latent systolic and diastolic dysfunctions (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In addition, global longitudinal strain (GLS) is a key measure often employed to detect early signs of heart failure (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study primarily aims to examine conventional echocardiography and S/SR parameters between the Norwegian and Russian populations. The Norwegian population mirrors the typical cardiovascular disease and mortality rates seen across Western Europe. Moreover, this research intends to explore the relationship of these parameters with possible factors causing variations in systolic and diastolic heart functions. This approach could provide valuable insights to better understand the cardiovascular health disparities between these populations.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis study was based on two cross-sectional population-based studies conducted at three locations. First, the KYH study on cardiovascular risk factors, cardiac structure, and function, which was conducted in Arkhangelsk and Novosibirsk between 2015 and 2018. The study recruited 5,088 women and men aged 35\u0026ndash;69 years for the baseline interview, and 2,381 participants from Arkhangelsk and 2,161 from Novosibirsk underwent health checks. Second, the Troms\u0026oslash;7 study, which was conducted in 2015 and 2016. All citizens in the Troms\u0026oslash; municipality aged \u0026ge;40 years were invited, of which 21,083 (64.7%) women and men aged 40\u0026ndash;99 years participated (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The KYH and Troms\u0026oslash;7 studies were planned and conducted in parallel by collaborative research groups. During the development phase, the study questionnaires and procedural protocols were harmonised, including protocols for echocardiographic examinations.\u003c/p\u003e \u003cp\u003eAmong the participants who underwent echocardiography (2,340 from Troms\u0026oslash; and 4,521 from Russia), a random study sample of equal-sized age (40\u0026ndash;49, 50\u0026ndash;59, and 60\u0026ndash;69 years) and sex groups was selected (N\u0026thinsp;=\u0026thinsp;2,109), comprising residents of Arkhangelsk (N\u0026thinsp;=\u0026thinsp;595), Novosibirsk (N\u0026thinsp;=\u0026thinsp;597), and Troms\u0026oslash; (N\u0026thinsp;=\u0026thinsp;917). The flowchart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the selection of participants and their division into groups. Due to planning of the study in the time period 2012\u0026ndash;2013, this research did not involve patients or public involvement in the study design, conduct, reporting, or dissemination plans.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eAll selected participants of the KYH and Troms\u0026oslash;7 studies underwent medical examination with an echocardiography component, questionnaire, and biological sample collection. Transthoracic echocardiography was performed in the left lateral decubitus position using a commercially available GE Healthcare systems Vivid q equipped with a 1.5\u0026ndash;3.6 MHz sector matrix transducer in Russia and a high-end machine E9 with a single crystal matrix sector probe of 1.5\u0026ndash;4.6 MHz in Norway. In both studies, conventional two-dimensional grey-scale images and pulsed, continuous, and colour Doppler data were acquired from the parasternal and apical views. For the subsequent S/SR analysis, grayscale images were obtained at a frame rate of at least 50 fps. Additionally, EchoPAC (v.203, GE-Vingmed AS, Horten, Norway) was used for offline conventional and S/SR measurements. Trained echocardiography specialists performed all the examinations. Conventional measurements were regularly assessed within and between the reading laboratories in Novosibirsk, and Troms\u0026oslash;. To avoid systematic errors of parameters with high inter-observer variabilities between reading groups, we replaced left atrial (LA) diameter, basal tissue Doppler velocities, and MAPSE with LA volume, STE-derived basal velocities, and mitral annular displacement measurements from a single reader, respectively. Furthermore, we included other conventional parameters measured as part of each screening, such as heart rate (HR), stroke volume (SV) derived from Doppler in the left ventricular outflow tract, and Doppler measurements from the mitral valve (MV), such as E/e\u0026rsquo;, MV early diastole (E) velocity, MV atrial contraction (A) velocity, E/A ratio, and MV E deceleration time (DT). The ejection fraction was calculated using the Simpson\u0026rsquo;s method, and pulmonary hypertension was defined as an uncorrected tricuspid regurgitation gradient of \u0026gt;\u0026thinsp;35 mmHg.\u003c/p\u003e\n\u003ch3\u003eStrain and strain-rate analysis\u003c/h3\u003e\n\u003cp\u003eVentricular strain measurement data were obtained in apical two- (2CH) and four-(4CH) chambers. The inter-reader-dependent variability of some conventional strain-derived parameters can be high, often introducing systematic errors. To avoid inter-reader bias between Russian and Norwegian reading laboratories, all S/SR analyses (Q-analysis, EchoPAC, and GE) were performed by a single reader.\u003c/p\u003e \u003cp\u003eThe myocardial borders were manually traced and the region of interest was corrected for myocardial thickness. Peak R was selected to define the time-point of end-diastole, while end-systole (ES) was defined as the time-point of aortic valve closure, which can be visualised by detecting the closure click on the spectral tracing of trans-aortic Doppler flow. Additionally, left ventricular systole was measured from the peak of the R-wave to aortic valve closure (AVC), and diastole was defined as the time between AVC and peak R.\u003c/p\u003e \u003cp\u003eThe automatically administered time-points for AVC were manually corrected when necessary. After automated tracking, the software extracted the longitudinal ES segmental mid-myocardial strain, peak systolic SR (SR S), peak diastolic SR E (at early diastole), and SR A (during atrial contraction), and after discarding segments with strain-curve artefacts (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), segmental S/SR averages were calculated for each heart. The STE-derived peak velocity and displacement values of the four basal segments were averaged to calculate the basal displacement and basal peak velocities during systole (s\u0026rsquo;), early diastole (e\u0026rsquo;), and atrial contraction (a\u0026rsquo;).\u003c/p\u003e \u003cp\u003eArtefact detection was performed by a second independent reader using screenshots of the strain curves generated using EchoPAC software, and images with detected artefacts (apical foreshortening and curved artefacts) were excluded from the analyses. Strain-curve artefacts were defined as curves that were deviated in diastole, blunted curves that showed reduced strain with missing post-systolic strain (PSS), or floating curves with deformations unrelated to the curves of other segments (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDefinition of subgroups\u003c/h3\u003e\n\u003cp\u003eThe study sample was divided into the following four subgroups: participants with cardiac disease, participants with uncontrolled hypertension, participants with controlled hypertension, and normotensive participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCardiac disease was defined as the presence of the following criteria: elevated NT-proBNP levels according to age- and sex-specific limits (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), Q-wave (classes 1.1\u0026ndash;1.2.7. of the Minnesota Code), left bundle branch block on the electrocardiogram (ECG), history of myocardial infarction or heart attack, ejection fraction (EF)\u0026thinsp;\u0026lt;\u0026thinsp;45%, aortic regurgitation, mitral regurgitation or mitral stenosis (grade 3 and 4), moderate aortic stenosis (mean pressure gradient\u0026thinsp;\u0026gt;\u0026thinsp;25 mmHg), and peak tricuspid regurgitation gradient\u0026thinsp;\u0026gt;\u0026thinsp;30 mmHg. Valvular stenosis was graded by valvular gradients and areas, and we used a multiparametric, semiquantitative approach for valvular regurgitation, as recommended in the guidelines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing the 2018 ESC/ESH (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and 2023 ESH guidelines (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, as measured during the health check. Controlled hypertension was defined as a blood pressure below these thresholds, in combination with self-reported use of antihypertensive medications such as diuretics, renin-angiotensin system medications, beta-blockers, or calcium antagonists. In contrast, healthy normotensive participants were those for whom the criteria for cardiac disease or hypertension did not apply.