Electrocardiogram Abnormalities and CVD Mortality: Prospective Cohort Study of Tehran Lipid and Glucose Study

doi:10.21203/rs.3.rs-3996359/v1

Download PDF

Research Article

Electrocardiogram Abnormalities and CVD Mortality: Prospective Cohort Study of Tehran Lipid and Glucose Study

https://doi.org/10.21203/rs.3.rs-3996359/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: The application of electrocardiogram (ECG) abnormalities alongside other clinical indicators can strengthen the hypothesis of improving the diagnosis and increasing the accuracy of screening for cardiovascular diseases. The present study aimed to determine the risk of mortality from cardiovascular diseases (CVD) based on ECG abnormalities, in addition to other known risk factors, within the Tehran Lipid and Glucose Study (TLGS) cohort population.

Methods: In this prospective cohort study conducted within the TLGS framework, a total of 6422 individuals aged 30 years and older, with ECG data and without a history of cardiovascular diseases, were examined over an average follow-up period of approximately 9 years. Logistic regression models (adjusted for age, gender, abdominal obesity, BMI, high blood pressure, LDL cholesterol levels, fasting blood glucose, smoking status, education level) were used to estimate the odds ratios (OR) for factors associated with CVD mortality.

Results: Overall, 233 CVD-related deaths occurred during the follow-up period. Individuals with Q-wave (OR=2.060, 95% CI: 1.327-3.198, P=0.001), and T-wave (OR=1.990, 95% CI: 1.327-3.986, P=0.001) abnormalities had about two times higher risk of CVD mortality compared to those without these abnormalities. However, ECG abnormalities including ST-segment (OR=1.169, 95% CI: 0.728-1.879, P=0.518), QRS duration (OR=0.780, P=0.492, 95% CI: 0.385-1.582), and PR interval (OR=1.406, 95% CI: 0.297-6.662, P=0.668) did not show a significant association with CVD mortality.

Conclusion: Among the ECG abnormalities (ST, T, Q, PR, QRS), Q and T abnormalities are associated with CVD mortality. It can be concluded that ECG abnormalities in asymptomatic adults are related to the risk of CVD mortality and can serve as warning factors for the development of mortality from CVD.

Mortality

Cardiovascular diseases

Electrocardiogram

Cohort

Non-communicable diseases are the leading cause of death worldwide and a significant barrier to sustainable human development (1). In 2011, the United Nations officially recognized non-communicable diseases, including cardiovascular diseases, as a major global health concern and set forth plans to reduce the impact of these diseases as a key priority (2). Reducing premature deaths from non-communicable diseases by one-third is recognized as the third Sustainable Development Goal (3).

CVD encompasses cardiac complications and ischemic heart diseases (IHDs), which play a significant role in reducing quality of life and causing mortality at a global level (4). The prevalence of CVD was reported at 523 million worldwide in 2019. From 1990 to 2019, the number of CVD-related deaths increased from 18.6 million to 21.1 million, with approximately 80% (5.2 million) of these deaths occurring in Asian countries (5, 6).

Metabolic disorders such as diabetes, high levels of LDL cholesterol, low levels of HDL cholesterol, high blood pressure, and tobacco use are among the main factors Incidence to the development of cardiovascular diseases (7). One of these cardiovascular diseases is acute myocardial infarction (AMI), commonly known as a heart attack, which occurs due to the reduction or cessation of blood flow to a part of the heart, leading to myocardial necrosis(8). Stroke is the second leading cause of death and the third leading cause of disability worldwide, with its burden rapidly increasing in low- and middle-income countries that cannot often address the challenges it poses (9). The Incidence of cardiovascular diseases varies from country to country and from one culture to another (10, 11). In Iran, CVD is the leading cause of death, accounting for approximately one million disability-adjusted life years (DALYs) and contributing to 46% of all deaths and 20-23% of the disease burden (12). Since many patients do not exhibit symptoms of cardiovascular diseases before the first clinical events and remain undiagnosed, the identification of asymptomatic high-risk individuals is of paramount importance for early prevention and timely intervention (13).

The electrocardiogram (ECG) is a widely used clinical test that is comprehensive, accessible, inexpensive, and non-invasive. It allows for the analysis of the electrical activity of the heart through 12 leads. This test is extensively utilized for the diagnosis of cardiac conditions such as cardiac enlargement, arrhythmias, myocardial infarctions (MIs), and more (14). Several previous studies have indicated a significant clinical association between ECG abnormalities and the risk of mortality from cardiovascular diseases (CVD) and coronary heart disease (CHD) (15-17).

In a study involving 16,793 Korean individuals, Chou and colleagues discovered that out of the 1,037 individuals with ST-T segment abnormalities, there was a higher incidence of atrial fibrillation or flutter during a 10-year follow-up compared to those without such abnormalities (18). Another prospective study conducted by Greenland and colleagues, spanning 22 years and including 7,985 women and 9,630 men, found that even mild T-wave or ST-T segment abnormalities were associated with an increased risk of mortality. These abnormalities can serve as markers for an elevated risk of coronary heart disease (CHD) and cardiovascular disease (CVD) (19).

