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The Association of the Metabolic Syndrome with T-wave Axis Deviation in NHANES III
The Association of the Metabolic Syndrome with T-wave Axis Deviation in NHANES III MOHAMMED F. FARAMAWI, MD, PHD, MSC, MPH, MACODU SALL, MPH, AND MOHAMMED Y. ABDUL KAREEM, MD, MSC
PURPOSE: We sought to study the association between metabolic syndrome (MetS) and abnormal T-wave axis deviation. METHODS: A representative sample of the adult U.S. population, 3810 individuals 40 years of age or older, was categorized as having metabolic syndrome and not having the syndrome as defined by Adult Treatment Panel III. T-wave axis deviation was measured from the standard 12-lead electrocardiogram. RESULTS: The odds of having abnormal T-wave axis deviation was calculated for those with metabolic syndrome versus those without after multivariable adjustment for age, race, daily alcohol consumption, body mass index categories, left ventricular hypertrophy, and heart rate. In multivariable weighted regression analysis, the odds were 2.03 times greater in those persons with MetS compared to those without (odds ratio, 1.79; 95% confidence interval, 1.04–3.11). The population-attributable risk percentage of abnormal T-wave axis deviation associated with MetS was 23.94%. Additionally, a graded relationship was observed between the number of MetS components and the odds ratio of abnormal T-wave axis (p trend ! 0.01). CONCLUSION: These data indicate that MetS is independently associated with an abnormal T-wave axis shift. This study calls for careful electrocardiographic monitoring among persons with MetS for early detection of abnormal T-wave axis in clinical practice to prevent severe and often fatal arrhythmias. Ann Epidemiol 2008;18:702–707. Ó 2008 Elsevier Inc. All rights reserved. KEY WORDS:
Metabolic Syndrome, Electrocardiography, Cross-sectional Studies.
INTRODUCTION Metabolic syndrome (MetS) is becoming a pandemic in Western countries (1). The prevalence of MetS in the United States is higher than 20% in men and women over the age of 20 years and higher than 40% in men and women over the age of 60 years (1). Epidemiological studies have shown that MetS is associated with cardiovascular diseases, such as coronary heart disease (CHD) (2–4). However, the association of this syndrome with electrocardiographic (ECG) subclinical risk factors such as T-wave axis deviation is poorly elucidated. Studies have reported a relationship between T-wave axis deviation and an increased risk of sudden death due to fatal ventricular arrhythmias (5) and CHD mortality in a broad range of clinical populations (6, 7) as well as in healthy subjects in population based studies (8, 9). As little is known about the association of MetS with frontal T-wave axis orientation in population-based studies,
From theDepartment of Preventive Medicine (M.F.F.) and the Department of Internal Medicine (M.Y.A.K.), Menufiya University, Egypt; and the Department of Epidemiology, Tulane School of Public Health & Tropical Medicine, New Orleans, LA (M.S.). Address correspondence to: Mohammed F. Faramawi, MD, PhD, Department of Preventive Medicine, Menufiya University, Shebeen El kum, Menufiya 11341, Egypt. Tel.: 202 2415 4532; fax: 202 2415 4531. E-mail: [email protected]. Received February 19, 2008; accepted June 3, 2008. Ó 2008 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010
this analysis aimed to examine the association between MetS with abnormal T-wave axis in a nationally representative sample of the adult U.S. population.