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eWe performed one-way analysis of variance (ANOVA) with Bonferroni post-hoc tests to assess between-group differences in continuous echocardiographic parameters. A χ\u003csup\u003e2\u003c/sup\u003e test was used for group comparisons of categorical variables. In contrast, for comparisons of continuous variables between Russian and Norwegian populations, we used linear regression analysis with and without adjustment for the following co-variates: age, sex, height, BMI, systolic and diastolic BP, HR, AF, smoking, pulmonary hypertension, and serum values for total, LDL, and HDL cholesterol; triglycerides; creatinine; high-sensitivity C-reactive protein; and HbA1C.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS v.28.0 (IBM Corp., Armonk, N.Y., USA), and statistical significance was set at a two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Continuous data are presented as mean (M)\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and NT-proBNP is presented as a skewed variable as the median (Me) with upper and lower quartiles (Q1; Q3). Furthermore, categorical characteristics are presented as absolute numbers (Abs) and proportions (%).\u003c/p\u003e \u003cp\u003eIntra-observer variability in strain and SR measurements was calculated from 135 randomly selected echocardiographic records comprising 1,620 segments. These were repeatedly analysed by the same observer 6\u0026ndash;12 months after the initial analysis. Subsequently, a second observer trained in the same echocardiography laboratory re-analysed the same images for inter-observer variability. Both observers performed at least 500 readings before performing the intra- and inter-observer studies. For the inter-observer variability of conventional systolic and diastolic parameters, 40 randomly selected echocardiograms from Russia and Norway were re-analysed by the main readers of the Russian and Norwegian reading laboratories who were blinded to the compared results. Subsequently, intra- and inter-observer variability were assessed as the limits of agreement derived from Bland-Altman plots, and intra-class correlation was calculated. Two previous publications based on the same dataset have described the intra- and inter-observer variability assessments in more detail (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents general demographic differences between the Russian and Norwegian population. The supplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e illustrates additionally differences between the two Russian populations from Arkhangelsk and Novosibirsk. Although the dataset was stratified by sex and age groups, Norwegian participants were slightly, but not significantly, older than Russian participants. Additionally, Norwegian participants were significantly taller, with a lower percentage showing an elevated BMI, which was highest in the Novosibirsk group. Russian participants had higher systolic and diastolic BP, as well as a higher prevalence of elevated systolic and diastolic BP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Concerning other cardiovascular risk factors, Norwegians displayed lower LDL cholesterol levels and the percentage of lipid-lowering drug usage was significantly lower (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, diabetes, daily smoking, renal failure, and elevated pro-BNP levels were less prevalent in the Norwegian population.\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\u003eCharacteristics of Norwegian or Russian participants\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\u003eRussian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorwegian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup \u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e598 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e54.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh BMI (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL Cholesterol (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh LDL Cholesterol (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e374 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Triglycerides (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid Lowering drugs (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh HbA1C (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking daily \u003csup\u003e\u0026sect;\u003c/sup\u003e (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinin (mmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u0026thinsp;\u0026plusmn;\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinin high (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHS CRP (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHx of renal failure (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHx of Cancer (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHx of Asthma (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHx of Stroke (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP (pmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (28/59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh NT-proBNP (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValvular heart disease\u0026thinsp;\u0026ge;\u0026thinsp;grade II (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA volume index high (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBMI: body mass index; BP: blood pressure; LDL: low density lipoproteins; HDL: high density lipoproteins; LV EF: left ventricle ejection fraction. \u0026sect;Refers to active current smoking; NT-proBNP in median (lower quartiles/upper quartiles)\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\u003eThe Norwegian population also had less participants with a history of angina, heart attack, or heart failure. However, the highest percentages of AF were registered among Norwegians, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, atrial volume index was significantly higher in the Russian population, which had the lowest number of patients with AF. The differences in antihypertensive drug use are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, Arkhangelsk had the highest prescription or self-medication rate, followed by Novosibirsk and Troms\u0026oslash; (the lowest rate).\u003c/p\u003e \u003cp\u003eTo assess the cardiac functional parameters, we compared the Russian and Norwegian subpopulations in their respective subgroups. The differences between the subgroups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The supplemental Table S2 shows the mean values of these measurements. Notably, in the normal and hypertensive subgroups, the mean heart rate was higher in the Russian participants than in the Norwegian participants while most of the adjusted systolic parameters like stroke volume longitudinal displacement, velocity and strain were not significantly different between the Russian and the Norwegian participants.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e of the supplements, the unadjusted longitudinal systolic strain was slightly lower in the Russian population than in the Norwegian population, and only the two hypertension groups showed significant differences. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates that SR S, SR E and SR A in most subgroups were equal in the Russian and Norwegian populations, whereas unadjusted SR E was slightly but still significantly lower in the group with elevated BP than in the other groups. Left ventricular (LV) diastolic functional parameters based on conventional Doppler indices (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed longer MV E DT and lower e\u0026rsquo; as possible indicators for impaired relaxation in the normal and hypertensive groups of the Russian population, with a longer MV E DT and higher LA volume index. Some indicators for elevated filling pressures like higher E/e\u0026rsquo; and higher E velocities were not significantly different between the adjusted groups. However, all parameters based on MV A and longitudinal function in the atrial contraction phase (i.e. SR A, a\u0026rsquo;) were significantly lower in some groups of the Russian population.\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\u003eMean difference between Norwegians and Russians for systolic and diastolic functional parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Normotensives\u003c/p\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControlled Hypertension\u003c/p\u003e \u003cp\u003eGroup C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCardiac\u003c/p\u003e \u003cp\u003eDisease\u003c/p\u003e \u003cp\u003eGroup D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup \u003cem\u003en (Norwegians/Russians)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441/351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251/374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62/150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163/317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eMean difference Norwegians - Russians (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal ES strain (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15 (-0.49 to 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.89 (-1.37 to -0.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.90 (-1.66 to -0.13)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06 (-0.66 to 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (-0.35 to 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.25 (-0.50 to 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.38 (-1.44 to 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.23 (-0.76 to 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal peak SR S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (-0.03 to 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.03 (-0.07 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05 (-0.11 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (-0.04 to 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (-0.05 to 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.07 to 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (-0.07 to 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.34 (-0.10 to 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal peak SR E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.07 to 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.06 (-0.01 to 0.11)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01 (-0.10 to 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.08 to 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00 (-0.06 to 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05 (-0.13 to 0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06 (-0.20 to 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04 (-0.06 to 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal peak SR A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.02 (-0.02 to 0.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.02 (-0.03 to 0.07)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03 (-0.05 to 0.10)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.02 (-0.08 to -0.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04 (0.03 to 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.08 (0.02 to 0.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07 (0.03 to 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17 (-0.09 to 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal displacement (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.81 (0.47 to 1.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.14 (0.64 to 1.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.41 (0.61 to 2.21)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37 (-0.27 to 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 (-0.45 to 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06 (-0.71 to 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.29 (-0.9to 0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09 (-0.97 to 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak velocity s\u0026acute; (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03 (-0.22 to 0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (-0.21 to 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.35 (-0.14 to 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 (-0.29 to 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16 (-0.43 to 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00 (-0.44 to 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09 (0.54 to 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (-0.42 to -0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak velocity e\u0026acute; (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.28 (-0.02 to 0.54)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.15 (-0.51 to 0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24 (-0.29to 0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08 (-0.53 to 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.49 (0.16 to 0.81)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 (-0.25 to 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01 (-0.71 to 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.41 (-0.95 to 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak velocity a\u0026acute; (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.31 (-0.50 to -0.12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08 (-0.32 to 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.45 (-0.85 to -0.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (-0.29 to 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.25 (-0.51 to-0.002)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.23 (-0.57 to 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.53 (-1.1 to -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05 (-0.37 to 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA volume Index (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.01 (1.03 to 2.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.31 (-2.99 to -1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.31 (-2.65 to 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.57 (--2.45 to 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05 (-0.12 to 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08 (-0.17 to -0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (-0.25 to 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.12 (-0.22 to -0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (1/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-4.30 (-5.56 to -3.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-2.54 (-4.28 to -0.80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-3.23 (-5.98 to -0.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-3.53 (-5.64 to -1.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-4.77 (-6.69 to -2.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32 (-2.97 to 2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-4.18 (-8.10 to- 0.28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.03 (-4.78 to 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke Volume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.36 (4.44 to 10.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.48 (2.76 to 10.21)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.16 (5.46 to 16.86)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7.44 (3.04 to 11.84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.04 (-1.21 to 7.