Considering the accessibility, affordability, and non-invasive nature of ECG, incorporating the assessment of ECG abnormalities alongside other clinical indicators could improve the accuracy and effectiveness of screening for cardiovascular diseases. Identifying high-risk groups for CVD at an early stage, utilizing the wealth of cardiac and vascular data available in the Iranian population, holds promising clinical significance for risk stratification and treatment management. Therefore, this study aimed to investigate the relationship between ECG abnormalities and mortality resulting from cardiovascular diseases.

Study design, setting and participants

This study was conducted within the framework of the Tehran Lipid and Glucose Study (TLGS), which is a population-based cohort study in Tehran aimed at determining the incidence and prevalence of non-communicable diseases and their risk factors. This study has been previously described in detail (20).

All individuals aged 30 and above who were participants in this cohort study, had no prior history of cardiovascular outcomes at the beginning of the study and had at least one electrocardiogram measurement were included in the study. They were followed up for an average of approximately 9 years. The demographic information of these individuals was extracted based on the study objectives.

ECG abnormalities in these individuals were classified according to Minnesota codes, and the necessary codes for this study were determined based on consultations with experts and consultants specialized in cardiovascular diseases. The individuals were then divided into two groups based on the presence or absence of ECG abnormalities, and further categorized into two groups based on the occurrence of the specific outcome of interest during the follow-up period and those who did not have the desired outcome.

Definition of ECG terms

A normal electrocardiogram (ECG) consists of a P wave, which represents atrial depolarization, a QRS complex resulting from ventricular depolarization, and a T wave resulting from ventricular repolarization. The QRS complex is usually, but not always, composed of three distinct waves: Q, R, and S. The time interval between the onset of the P wave and the onset of the Q wave is referred to as the PR interval. In the absence of a Q wave, it is called the PR segment. In abnormal conditions where the impulse is conducted slowly through the atrioventricular node, the PQ interval is prolonged, typically ranging from 0.25 to 0.40 seconds. The time interval between the beginning of the Q wave and the end of the T wave is called the QT interval, which normally lasts about 0.35 seconds and represents the duration of ventricular contraction. The QT interval can vary in various disorders such as calcium concentration disturbances (hypocalcemia and hypercalcemia). The QRS complex reflects the spread of depolarization through the ventricles, and any condition that causes abnormal conduction of impulses will alter the shape, voltage, or duration of the QRS complex. Some diseases can cause such severe damage to the myocardium that it struggles to generate a normal membrane potential after each beat. As a result, certain muscle fibers may produce longer-than-usual electrical currents, leading to an abnormal T wave shape. If, due to the inability of some myocardial fibers to repolarize between two beats, a segment of the electrocardiogram between the S and T waves (ST segment) moves above or below the baseline, it can be certain that at least a portion of the ventricular muscle has been severely damaged, which occurs in acute heart attacks. Electrocardiography can be used to diagnose abnormal heart rhythms. For example, in heart block, the P waves are completely separate from the QRS and T waves. In atrial fibrillation, clear P waves are not observed, but numerous small waves repeat indefinitely on the electrocardiogram. Finally, in ventricular extrasystoles, occasional QRS complexes appear that are completely out of rhythm and asynchronous with the rest of the electrocardiogram (21).

Definition of CVD

CVD mortality is defined as any fatal event resulting from coronary artery disease and stroke (a neurological deficit occurring within the past 24 hours) (22). The details of the set of cardiovascular outcomes have been previously published (20, 23)

Dependent variable

The initial variable of interest, CVD mortality, was classified as a binary variable (occurrence and non-occurrence). In logistic regression analysis, it was transformed into a binary variable of Yes or No CVD mortality.

Data entry and analysis

In this study, statistical analysis was conducted using Stata17 software. The baseline characteristics of the study population were described in terms of mean values and standard deviations for continuous variables and frequencies for categorical variables. Comparison of basic characteristics between men and women was done using Student's t-test for continuous variables with normal distribution, chi-square test for categorical variables, and Mann-Whitney U statistic for skewed and ordered variables. The incidence rate of CVD death (95% CI) was calculated by dividing the number of new cases of CVD mortality by the total number of people who entered the study at the beginning for each gender.

Logistic regression models were used to examine the relationship between electrocardiographic abnormalities and CVD mortality while adjusting for confounding variables and the relevant assumptions were checked. Various factors, such as age, gender, marital status, education level, body mass index, abdominal obesity, blood pressure, LDL cholesterol levels, fasting blood glucose, smoking status, and specific electrocardiographic abnormalities (Q, T, ST, QRS, PR), were included in the regression models. The assumptions of multicollinearity were checked using variance inflation factors (VIF) and tolerances. Variables with p-values ˂ 0.2 in the univariate logistic model were selected for the multiple logistic regression model. but due to the conceptual and statistical overlap among HDL, LDL, total cholesterol, and triglycerides, only the LDL variable, along with gender, age, abdominal obesity, systolic blood pressure, and smoking status, was included in the multiple regression model. Abnormalities in QRS and PR intervals had P-values > 0.2 but were included in the model as exceptions The significance level was set at 0.05.