METHODS Study Population Between 1988 and 1994, the National Center for Health Statistics conducted the third National Health and Nutrition Examination Survey (NHANES III). This crosssectional study consisted of a multistage, stratified, clustered probability sample of the civilian noninstitutionalized U.S population. Because NHANES III is based on a complex multistage probability sample design, appropriate probability sampling weights were assigned to produce correct population estimates. The sampling weights incorporate the differential probabilities of selection and adjust for noncoverage, non-response and oversampling. With the large oversampling of older persons, black persons, and Mexican Americans in NHANES III, it was essential that the sampling weights be used in all analyses to produce an unbiased national estimate. Otherwise misinterpretation of results would likely occur. NHANES III consisted of a standardized questionnaire administered in the home by a trained interviewer followed by a detailed physical examination at a mobile examination 1047-2797/08/$–see front matter doi:10.1016/j.annepidem.2008.06.002
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Selected Abbreviations and Acronyms MetS Z metabolic syndrome CHD Z coronary heart disease ECG Z electrocardiographic NHANES III Z Third National Health and Nutrition Examination Survey MEC Z mobile examination center BMI Z body mass index HDL Z high-density lipoprotein LDL Z low-density lipoprotein LVH Z left ventricular hypertrophy
center (MEC). Data collected at the home interview relevant to the current analysis included self-report demographics (age, race, and gender). Questions about cardiac disease (e.g., heart failure), coronary artery disease (e.g., myocardial infarction and angina pectoris) were asked in the same questionnaire. Up to six blood pressure measurements were taken on two occasions according to a standard protocol in the MEC. The first set of three blood pressures was measured in the home by a trained and certified interviewer and a physician obtained the second set during the medical examination. The averages of all available blood pressure measurements were used for data analysis. The body weights (measured in kilograms) and heights (in meters) were measured for each participant. The body mass index (BMI) was calculated as weight in kilograms divided by height in square meters and was rounded to the nearest tenth. Normal body weight was defined as BMI >18.5 and BMI <24.9, overweight as a BMI of 25.0–29.9 and obesity as a BMI of >30.0 (10). During the physical examination at the MEC, a 24-hour dietary recall was administered, which assessed the amount of alcohol consumed during the previous day. The data collected were used to calculate the daily intake of alcohol (in grams). The daily alcohol consumption was categorized into >1 g/d or !1 g/d. Measurements of the MetS Components During the visit to the MEC, a fasting venous blood specimen was drawn from each participant according to a standardized protocol (11). Plasma glucose was measured at the University of Missouri Diabetes Diagnostic Laboratory using a hexokinase enzymatic method (12). Fasting serum insulin, total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides were measured at other centralized laboratories (11, 13). The Adult Treatment Panel III guidelines were used to define the MetS as the presence of three or more of the following (14): Systolic blood pressure >130 mm Hg and/or diastolic blood pressure >85 mm Hg
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HDL !40 mg/dL for men and !50 mg/dL for women Triglycerides >150 mg/dL Fasting blood glucose >100 mg/dL Waist circumference O102 cm for men and O88 cm for women Cotinine concentration in the blood, a metabolite of nicotine, was used as a biomarker to classify participants into groups of current and non-current smokers. Current smokers were defined as those who had cotinine levels O15 ng/mL, whereas those with serum cotinine <15 ng/mL were classified as non-current smokers (15, 16). Measurement of the Frontal T-wave Axis Orientation A standard 12-lead resting electrocardiogram was performed on all men and women aged 40 years or over using a Marquette MAC 12 (Marquette Medical Systems, Inc., Milwaukee, WI. ECG data were recorded with eight independent components of the 12 standard leads simultaneously and were sampled at 250 samples per second per channel. The electrocardiograms were initially processed in a central laboratory at the EPICORE Center (University of Alberta, Edmonton, Alberta, Canada) and during later phases of the study at the EPICARE Center (Wake Forest University, Winston-Salem, NC). All electrocardiographic processing was done by the Dalhousie ECG Program. An ECG program which relies on the use of selective averaging was used to derive a representative P-QRS-T complex for analysis of wave durations and to detect left ventricular hypertrophy (LVH) (17). The mean QRS duration of each individual was calculated by this program. This program has greatly improved the precision and accuracy of ECG interval measurements. Values of T-wave integrals (i.e., net T-wave areas determined from the six limb leads) were used for the mean frontal plane T-wave axis calculation. Three separate frontal T-wave axis angle values were also calculated. The angle values were calculated from three pairs of limb leads (I, AVF), (II, AVL), and (III, AVR); the final mean frontal plane axis was taken as the mean value of these three separate angle determinations. Based on the frontal T-wave axis, patients were categorized as having normal T-wave axis (>15 to <75 ), borderline (O75 to <105 or !15 to > 15 ), and abnormal (! 15 to > 180 or O105 to <180 ), according to Kors et al. (8) Statistical Analysis Of 8,561 subjects who underwent ECG testing, 110 were excluded because of missing data about frontal T-wave axis, heart rate, left ventricular enlargement or QRS duration. Other participants were excluded because they did not fast for 8 hours or more (n Z 3,536), they had missing information about the key variables of MetS (n Z 435) or daily
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alcohol consumption (n Z 138). Additionally, individuals who had electrocardiograms showing atrial or ventricular conduction abnormalities (n Z 141) or history of heart disease (n Z 391) were excluded. This left 3,810 participants for the current analyses. All analyses were performed by using Stata version 9, which took into account the complex sampling techniques used in NHANES III. Observations were weighted using weights calculated for that purpose by the National Center for Health Statistics to reflect the general U.S. population. These weights were also designed to adjust for biases attributable to non-response and over-sampling. Basic descriptive statistics, including calculation of means and their standard deviations for the continuous variables, numbers and percentages for categorical variables were performed to characterize the study patients. Comparisons between groups were made using the Student t test for continuous variables and the chi-square test for categorical variables. Univariate and multivariate weighted logistic regression analyses were conducted to examine the association of the MetS (independent variable) with frontal T-wave axis orientation (dependent variable). The strength of the association was reported as odds ratio (OR) with the 95% confidence interval. Additionally, multivariate models, testing for the interaction terms (effect modifier) between the MetS and age, MetS and gender, MetS and race separately, were performed. Trends across the number of MetS components were determined using the Cochran-Armitage test for trend. To determine the percentage of total risk of an abnormal frontal T-wave axis attributable to MetS in the study population, population-attributable risk was calculated as (Pe [OR 1]/[Pe [OR – 1] þ 1), where Pe is the prevalence of MetS and OR is the fully adjusted odds ratio. Finally, a multivariable weighted logistic regression was conducted to determine which MetS components which can predict frontal T wave axis deviation. The model included increased waist circumference, high triglyceride levels, high systolic blood pressure, high diastolic blood pressure, low HDL and impaired fasting blood glucose level.