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21 (-1.42 to 9.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.23 (-4.97 to 11.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39 (-4.13 to 8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV E/e\u0026acute;(1/1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (-0.48 to 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (-1.61 to 2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 (-0.40 to 2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22 (-2.16 to 2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.64 (-1.49 to 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07 (-3.53 to 3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.38(-0.75 to 3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.95 (-2.55 to 4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV E velocity (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02 (-0.04 to 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-002 (-0.04 to 0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.06 (-0.11 to -0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (-0.03 to 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09 (-0.02 to -0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02 (-0.06 to 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04 (-0.10 to 0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00 (-0.05 to 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV A velocity (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.03 (0.01 to 0.05)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04 (0.01 to 0.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02 (-0.03 to 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (-0.02 to 0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.05 (0.03 to 0.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04 (0.01 to 0.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.09 (0.03 to 0.15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01 (-0.03 to 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A ratio (1/1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.08 (-0.13 to -0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.07 (-0.12 to -0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (-0.20 to 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (-0.08 to 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.10 (-0.15 to -0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.09 (-0.16 to -0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.16 (-0.30 to -0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02 (-0.12 to 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV E deceleration time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-30.8 (-36.4 to-25.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-32.5 (-40.4 to -24.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-32.8 (-46.4 to-19.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-38.3 (-46.5 to -20.2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-32.4 (-41.2 to -23.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-32.0 (-44.2 to -19.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-43.2 (-63.7 to -22.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-38.8 (-49.6 to -26.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEjection fraction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.81 (-0.76 to 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-1.71 (-2.85 to 0.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.68 (-3.21 to -0.15)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-4.27 (-5.92 to -2.62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.61 (-1.68 to 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-3.18 (-4.93 to -1.42)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.87 (-4.03 to 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-3.68 (-5.99 to -1.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDifference between Norwegians and Russians (N-R) unadjusted and adjusted for age, sex, BMI, height, systolic blood pressure (BP), diastolic BP, heart-rate, atrial fibrillation, smoking, pulmonary artery pressure, serum values for total, LDL and HDL cholesterol, triglycerides, creatinine, high sensitive C-reactive protein and HBA1C age. sex. height. body mass index. blood pressure and heart-rate. atrial fibrillation and pulmonary hypertension; Heart rate was not adjusted for heart rate.\u003c/p\u003e \u003cp\u003eES: endsystolic; SR: strain rate; S: systolic; E: early diastolic; A: at atrial contraction; LA: left atirial; MV: mitral valve\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the unadjusted and adjusted linear regressions for speckle tracking and conventional systolic and diastolic parameters. After adjustment, the systolic longitudinal functional parameters and SR-E did not differ between the populations, while Russians still displayed reduced atrial SR and velocities, as well as a higher LA volume. Regarding conventional parameters, MV A velocity, E/A ratio, HR, and EF were higher, E DT was longer and SV was lower.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of functional systolic and diastolic parameters between Russians and Norwegians\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted Linear Regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eAdjusted Linear Regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorwegians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorwegians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRussians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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\u003eUnadjusted Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnadjusted Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted Mean Difference (CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal ES strain (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03 (-0.05 to 0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal peak SR S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03 (-0.05 to 0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal peak SR E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00 (-0.04 to 0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal peak SR A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.06 (0.03 to 0.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal displacement (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19 (-0.16 to 0.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak velocity s\u0026acute; (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.112\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05 (-0.23 to 0.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak velocity e\u0026acute; (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-6.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18 (-0.05 to 0.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak velocity a\u0026acute; (cm/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-6.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-6.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.25 (-0.42 to -0.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA volume Index (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e24.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e25.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-1.01 (-2.01 to -0.01)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e65.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e61.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e64.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-2.52 (-3.74 to -1.31)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke volume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e89.1\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e89.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e83.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.20 (0.51 to 5.