Finally, the variables of age, gender, abdominal obesity, systolic blood pressure, high levels of LDL cholesterol, smoking status, and abnormalities in Q, T, ST, QRS, and PR were entered into a multiple logistic regression model. A significance level of 0.05 was considered in the multivariable logistic regression model. Ultimately, for each of the variables, the odds ratio (OR) and its 95% confidence interval were estimated.

In this study, a total of 6,422 individuals aged 30 years and above, with ECG data and no history of coronary artery disease, were examined. The mean age of the participants was 47.58 ± 12.56 years, and 3,627 (56.60%) of them were women. About 33.26% of the participants were in the age group younger than 40 years, 87.82% of them were married, and 44.97% had education levels higher than a high school diploma. 43.56% of the individuals were overweight, and 25.71% had abdominal obesity. Nearly half of the participants had normal blood pressure (53% had normal systolic blood pressure, and 50.7% had normal diastolic blood pressure). 34.87% had very high LDL cholesterol levels, and 71.67% had normal fasting blood glucose levels. The highest number of deaths from cardiovascular diseases occurred in men (144 individuals, 61.80%) and the age group of 60-69 years (94 individuals, 40.52%), And 109 of them (46.78%) had an abnormality in ECG (Table 1).

The variables of gender, age, abdominal obesity, systolic blood pressure, smoking status, LDL, Q, ST, T, QRS, and PR were entered into a multiple regression model (Table 2).

The results of the study showed that the odds ratio of CVD mortality in men was 74% higher than in women (OR=1.743, 95%, CI: 1.232-2.465, P=0.002). For each year of increasing age, the chance of CVD mortality increased by 0.078% (OR=1.078, 1.062–1.093, P<0.001). Systolic blood pressure (OR=1.014, 1.007-1.020, P<0.001) and LDL (OR=1.006, 1.002-1.009, P=0.001) had a significant and direct relationship with the intended outcome. In people with electrocardiogram abnormalities, Q wave (OR=2.060, 1.327-3.198, P<0.001) and T wave (OR=1.990, 1.327–2.986, P=0.001) CVD mortality was 106% and 99% higher than people without these abnormalities, respectively. The chance of CVD mortality in people with ST-segment abnormalities was 16% (OR=1.169, 0.728–1.879, P=518), and in people with abnormalities PR 40% (OR=1.406, 0.297-6.663, P=0.668) was more ECG than people without these abnormalities, but their relationship was not significant. No significant relationship was observed between QRS abnormality and CVD mortality (OR=0.780, 0.385-1.583, P=492) (Table 2).

This study examined the association between electrocardiogram (ECG) abnormalities and death resulting from cardiovascular diseases (CVD). The study findings demonstrated that the occurrence of CVD-related death was more prevalent in individuals with ECG abnormalities compared to those without these abnormalities. Among the ECG abnormalities studied (ST, T, Q, PR, QRS), significant associations were observed between Q and T wave abnormalities and death due to CVD. this relationship was adjusted for abdominal obesity, diastolic blood pressure, HDL, LDL, total cholesterol, triglycerides, and smoking status.

Among the risk factors examined in this study, age was directly associated with CVD mortality. Several studies have also identified older age, particularly over 60 years, as a major risk factor for this mortality (24-26). However, most of the conducted studies have primarily focused on elderly individuals and those at risk of Incidence cardiovascular diseases, limiting the generalizability of the findings to younger or healthier populations and different ethnic groups (27).

Systolic blood pressure also showed a direct relationship with CVD death. A cohort study reported a gradual increase in the risk of CVD with higher baseline levels of SBP and diastolic blood pressure (DBP), where each additional 20 mmHg increase in SBP and 10 mmHg increase in DBP were associated with a twofold higher risk of Incidence CVD (28). Other evidence indicates that high blood pressure is the primary cause of long-term consequences such as heart failure (29). It has been estimated that high blood pressure accounts for 49% of the attributable risk for coronary heart disease (CHD) and 26% for stroke (30). However, these estimates may potentially underestimate the true contribution of high blood pressure to CVD due to residual confounding in many cohort studies that rely on a limited number of BP measurements (31). A meta-analysis of 42 trials reported a linear association between average SBP and the risk of death from cardiovascular and coronary artery diseases (32). Another systematic review and meta-analysis found that both long-term and short-term fluctuations in systolic blood pressure are associated with mortality from all causes (27).