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV E/e\u0026acute; (1/1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.096\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04 (-1.21 to 1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV E velocity (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.148\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01 (-0.03 to 0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV A velocity (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.04 (0.03 to 0.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE/A ratio ()\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.08 (-0.12 to -0.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMV E deceleration time (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e172\u0026thinsp;\u0026plusmn;\u0026thinsp;45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e207\u0026thinsp;\u0026plusmn;\u0026thinsp;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e173\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e207\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-36-3 (-41.9 to -30.7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEjection fraction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e54.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e55.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e55.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-2.37 (-3.07 to -1.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eLinear Regression Model adjusted for age, sex, BMI, height, systolic BP (blood pressure), diastolic BP, heart-rate, atrial fibrillation, smoking, pulmonary artery pressure, serum values for total, LDL and HDL cholesterol, triglycerides, creatinine, high-sensitive C-reactive protein and HbA1C. \u0026Dagger; Not corrected for Heart Rate. ES: end-systolic; SR: strain rate; S: systolic; E: early diastolic; A: at atrial contraction; PSS: post-systolic shortening; MV: mitral valve; LA: left atrial; TR: tricuspid regurgitation\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\u003eThe intra-observer variability is relevant for speckle tracking-derived parameters. The supplemental Table S3 shows good inter-observer variability for global S/SR measurements, without significant differences between the readings of the different populations. However, Table S4 in supplements demonstrates that the inter-observer variability for the conventional echocardiographic parameters introduced a significant error with the \u0026ldquo;overestimation\u0026rdquo; of EF, MV deceleration time, and SV in the Russian population, while the readings of MV E, A, and E/A ratio showed reliable intra-class correlation with insignificant deviations.\u003c/p\u003e \u003cp\u003eTable S5 in supplements shows the significant covariates of the linear regression model, where systolic parameters were mainly influenced by after-load-relevant factors such as body height, BMI, BP, HR, AF, and pulmonary hypertension, while general cardiovascular risk factors such as LDL, HDL, creatinine, high-sensitivity C-reactive protein levels (HS-CRP), and HBA1C were significant factors influencing diastolic function.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMain findings of the study\u003c/h2\u003e \u003cp\u003eIn this study, we found significantly different cardiac functional properties between the Russian and Norwegian populations. After adjustment for co-variates in the linear regression analysis, Russian participants still showed a lower SV and higher HR than Norwegian participants. In the Russian population, diastolic properties with higher MV E DT and larger left atria indicated impaired relaxation, while higher MV velocity E, E/A, and E/e\u0026acute; indicate higher filling pressures. In contrast to longitudinal systolic parameters, diastolic properties remained reduced in the Russian population after adjusting for factors known to influence systolic and diastolic cardiac function. Russians also tended to have lower systolic functional parameters; however, longitudinal strain and tissue velocities did not differ after adjustment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in cardiovascular risk factors and diseases\u003c/h2\u003e \u003cp\u003eThe Russian population has considerably higher cardiovascular morbidity and mortality rates than developed Western European populations (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The study was conducted as a part of the Heart to Heart collaboration(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) which was established to acquire new knowledge about the considerably higher premature mortality rate from cardiovascular diseases in Russia compared to Norway (735 vs 89 per 100.000 men and 239 vs 33 per 100.000 women, respectively, during 2012\u0026ndash;2016). In 2018, cardiovascular mortality rates per 100,000 inhabitants were 167 in Germany, 86 in France, 153 in Italy, 97 in Spain, 114 in Norway, and 431 in Russia. The average age of cardiovascular death was 81 years in Western European countries, compared to 80 years in Norway and 71 years in Russia(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe KYH and H2H studies aimed to compare two representative populations to gain more knowledge regarding cardiac function and its dependency on known influential factors (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This study confirms the previously described unfavourable risk profile of the Russian population. The Russian participants had higher diastolic and systolic BP, high-sensitivity C-reactive protein levels, BMI, prevalence of diabetes, active smoking, and renal failure (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Only serum cholesterol levels were similar in both populations; however, triglyceride levels were higher in the Russian population, possibly due to the higher incidence of diabetes. Furthermore, higher percentages of heart attacks, Q waves on the ECG, elevated NT-proBNP, stroke, and pulmonary hypertension reflect a higher prevalence of cardiovascular disease and heart failure in Russia (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAF, assessed through a questionnaire, was found to be paradoxically more prevalent in the Norwegian population, which showed lower atrial volumes\u0026mdash;an objective measure performed by a single reader in both populations. The diagnosis of AF is influenced by a low threshold for detection and the availability of medical consultations, which may be more accessible in Norway than in Russia. Generally, AF is known to be underdiagnosed, and the number of reported cases in an epidemiological study reflects several factors, including the accessibility of medical care, patient awareness, in addition to the true prevalence of the condition.\u003c/p\u003e \u003cp\u003eA study on the Novosibirsk population confirmed that the prevalence of AF based on resting ECGs was generally comparable to that of North American or European populations. Moreover, the prevalence of AF was even higher in the Novosibirsk population compared to these countries in age-groups over 65 years(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Specifically, the proportions of paroxysmal AF and persistent AF were reported to be approximately 40% and 20%, respectively, in a Russian population cohort and these conditions might be underdiagnosed. Additionally, individuals with AF are prone to both fatal and non-fatal CVD events, and could be underrepresented in the survey sample due to selection bias. This might explain the paradoxically low prevalence of AF reported in the Russian population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSystolic functional parameters, including GLS and GLSR\u003c/h2\u003e \u003cp\u003eSeveral physiological factors, such as after-load, HR, and filling properties, influence systolic GLS and GLSR. After-load depends on BP, ventricular size, and vascular resistance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), which explains the relationship between S/SR and body size and, thus, height and sex. In fact, BMI directly influences vascular resistance and blood pressure (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Additionally, age is known to reduce longitudinal function (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), but not significantly in the age range of 40\u0026ndash;69 years (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In a previous study, we showed that normotensive individuals taking antihypertensive drugs had lower global strain than normotensive controls (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Furthermore, hypertensives had a higher HR and SV than normotensives, while longitudinal S/SR and displacement were reduced, indicating that a higher SV in hypertension is generated by increased radial contraction.