Regarding the findings of this study, BMI and FBS did not demonstrate a significant association with the outcome of interest. Similarly, a study conducted by Fereydoun Azizi and colleagues using the same population data reported similar findings over a 10-year follow-up period (33). While obesity has been recognized as a risk factor for CVD in other studies (34-36), conflicting opinions exist regarding the effects of obesity and overweight on mortality. An analysis conducted in the United States estimated that obesity contributes to 300,000 deaths per year (37). Another study also showed that the risk of mortality from any cause is higher in individuals with a high BMI (38). Recent studies have presented more varied associations between obesity and mortality. The risk of mortality associated with obesity has mainly been observed among younger populations, while it has been reported to have a protective effect among older individuals (39-42). Additionally, being overweight has been associated with a 20% reduction in overall mortality risk and a 40% increase in the risk of Incidence of CVD in the Iranian population (43). Regarding FBS, contrary to the results of this study, another study estimated that diabetes accounts for 11% of deaths from cardiovascular in an adult population with a 10% prevalence of diabetes (44). Since the data from the mentioned studies are predominantly from high-income countries, it may not accurately estimate the attributed burden of cardiovascular disease to diabetes in low- and middle-income countries. Another study conducted on postmenopausal women found that fasting glucose in the high range was significantly associated with cardiovascular diseases and all-cause mortality while fasting glucose in the low range did not show such an association (45). This discrepancy in results may be due to variations in the prevalence of diabetes across different studies, which are somewhat influenced by gender distribution differences.

The present study demonstrated a significant association between LDL and the study outcome. Other studies (46-48) have also confirmed these findings. The Kronmal et al. study yielded similar results to this study in all age groups (49). A meta-analysis of 53 randomized controlled trials concluded that a reduction of 1 mmol/L in LDL-C decreases the risk of cardiovascular mortality (50). However, other studies have shown contradictory results regarding the association between LDL levels and CVD mortality compared to the findings of this study.

In this study, no association was found between WHR and death from CVD, while another study accepted this association in women (51). However, Timothybourne and colleagues stated that abdominal obesity, as assessed by waist-to-hip ratio, is a better predictor than waist circumference for CVD and CHD mortality, which, in turn, is a better predictor than BMI (52). In a meta-analysis conducted in Britain, WC and WHR were found to have the strongest association with an increased risk of CVD and all-cause mortality, and among the two, WHR showed the highest level of association. In multivariable models, BMI was not associated with CVD incidence and had an inverse association with all-cause mortality (53). Several other studies have also introduced WHR as the most useful obesity measure for identifying individuals at risk of CVD (54-58). However, in all of these studies, after age matching and controlling for other group characteristics, this association was observed.

In this study, the odds ratio of death from CVD was higher in men than in women, and the study by Woodward and colleagues also reached a similar conclusion (25). Another study stated that the rate of CVD mortality is higher in men during younger ages but higher in women, particularly around the age of 60 (26).

Multiple studies have found that being unmarried (never married, divorced, or widowed) is associated with an increased likelihood of Incidence of CVD and death from CHD (59-62). Another study showed that CVD mortality is higher in married men than in unmarried men, but in women, the reverse relationship holds (63). This study did not show a significant association between marital status and death from CVD.

The association between electrocardiogram (ECG) abnormalities and death from cardiovascular diseases (CVD) has been examined in previous studies. This study findings demonstrated that abnormal ECG findings initially increased the risk of CVD mortality by 3.3 times and, after adjusting for known risk factors, increased the risk of CVD-related mortality by 1.93 times. Similar results were reported by the Health ABC study, which showed a significant association between major and minor ECG abnormalities and an increased risk of cardiovascular disease (CAD) (64).

In a prospective population-based study conducted by Adam Goldman and colleagues, involving 2,601 individuals without known CVD, the cumulative occurrence of CVD and overall mortality was monitored over 23 years. They also found that ECG abnormalities were associated with a more than 46% increased risk of developing CVD, and non-specific T-wave changes and left ventricular axis deviation were significantly associated with mortality (65).

A study in Lithuania reported that individuals with ECG abnormalities, MI, and ischemic changes had a 2.5-fold and 4.4-fold increased risk of death from CVD in men and a 1.51-fold and 2.56-fold increased risk in women compared to those without ECG abnormalities. Utilizing ECG abnormalities alongside established risk factors improved the early identification of individuals at risk of CVD (66).

In a systematic review conducted by Daniel E. Jonas and colleagues, which examined the screening of cardiovascular risk using resting and exercise ECG, 16 studies (N=77,140) were reviewed. In two clinical trials (n=1,151) involving adults aged 50 to 75 with diabetes, no significant relationship was found between screening with exercise ECG and cardiovascular outcomes. No clinical trial evaluating screening with resting ECG was found in their searches. Evidence from 5 cohort studies (n=9,582) screening with exercise ECG and 9 cohort studies (n=66,407) screening with resting ECG demonstrated that adding ECG abnormalities to known risk factors improved the prediction of cardiovascular diseases (67).

In a prospective study by Jingya Niu and colleagues, involving 2,470 participants with ECG abnormalities, 464 CVD events were observed during the follow-up period (68, 69).

Several studies have also found a significant association between ECG abnormalities and known risk factors for cardiovascular diseases, including a cross-sectional study conducted on 31,399 Korean participants that used the Novacode system to examine ECG abnormalities (70), another cross-sectional study on an Iranian population with 3,723 participants that used the Minnesota coding system to classify ischemic ECG manifestations (71), and a multi-ethnic cross-sectional study in the United States involving 6,765 participants (Caucasian, Chinese, African-American, and Hispanic) that used the Minnesota coding system to classify ECG abnormalities (72). Recent studies have also shown that ECG analysis using artificial intelligence-based ECG learning approaches may predict mortality, cardiac arrhythmias, cardiac function, heart failure, and valvular heart diseases (73-79).

In the study by Xiaojian Zhang et al., patients with T-wave abnormalities, ST segment abnormalities, and ST-T abnormalities had lower overall and cardiovascular survival rates compared to patients without any ECG abnormalities (80). Other studies have demonstrated the association of ischemic ECG findings or ST-T abnormalities with an increased risk of Incidence of CVD, cardiovascular mortality, and all-cause mortality (81, 82). However, in this study, a significant relationship between ST abnormalities and death from cardiovascular diseases was not observed, which is consistent with the findings of the study by Caravallo et al., suggesting that these differences in results may be due to the use of different models in the studies or differences in the selected sample populations (83).

The main strengths of this study include a large sample size, a long-term cohort study design, the involvement of a minimal number of cardiovascular specialists in interpreting all ECGs, and efforts to minimize observer variability through adjustment using numerous intervention variables. However, the study also has limitations, such as the possibility that despite the use of many intervention variables in the adjustment method, some unmeasured confounders may still interfere with the association between ECG abnormalities and the risk of death from CVD.

This study's findings demonstrate a significant association between electrocardiogram (ECG) abnormalities (ST, T, Q, PR, QRS) and CVD mortality, particularly the Q and T wave irregularities. It can be concluded that ECG abnormalities in asymptomatic adults increase the risk of CVD mortality. With further research, ECG screening may be used alongside other conventional methods for early identification of high-risk patients.

Ethics approval and consent to participate

This study involves human participants and has been reviewed and approved by the Ethics Committee of Kurdistan University of Medical Sciences, with the ethics code IR.MUK.REC.1401.390. In the TLGS study, informed written consent was obtained from all participants.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Vice-Chancellor funded this study for Research and Technology of Kurdistan University of Medical Sciences, Sanandaj, Iran.

Authors' contributions

AA, DK and SM conceived and designed the study. SM, BN and AA analyzed and interpreted the data and drafted the manuscript. SM, AA, DK, FA, NH and BN were involved in the composition of the study tool, supervision of the research process, and critical revision and review of the manuscript. All the authors read and approved the final manuscript.

Acknowledgment

We are sincerely grateful to the specialists of cardiovascular diseases and electrocardiogram, Dr. Arash Ghanbarian, who collaborated with us in the interpretation of the ECG, and Dr. Ako Mahmoudian, who collaborated with us in the interpretation of the study data.