\u003c/p\u003e \u003cp\u003eIn the present study, the Russian population had a higher HR and lower SV. This indicates a higher sympathicotonia combined with cardio-depressive factors in Russians. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Russian participants had slightly reduced strain and systolic SR in most groups; however, the uncorrected differences were marginal. The combination of increasing S/SR with a higher sympathicotonia and longitudinal S/SR reducing factors\u0026mdash;such as higher BP and BMI and lower ventricular filling time at higher HRs\u0026mdash;diminished differences in longitudinal strain between the populations. Thus, differences in age, sex, height, BMI, HR, and BP could explain lower cardiac function in the Russian population. Moreover, most systolic longitudinal functional S/SR ratios were not significantly different after adjustment. Other factors such as cholesterol, triglycerides, renal function, and HS-CRP had no direct influence on LV longitudinal function. Therefore, as the uncorrected strains showed the highest differences in the hypertensive groups, the main factor for reduced function in the Russian population may have been the effect of BP on longitudinal systolic and diastolic function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDiastolic functional parameters\u003c/h2\u003e \u003cp\u003eHigher E-wave velocity and E/A ratios indicate higher filling pressures congruent with higher HR and lower SV. Additionally, higher e\u0026acute; and SR E may be caused by higher E wave velocities and filling pressures, while lower e\u0026acute; or SR E may be caused by impaired relaxation. Therefore, the opposite effect of reduced relaxation and increased filling pressures might be the reason for the small effect of population differences on SR E. Beyond age, sex, BMI, BP, HR, and AF HR, which showed a significant influence on systolic longitudinal parameters, cholesterol, triglycerides, creatinine, HS-CRP, and HBA1C showed significant effects on diastolic functional parameters, EF, SV, and HR. However, the remaining differences after adjustment indicate the influence of factors that were not investigated in the present study. Interestingly, diastolic function seems to be equally affected by factors connected with markers for diabetes, renal function, or inflammatory processes, such as the influence of pre- and after-loads.\u003c/p\u003e \u003cp\u003eThis study was not designed to investigate the effects of alcohol consumption on myocardial function parameters. However, there is a possible prominent difference in alcohol intake between the Russian and Norwegian populations, which has not been considered. Alcohol consumption is associated with impaired relaxation and high filling pressures (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In fact, a recent KYH sub-study of patients diagnosed with chronic alcohol usage showed the dependency of diastolic functional parameters such as e\u0026acute;, E/e\u0026rsquo;, and LA volume on alcohol consumption (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) in a subclinical setting. In another KYH sub-analysis, the association between measures of diastolic function, alcohol amount per session, and binge drinking (positive for E/e\u0026rsquo; and negative for e\u0026rsquo;) was reported in a general population sample (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Furthermore, other factors such as the psychosocial environment in relation to simpatico tone may also play a role in these differences. The Novosibirsk population includes less than 5% other than Russian or Ukrainian ethnicity, a percentage too small to consider ethnicity as a major confounder for diastolic function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMethodological considerations on inter-reader variability\u003c/h2\u003e \u003cp\u003eConventional diastolic functional parameters are primarily based on MV Doppler assessment. To avoid bias between laboratories at different locations, the S/SR analyses and atrial volumes were read by a single reader. Similarly, the TDI velocities were replaced with the STE peak values of the basal segmental velocities. The conventional Doppler parameters were measured in different laboratories in Russia and Norway. Theoretically, this might introduce a significant bias. However, as shown in Table\u0026nbsp;5, the inter-observer variability for MV Doppler-based parameters was excellent; therefore, this was unlikely to be a problem. Furthermore, the MV DT reduced by the systematic error would still be significantly longer in the Russian population than in the Norwegian population. This is congruent with larger LA volumes in the Russian population (which was read by a single reader), indicating delayed relaxation. Additionally, Doppler readings with large differences between MV A values between the populations correlated well with the findings of A velocities and SR A, indicating higher filling pressures.\u003c/p\u003e \u003cp\u003eTherefore, we interpreted that the differences in conventional diastolic parameters between populations were not reader-related. Furthermore, applying the systematic reading errors of the laboratories (Table\u0026nbsp;5) to the results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it is possible that the EF was significantly lower in the Russian population. It is also possible that the lower SV of the Russian population was underestimated. Lower EF in the Russian population correlated well with lower SV; however, these two parameters displayed the highest inter-reader variability, and should therefore be interpreted with caution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eVentricular dimensions derived from M-mode echocardiograms, such as septal thickness, myocardial mass, end-diastolic diameter, and tissue Doppler measurements, showed high systematic differences between the Russian and Norwegian reading groups and were therefore not included in the present study. However, we included EF, SV, and MV DT in the study, which are parameters with high inter-reader variability between the reading groups. Nonetheless, the analyses of these parameters should be interpreted with caution. EF and SV were the only systolic parameters with significant differences between the groups, which may indicate a systematic reading error instead of a cardiac functional difference. Previously, Iankuchykova et al. investigated differences in biochemical markers between these populations and suggested adjustments for different laboratories; however, we used the original values for biochemical markers (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The biomarkers were used only for adjustment and description; therefore, it is highly unlikely that the correction would have changed the outcome. Hypertension thresholds were defined following the 2018 ESC/ESH guidelines which are unchanged in the 2023 ESH guidelines. Blood pressure strongly influences cardiac systolic and diastolic function and might also affect participants with high normal blood pressure (130\u0026ndash;139 and 85\u0026ndash;89 mmHg), which were categorized as normal in the present study. However, we adjusted for blood pressure as a continuous variable across all four functional groups.