Clark H. NCDs: a challenge to sustainable human development. The Lancet. 2013;381(9866):510-1.
Alwan A. Global status report on noncommunicable diseases 2010: World Health Organization; 2011.
Nations U. Sustainable development knowledge platform. New Orleans: Nations U. 2016.
Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2018;392(10159):1736-88.
Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. Journal of the American College of Cardiology. 2020;76(25):2982-3021.
Zhao D. Epidemiological features of cardiovascular disease in Asia. JACC: Asia. 2021;1(1):1-13.
Petrakis D, Vassilopoulou L, Mamoulakis C, Psycharakis C, Anifantaki A, Sifakis S, et al. Endocrine disruptors leading to obesity and related diseases. International journal of environmental research and public health. 2017;14(10):1282.
Saleh M, Ambrose JA. Understanding myocardial infarction. F1000Research. 2018;7:1378.
Owolabi MO, Thrift AG, Mahal A, Ishida M, Martins S, Johnson WD, et al. Primary stroke prevention worldwide: translating evidence into action. The Lancet Public Health. 2022;7(1):e74-e85.
Talaei M, Sarrafzadegan N, Sadeghi M, Oveisgharan S, Marshall T, Thomas GN, et al. Incidence of cardiovascular diseases in an Iranian population: the Isfahan Cohort Study. Arch Iran Med. 2013;16(3):138-44.
Talaei M, Sarrafzadegan N, Sadeghi M, Oveisgharan S, Marshall T, Thomas GN, et al. Incidence of cardiovascular diseases in an Iranian population: the Isfahan Cohort Study. Archives of Iranian medicine. 2013;16(3):0-.
Sarrafzadegan N, Mohammmadifard N. Cardiovascular disease in Iran in the last 40 years: prevalence, mortality, morbidity, challenges and strategies for cardiovascular prevention. Archives of Iranian medicine. 2019;22(4):204-10.
Lowres N, Neubeck L, Redfern J, Freedman SB. Screening to identify unknown atrial fibrillation. Thrombosis and haemostasis. 2013;110(08):213-22.
Hammad M, Maher A, Wang K, Jiang F, Amrani M. Detection of abnormal heart conditions based on characteristics of ECG signals. Measurement. 2018;125:634-44.
De Bacquer D, De Backer G, Kornitzer M, Blackburn H. Prognostic value of ECG findings for total, cardiovascular disease, and coronary heart disease death in men and women. Heart. 1998;80(6):570-7.
Denes P, Larson JC, Lloyd-Jones DM, Prineas RJ, Greenland P. Major and minor ECG abnormalities in asymptomatic women and risk of cardiovascular events and mortality. Jama. 2007;297(9):978-85.
Machado DB, Crow RS, Boland LL, Hannan PJ, Taylor Jr HA, Folsom AR. Electrocardiographic findings and incident coronary heart disease among participants in the Atherosclerosis Risk in Communities (ARIC) study. The American journal of cardiology. 2006;97(8):1176-81. e3.
Cho MS, Nam G-B, Kim YN, Kim J, Choi K-J, Kim Y-H. Clinical Implications of Ventricular Repolarization Parameters on Long-Term Risk of Atrial Fibrillation―Longitudinal Follow-up Data From a General Ambulatory Korean Population―. Circulation Journal. 2020;84(7):1067-74.
Greenland P, Xie X, Liu K, Colangelo L, Liao Y, Daviglus ML, et al. Impact of minor electrocardiographic ST-segment and/or T-wave abnormalities on cardiovascular mortality during long-term follow-up. The American journal of cardiology. 2003;91(9):1068-74.
Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran Lipid and Glucose Study phase II. Trials. 2009;10:1-15.
Hall JE, Hall ME. Guyton and Hall textbook of medical physiology e-Book: Elsevier Health Sciences; 2020.
Hosseinpanah F, Barzin M, Sheikholeslami F, Azizi F. Effect of different obesity phenotypes on cardiovascular events in Tehran Lipid and Glucose Study (TLGS). The American journal of cardiology. 2011;107(3):412-6.
Hadaegh F, Harati H, Ghanbarian A, Azizi F. Association of total cholesterol versus other serum lipid parameters with the short-term prediction of cardiovascular outcomes: Tehran Lipid and Glucose Study. European Journal of Preventive Cardiology. 2006;13(4):571-7.
Wu C-Y, Chou Y-C, Huang N, Chou Y-J, Hu H-Y, Li C-P. Association of body mass index with all-cause and cardiovascular disease mortality in the elderly. PloS one. 2014;9(7):e102589.
Bots SH, Peters SA, Woodward M. Sex differences in coronary heart disease and stroke mortality: a global assessment of the effect of ageing between 1980 and 2010. BMJ global health. 2017;2(2):e000298.
Mikkola TS, Gissler M, Merikukka M, Tuomikoski P, Ylikorkala O. Sex differences in age-related cardiovascular mortality. PLoS One. 2013;8(5):e63347.
Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. bmj. 2016;354.
Lewington S. Prospective studies collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903-13.
Ho JE, Enserro D, Brouwers FP, Kizer JR, Shah SJ, Psaty BM, et al. Predicting heart failure with preserved and reduced ejection fraction: the international collaboration on heart failure subtypes. Circulation: Heart Failure. 2016;9(6):e003116.
Guilbert JJ. The world health report 2002 - reducing risks, promoting healthy life. Education for health (Abingdon, England). 2003;16(2):230.
Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension. 2020;75(2):285-92.
Bundy JD, Li C, Stuchlik P, Bu X, Kelly TN, Mills KT, et al. Systolic blood pressure reduction and risk of cardiovascular disease and mortality: a systematic review and network meta-analysis. JAMA cardiology. 2017;2(7):775-81.
Fekri N, Khaloo P, Ramezankhani A, Mansournia MA, Azizi F, Hadaegh F. Association of body mass index with life expectancy with and without cardiovascular disease. International Journal of Obesity. 2020;44(1):195-203.
Wilson PW, D'Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch Intern Med. 2002;162(16):1867-72.
Kenchaiah S, Evans JC, Levy D, Wilson PW, Benjamin EJ, Larson MG, et al. Obesity and the risk of heart failure. The New England journal of medicine. 2002;347(5):305-13.
Boloukat RR, Ramezankhani A, Hasheminia M, Tasdighi E, Azizi F, Hadaegh F. Impact of blood pressure, cholesterol and glucose in the association between adiposity measures and coronary heart disease and stroke among Iranian population. Clinical nutrition (Edinburgh, Scotland). 2018;37(6 Pt A):2060-7.
Allison DB, Fontaine KR, Manson JE, Stevens J, VanItallie TB. Annual deaths attributable to obesity in the United States. Jama. 1999;282(16):1530-8.
Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW, Jr. Body-mass index and mortality in a prospective cohort of U.S. adults. The New England journal of medicine. 1999;341(15):1097-105.
Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Jama. 2013;309(1):71-82.
Grabowski DC, Ellis JE. High body mass index does not predict mortality in older people: analysis of the Longitudinal Study of Aging. J Am Geriatr Soc. 2001;49(7):968-79.
Dong SY, Wang ML, Li ZB, Dong Z, Liu YQ, Lu RJ, et al. Obesity, weight change, and mortality in older adults with metabolic abnormalities. Nutrition, metabolism, and cardiovascular diseases : NMCD. 2018;28(7):749-55.
He X, Liu C, Chen Y, He J, Dong Y. Overweight Without Central Obesity, Cardiovascular Risk, and All-Cause Mortality. Mayo Clinic proceedings. 2018;93(6):709-20.
Sardarinia M, Akbarpour S, Lotfaliany M, Bagherzadeh-Khiabani F, Bozorgmanesh M, Sheikholeslami F, et al. Risk Factors for Incidence of Cardiovascular Diseases and All-Cause Mortality in a Middle Eastern Population over a Decade Follow-up: Tehran Lipid and Glucose Study. PLoS One. 2016;11(12):e0167623.
Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di Angelantonio E, et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375(9733):2215-22.
Mongraw-Chaffin M, LaCroix AZ, Sears DD, Garcia L, Phillips LS, Salmoirago-Blotcher E, et al. A prospective study of low fasting glucose with cardiovascular disease events and all-cause mortality: The Women's Health Initiative. Metabolism. 2017;70:116-24.
Mattiuzzi C, Sanchis-Gomar F, Lippi G. Worldwide burden of LDL cholesterol: Implications in cardiovascular disease. Nutrition, metabolism, and cardiovascular diseases : NMCD. 2020;30(2):241-4.
Navarese EP, Robinson JG, Kowalewski M, Kolodziejczak M, Andreotti F, Bliden K, et al. Association Between Baseline LDL-C Level and Total and Cardiovascular Mortality After LDL-C Lowering: A Systematic Review and Meta-analysis. Jama. 2018;319(15):1566-79.
Wadhera RK, Steen DL, Khan I, Giugliano RP, Foody JM. A review of low-density lipoprotein cholesterol, treatment strategies, and its impact on cardiovascular disease morbidity and mortality. Journal of clinical lipidology. 2016;10(3):472-89.
Kronmal RA, Cain KC, Ye Z, Omenn GS. Total serum cholesterol levels and mortality risk as a function of age: a report based on the Framingham data. Archives of internal medicine. 1993;153(9):1065-73.
Khan SU, Michos ED. Cardiovascular mortality after intensive LDL-Cholesterol lowering: Does baseline LDL-Cholesterol really matter? American Journal of Preventive Cardiology. 2020;1:100013.
Medina-Inojosa JR, Batsis JA, Supervia M, Somers VK, Thomas RJ, Jenkins S, et al. Relation of Waist-Hip Ratio to Long-Term Cardiovascular Events in Patients With Coronary Artery Disease. Am J Cardiol. 2018;121(8):903-9.
Welborn TA, Dhaliwal SS, Bennett SA. Waist-hip ratio is the dominant risk factor predicting cardiovascular death in Australia. The Medical journal of Australia. 2003;179(11-12):580-5.
Czernichow S, Kengne AP, Stamatakis E, Hamer M, Batty GD. Body mass index, waist circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk?: evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2011;12(9):680-7.
Dalton M, Cameron AJ, Zimmet PZ, Shaw JE, Jolley D, Dunstan DW, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med. 2003;254(6):555-63.
de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J. 2007;28(7):850-6.
Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk--a review of the literature. Eur J Clin Nutr. 2010;64(1):16-22.
Megnien JL, Denarie N, Cocaul M, Simon A, Levenson J. Predictive value of waist-to-hip ratio on cardiovascular risk events. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity. 1999;23(1):90-7.
Myint PK, Kwok CS, Luben RN, Wareham NJ, Khaw KT. Body fat percentage, body mass index and waist-to-hip ratio as predictors of mortality and cardiovascular disease. Heart. 2014;100(20):1613-9.
Wong CW, Kwok CS, Narain A, Gulati M, Mihalidou AS, Wu P, et al. Marital status and risk of cardiovascular diseases: a systematic review and meta-analysis. Heart. 2018;104(23):1937-48.
Floud S, Balkwill A, Canoy D, Wright FL, Reeves GK, Green J, et al. Marital status and ischemic heart disease incidence and mortality in women: a large prospective study. BMC Med. 2014;12:42.
Manfredini R, De Giorgi A, Tiseo R, Boari B, Cappadona R, Salmi R, et al. Marital Status, Cardiovascular Diseases, and Cardiovascular Risk Factors: A Review of the Evidence. Journal of women's health (2002). 2017;26(6):624-32.