\u003c/p\u003e \u003cp\u003eThe World Health Organisation reports a per-capita alcohol consumption of 5.8 and 18.7 litres in women and men, respectively, in Russia, while that in Norway is 3.2 and 11.6 litres for women and men, respectively (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Alcohol consumption and drinking behaviour in the Russian population may be important factors for higher mortality and heart failure rates. However, objective information on drinking patterns is difficult to obtain from questionnaire-based population studies; therefore, this might be important missing information (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In the present sub-study, only one question regarding regular alcohol consumption during the past year was available, which was answered positively by 93% of the Norwegian population and 82% of the Russian population. Owing to the lack of relevance, we decided not to include this risk factor in our analysis. We assume that alcohol consumption and drinking patterns might have had a prominent effect in the present study, especially on diastolic cardiac function (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe extent and significance of differences between diastolic measurements across groups were not uniform. However, when comparing the Norwegian and Russian populations within each of the normal and hypertensive groups, we observed a consistent pattern of differences. This indicates that despite some variability, meaningful conclusions can be drawn.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eUsing conventional and S/SR measures to compare the two populations, we found that the Russian population showed a lower systolic and diastolic cardiac function than the Norwegian population. Higher BP, HR, BMI, sex, and age were the most important factors explaining lower longitudinal function in the Russian population. However, even after further adjustment for known risk factors for suppressed longitudinal strain, including diabetes, cholesterol, and HS-CRP, a significant difference between the populations remained, indicating that other risk factors are important. Future studies are required to identify these risk factors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributorship statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eARo: study design, funding of the PhD project, statistical analyses,\u0026nbsp;and writing\u0026nbsp;of the manuscript. MK: data-collection, strain-analysis, statistical analysis,\u0026nbsp;and writing of the manuscript; HAC: artefact analysis and strain-analysis for inter-observer-variability; SM and ARy: funding, conduction of the data-collection in Russia, and critical revision of the manuscript for important intellectual content; HS: design funding and conduction of the Tromsø7 study and critical revision of the manuscript. AVK: conduction of the data collection in Arkhangelsk and critical revision of the manuscript for important intellectual content.\u0026nbsp;All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the contributions of\u0026nbsp;the KYH and Tromsø7 study participants. We would\u0026nbsp;also like to thank\u0026nbsp;Editage (www.editage.com) for\u0026nbsp;the English language editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHS has received lecture fees from Amgen and Novartis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe KYH study was a component of the International Project on CVD in Russia (IPCDR), funded by the Welcome Trust Strategic Award (No. 100 217), UiT The Arctic University of Norway,\u0026nbsp;the Norwegian Institute of Public Health, and the Norwegian Ministry of Health and Social Affairs. SM\u0026nbsp;and Ary were supported by the Russian Academy of Science, State Target (grant number:\u0026nbsp;№ FWNR-2024-0002). The study sponsor/funder was not involved in the design of the study; the collection, analysis, and interpretation of data; or\u0026nbsp;the writing of the report; and no restrictions were imposed\u0026nbsp;on the publication of the report. The first author received a PhD grant from Helse Nord RHF (HNF 1458-19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Know Your Heart and Tromsø Studies; however, restrictions apply to the availability of these data, which were used under licence for the current study and are not publicly available. However, data from this sub-study\u0026nbsp;are\u0026nbsp;available from the corresponding authors upon reasonable request, with permission\u0026nbsp;from the Know Your Heart Steering Group and the corresponding author of this manuscript. All such KYH data requests will be guided by protecting personal information, confidentiality agreement with participants, and informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with\u0026nbsp;the principles of the Declaration of Helsinki. The Know Your Heart study was approved by the ethics committees of the London School of Hygiene\u0026nbsp;and Tropical Medicine (approval number: 8808, received 24/02/2015), Novosibirsk State Medical University (approval number: 75, received 21/05/2015), the Institute of Preventative Medicine, Novosibirsk (no approval number, approval received 26/12/2014), and the Northern State Medical University, Arkhangelsk (approval number: 01/01–15, received 27/01/2015).\u0026nbsp;Written informed consent was obtained from all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTownsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J. 2016;37(42):3232-45.\u003c/li\u003e\n\u003cli\u003eCook S, Malyutina S, Kudryavtsev AV, Averina M, Bobrova N, Boytsov S, et al. Know Your Heart: Rationale, design and conduct of a cross-sectional study of cardiovascular structure, function and risk factors in 4500 men and women aged 35-69 years from two Russian cities, 2015-18. Wellcome Open Research. 2018;3.\u003c/li\u003e\n\u003cli\u003eCook S, Eggen AE, Hopstock LA, Malyutina S, Shapkina M, Kudryavtsev AV, et al. Chronic Obstructive Pulmonary Disease (COPD) in Population Studies in Russia and Norway: Comparison of Prevalence, Awareness and Management. Int J Chron Obstruct Pulmon Dis. 2021;16:1353-68.\u003c/li\u003e\n\u003cli\u003eIakunchykova O, Averina M, Wilsgaard T, Malyutina S, Kudryavtsev AV, Cook S, et al. What factors explain the much higher diabetes prevalence in Russia compared with Norway? Major sex differences in the contribution of adiposity. BMJ Open Diabetes Res Care. 2021;9(1).\u003c/li\u003e\n\u003cli\u003eCook S, Hopstock LA, Eggen AE, Bates K, Iakunchykova O, Kontsevaya A, et al. Pharmacological management of modifiable cardiovascular risk factors (blood pressure and lipids) following diagnosis of myocardial infarction, stroke and diabetes: comparison between population-based studies in Russia and Norway. BMC Cardiovasc Disord. 2020;20(1):234.\u003c/li\u003e\n\u003cli\u003eCook S, Solbu MD, Eggen AE, Iakunchykova O, Averina M, Hopstock LA, et al. Comparing prevalence of chronic kidney disease and its risk factors between population-based surveys in Russia and Norway. BMC Nephrol. 2022;23(1):145.\u003c/li\u003e\n\u003cli\u003eHopstock LA, Kudryavtsev AV, Malyutina S, Cook S. Hazardous alcohol consumption and problem drinking in Norwegian and Russian women and men: The Tromso Study 2015-2016 and the Know Your Heart study 2015-2018. Scand J Public Health. 2021:14034948211063656.\u003c/li\u003e\n\u003cli\u003eCook S, Saburova L, Bobrova N, Avdeeva E, Malyutina S, Kudryavtsev AV, et al. Socio-demographic, behavioural and psycho-social factors associated with depression in two Russian cities. J Affect Disord. 2021;290:202-10.\u003c/li\u003e\n\u003cli\u003eKornev M, Caglayan HA, Kudryavtsev A, Malyutina S, Ryabikov A, Stylidis M, et al. Novel approach to artefact detection and the definition of normal ranges of segmental strain and strain-rate values. Open Heart. 2022;9(2).\u003c/li\u003e\n\u003cli\u003eSoufi Taleb Bendiab N, Meziane-Tani A, Ouabdesselam S, Methia N, Latreche S, Henaoui L, et al. Factors associated with global longitudinal strain decline in hypertensive patients with normal left ventricular ejection fraction. Eur J Prev Cardiol. 2017;24(14):1463-72.\u003c/li\u003e\n\u003cli\u003eSoufi Taleb Bendiab N, Ouabdesselam S, Henaoui L, Lopez-Sublet M, Monsuez JJ, Benkhedda S. Impact of Diabetes on Cardiac Function in Patients with High Blood Pressure. Int J Environ Res Public Health. 2021;18(12).\u003c/li\u003e\n\u003cli\u003eMarwick TH, Leano RL, Brown J, Sun JP, Hoffmann R, Lysyansky P, et al. Myocardial strain measurement with 2-dimensional speckle-tracking echocardiography: definition of normal range. JACC Cardiovasc Imaging. 2009;2(1):80-4.\u003c/li\u003e\n\u003cli\u003eAdamu U, Schmitz F, Becker M, Kelm M, Hoffmann R. Advanced speckle tracking echocardiography allowing a three-myocardial layer-specific analysis of deformation parameters. Eur J Echocardiogr. 2009;10(2):303-8.\u003c/li\u003e\n\u003cli\u003eStylidis M, Leon DA, Rsner A, Schirmer H. Global myocardial longitudinal strain in a general population-associations with blood pressure and subclinical heart failure: The Tromso Study. Int J Cardiovasc Imaging. 2020;36(3):459-70.\u003c/li\u003e\n\u003cli\u003eMichael Kornev HAC, Alexander V Kudryavtsev, Sofia Malyutina, Andrey Ryabikov, Michael Stylidis, Henrik Schirmer, Assami R\u0026ouml;sner. A new approach to artefact detection and definition of normal ranges of segmental strain and strain-rate values. 2022.\u003c/li\u003e\n\u003cli\u003eAverina M, Stylidis M, Brox J, Schirmer H. NT-ProBNP and high-sensitivity troponin T as screening tests for subclinical chronic heart failure in a general population. ESC Heart Fail. 2022;9(3):1954-62.\u003c/li\u003e\n\u003cli\u003ePrendergast B, Vahanian A. The 2021 ESC/EACTS guidelines for the management of valvular heart disease: a new template for Heart Teams and their patients. Cardiovasc Res. 2022;118(1):e11-e3.\u003c/li\u003e\n\u003cli\u003eWilliams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021-104.\u003c/li\u003e\n\u003cli\u003eMancia G, Kreutz R, Brunstrom M, Burnier M, Grassi G, Januszewicz A, et al. 2023 ESH Guidelines for the management of arterial hypertension The Task Force for the management of arterial hypertension of the European Society of Hypertension: Endorsed by the International Society of Hypertension (ISH) and the European Renal Association (ERA). J Hypertens. 2023;41(12):1874-2071.\u003c/li\u003e\n\u003cli\u003eMichael Kornev HAC, Alexander V Kudryavtsev, Sofia Malyutina, Andrey Ryabikov, Michael Stylidis, Henrik Schirmer, Assami R\u0026ouml;sner. The influence of hypertension on systolic and diastolic left ventricular function including segmental strain and strain rate. 2022.\u003c/li\u003e\n\u003cli\u003e(Rosstat) FSSS. Official statistics, Demography https://rosstat.gov.ru/free_doc/2019/demo/edn10-19.htm2020 [\u003c/li\u003e\n\u003cli\u003eAssociation AH. Heart Disease and Stroke Statistics\u0026mdash; 2020 Update Circulation. 2020;141:e139\u0026ndash;e5962020 [\u003c/li\u003e\n\u003cli\u003e(HFA-DB) EHfAFoD. European Health for All Family of Databases (HFA-DB) https://gateway.euro.who.int/en/hfa-explorer/ 2021 [\u003c/li\u003e\n\u003cli\u003eOrganisation WH. WHO Mortality Database. http://www.who.int/healthinfo/mortality_data/en/2021 [\u003c/li\u003e\n\u003cli\u003eCook S, Malyutina S, Kudryavtsev AV, Averina M, Bobrova N, Boytsov S, et al. Know Your Heart: Rationale, design and conduct of a cross-sectional study of cardiovascular structure, function and risk factors in 4500 men and women aged 35-69 years from two Russian cities, 2015-18. Wellcome open research. 2018;3:67-.\u003c/li\u003e\n\u003cli\u003eIakunchykova O, Schirmer H, Leong D, Malyutina S, Ryabikov A, Averina M, et al. Heavy alcohol drinking and subclinical echocardiographic abnormalities of structure and function. Open Heart. 2021;8(1).\u003c/li\u003e\n\u003cli\u003eShapkina M, Ryabikov A, Mazdorova E, Titarenko A, Avdeeva E, Mazurenko E, et al. The Determinants of the 13-Year Risk of Incident Atrial Fibrillation in a Russian Population Cohort of Middle and Elderly Age. J Pers Med. 2022;12(1).\u003c/li\u003e\n\u003cli\u003eR\u0026ouml;sner A, Bijnens B, Hansen M, How OJ, Aarsaether E, M\u0026uuml;ller S, et al. Left ventricular size determines tissue Doppler-derived longitudinal strain and strain rate. Eur J Echocardiogr. 2009;10(2):271-7.\u003c/li\u003e\n\u003cli\u003eMalyutina; SK, Voronina; E, Guseva; V, Yasukevich; N, Palekhina; Y, Shakhmatov; S, et al. Echocardiographic parameters in relation to patterns of alcohol intake: interim analysis. Eur J Prev Cardiol2019. p. S95.\u003c/li\u003e\n\u003cli\u003eLakunchykova O, Averina M, Wilsgaard T, Watkins H, Malyutina S, Ragino Y, et al. Why does Russia have such high cardiovascular mortality rates? Comparisons of blood-based biomarkers with Norway implicate non-ischaemic cardiac damage. J Epidemiol Community Health. 2020;74(9):698-704.\u003c/li\u003e\n\u003cli\u003eOrganization WH. Global status report on alcohol and health https://apps.who.int/iris/handle/10665/274603]2018 [\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5307004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5307004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eCardiovascular morbidity and mortality rates are high in Russia and it is likely that this reflects a similar impact on the general cardiac health of the population. The current study seeks to compare standard echocardiography and strain-based measurements between Russian and Norwegian populations, while also exploring their links to hemodynamic and risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study included echocardiographic measurements of 1,192 participants from Arkhangelsk and Novosibirsk, Russia, and 917 from the Tromsø Study population, Norway. The sample included men and women aged 40–69 years. Normalcy, defined as the absence of hypertension or indicators of CVD, was observed in 840 individuals. We performed conventional echocardiography and analysed two-dimensional speckle-tracking longitudinal strains, including systolic, early-, and late-diastolic SR values. The study population was divided into four groups: normal, controlled hypertension, hypertensive blood pressure, and cardiac disease. Echocardiographic parameters were compared between the Russian and Norwegian populations,adjusted for age, sex, height, body mass index, blood pressure, heart rate (HR), atrial fibrillation (AF), smoking, pulmonary hypertension, and serum values for total, LDL (low density lipoprotein), and HDL (high density lipoprotein) cholesterol; triglycerides; creatinine; high-sensitivity C-reactive protein; and HbA1C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Russians showed a tendency towards lower longitudinal systolic functional parameters, which were most prominent in the normotensive group. However, these differences became insignificant after adjusting for parameters that influence pre- and after-loads. Russians also had a lower stroke volume, higher HR, higher left atrial volume, lower A, and higher E/A ratio, indicating a higher incidence of diastolic dysfunction in the Russian population that persisted after adjustments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e After adjusting for factors that influence cardiac function, there were no differences in systolic functional parameters betweenthe Norwegian and Russian populations. However, differences in diastolic parameters, which persisted after adjusting for conventionally influential factors, indicated unexplained underlying causes of diastolic dysfunction in the Russian population.\u003c/p\u003e","manuscriptTitle":"Cardiac systolic and diastolic function in relation to cardiovascular risk factor distribution: a comparison of strain-rate imaging in Russian and Norwegian populations Heart-to-Heart: Norwegian-Russian multilevel educational collaboration in cardiovascular disease epidemiology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 13:54:23","doi":"10.21203/rs.3.rs-5307004/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":"bc3248dd-6495-45ce-9a77-f723322bc085","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T14:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-09 13:54:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5307004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5307004","identity":"rs-5307004","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}