Molloy GJ, Stamatakis E, Randall G, Hamer M. Marital status, gender and cardiovascular mortality: behavioural, psychological distress and metabolic explanations. Social science & medicine (1982). 2009;69(2):223-8.
Malyutina S, Bobak M, Simonova G, Gafarov V, Nikitin Y, Marmot M. Education, marital status, and total and cardiovascular mortality in Novosibirsk, Russia: a prospective cohort study. Ann Epidemiol. 2004;14(4):244-9.
Auer R, Bauer DC, Marques-Vidal P, Butler J, Min LJ, Cornuz J, et al. Association of major and minor ECG abnormalities with coronary heart disease events. Jama. 2012;307(14):1497-505.
Goldman A, Hod H, Chetrit A, Dankner R. Incidental abnormal ECG findings and long-term cardiovascular morbidity and all-cause mortality: A population based prospective study. International journal of cardiology. 2019;295:36-41.
Tamosiunas A, Petkeviciene J, Radisauskas R, Bernotiene G, Luksiene D, Kavaliauskas M, et al. Trends in electrocardiographic abnormalities and risk of cardiovascular mortality in Lithuania, 1986-2015. BMC Cardiovasc Disord. 2019;19(1):30.
Jonas DE RS, Middleton JC, Barclay C, Green J, Baker C, Asher GN. Screening for cardiovascular disease risk with resting or exercise electrocardiography: evidence report and systematic review for the US Preventive Services Task Force. . Jama. Jun 12 .2018 ;319(22):2315-28.
Niu J, Deng C, Zheng R, Xu M, Lu J, Wang T, et al. The Association and Predictive Ability of ECG Abnormalities with Cardiovascular Diseases: A Prospective Analysis. Glob Heart. 2020;15(1):59.
Niu J, Deng C, Zheng R, Xu M, Lu J, Wang T, et al. The association and predictive ability of ECG abnormalities with cardiovascular diseases: a prospective analysis. Global Heart. 2020;15(1).
Kim HK, Kim CH, Ko KH, Park SW, Park JY, Lee KU. Variable association between components of the metabolic syndrome and electrocardiographic abnormalities in Korean adults. Korean J Intern Med. 2010;25(2):174-80.
Nabipour I, Amiri M, Imami SR, Jahfari SM, Shafeiae E, Nosrati A, et al. The metabolic syndrome and nonfatal ischemic heart disease; a population-based study. International journal of cardiology. 2007;118(1):48-53.
Ebong IA, Bertoni AG, Soliman EZ, Guo M, Sibley CT, Chen YD, et al. Electrocardiographic abnormalities associated with the metabolic syndrome and its components: the multi-ethnic study of atherosclerosis. Metabolic syndrome and related disorders. 2012;10(2):92-7.
Lin C, Chau T, Lin CS, Shang HS, Fang WH, Lee DJ, et al. Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction. NPJ Digit Med. 2022;5(1):8.
Lima EM, Ribeiro AH, Paixão GMM, Ribeiro MH, Pinto-Filho MM, Gomes PR, et al. Deep neural network-estimated electrocardiographic age as a mortality predictor. Nat Commun. 2021;12(1):5117.
Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, et al. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020;9(7):e014717.
Baek YS, Lee SC, Choi W, Kim DH. A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm. Sci Rep. 2021;11(1):12818.
Lindow T, Palencia-Lamela I, Schlegel TT, Ugander M. Heart age estimated using explainable advanced electrocardiography. Sci Rep. 2022;12(1):9840.
Chang CH, Lin CS, Luo YS, Lee YT, Lin C. Electrocardiogram-Based Heart Age Estimation by a Deep Learning Model Provides More Information on the Incidence of Cardiovascular Disorders. Front Cardiovasc Med. 2022;9:754909.
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70-4.
Zhan X, Zeng C, He J, Wang M, Xiao J. Non-specific electrocardiographic ST-T abnormalities predict mortality in patients on peritoneal dialysis. Front Cardiovasc Med. 2022;9:930517.
Daviglus ML, Liao Y, Greenland P, Dyer AR, Liu K, Xie X, et al. Association of nonspecific minor ST-T abnormalities with cardiovascular mortality: the Chicago Western Electric Study. Jama. 1999;281(6):530-6.
De Bacquer D, De Backer G, Kornitzer M, Blackburn H. Prognostic value of ECG findings for total, cardiovascular disease, and coronary heart disease death in men and women. Heart. 1998;80(6):570.
Oliver J, Garcia P, Gonzalez A, Sanchez-Aquino R, Caravallo E, Aroca A, editors. Isolated partial anomalous pulmonary venous connection in adults. A diagnostic challenge. EUROPEAN HEART JOURNAL; 2004: WB SAUNDERS CO LTD 32 JAMESTOWN RD, LONDON NW1 7BY, ENGLAND.

Tables 1 to 2 are available in the Supplementary Files section

No competing interests reported.

Tables.docx

Download PDF

Editorial decision: Revision requested
15 Nov, 2024
Reviews received at journal
14 Nov, 2024
Reviewers agreed at journal
07 Oct, 2024
Reviewers agreed at journal
07 Aug, 2024
Reviewers agreed at journal
22 Jun, 2024
Reviews received at journal
06 Apr, 2024
Reviewers agreed at journal
02 Apr, 2024
Reviewers agreed at journal
01 Apr, 2024
Reviewers invited by journal
31 Mar, 2024
Editor invited by journal
14 Mar, 2024
Editor assigned by journal
12 Mar, 2024
Submission checks completed at journal
12 Mar, 2024
First submitted to journal
28 Feb, 2024

You are reading this latest preprint version

Electrocardiogram Abnormalities and CVD Mortality: Prospective Cohort Study of Tehran Lipid and Glucose Study

Status:

Version 1

Abstract

Introduction

Materials and Methods

Results

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1