RESULTS The overall prevalence of the MetS was 39.84%, while the overall prevalences of borderline and abnormal frontal Twave axis deviation were 14.82% and 2.97%, respectively. Participants with MetS were more likely to be female, obese, and older than those without MetS (Table 1). The daily alcohol consumption of individuals with MetS was less than that of MetS-free participants (see Table 1). In regard to the MetS components, the mean values of systolic blood pressure, diastolic blood pressure, fasting blood glucose level, waist circumference, and triglyceride levels were higher in
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TABLE 1. Personal characteristics, personal habits, ECG findings, and MetS components by MetS status Variables*
MetS n Z 2,104
Age, yr 53.92 (12.12) Male 1,080 (56.06) Race White 1,512 (86.82) Black 526 (08.95) Other 66 (04.23) Smokers 652 (29.90) Alcohol consumption >1 g/d 467 (25.40) BMI categoriesy Normal weight 1,007 (51.97) Overweight 780 (34.79) Obese 317 (13.24) QRS complex duration, ms 97.25 (12.29) Heart rate, beats/min 65.43 (10.30) LVH 148 (5.62) T-wave axis deviation Normal 1,719 (84.35) Borderline 333 (13.88) Abnormal 52 (1.77) Fasting blood glucose, mg/dL 93.14 (14.99) Systolic blood pressure, mm Hg 120.49 (14.97) Diastolic blood pressure, mm Hg 73.16 (8.10) HDL, mg/dL 55.32 (14.39) Triglycerides, mg/dL 112.13 (61.90) Waist circumference, cm 89.95 (11.70)
þ MetS n Z 1,706
p Value
58.75 (12.18) !0.01 900 (50.00) 0.01 1,305 (89.46) 0.01 359 (08.17) 42 (02.37) 445 (27.41) 0.40 262 (16.4) !0.01 232 (37.06) !0.01 705 (36.57) 769 (26.37) 99.62 (14.00) 70.10 (11.64) !0.01 225 (10.94) !0.01 1,292 (78.84) 305 (16.23) 109 (4.93) 115.80 (40.12) 137.23 (17.35) 80.28 (10.20) 43.81 (13.99) 207.15 (69.11) 103.47 (12.25)
!0.01 !0.01 !0.01 !0.01 !0.01 !0.01 !0.01 !0.01 !0.01
Z absent; þ Z present; ECG Z electrocardiographic; MetS Z metabolic syndrome; BMI Z body mass index; LVH Z left ventricular hypertrophy; HDL Z high-density lipoprotein. *Variable values expressed as mean (standard deviation) or number (percentage), as applicable. y BMI categories: normal (between >18.5 and <24.9); overweight (>25.0 and <29.9); obese (>30.0).
the MetS group, whereas the mean value of HDL was lower in the same group. Additionally, the mean QRS interval was longer and LVH was more prevalent among participants who had the syndrome, as shown in Table 1. The obtained results from the multivariate logistic regression showed that gender, smoking, and QRS duration were not statistically significant predictors for frontal Twave axis deviation. Another logistic model including the significant predictors (i.e., age, race, daily alcohol consumption, heart rate, LVH, and BMI categories) was conducted. The OR of MetS after excluding smoking, gender, and QRS duration did not differ substantially from that of MetS before excluding the previous variables. Therefore the final model included the significant predictors only. Participants who had MetS had 2.90 times greater odds of an abnormal frontal T-wave axis deviation than those without the syndrome (Table 2). The OR remained significant and became 1.79 after adjustment for age, race, alcohol drinking, heart rate, BMI categories, and LVH (see Table 2). Additionally, the interaction terms in the weighted logistic models were not significant (all p values, O0.05).
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TABLE 2. Relationship of MetS and MetS components number with frontal T-wave axis orientation Unadjusted, n Z 3,810
Adjusted,* n Z 3,810
OR (95% CI)
OR (95% CI)
T-wave axis deviation relative to normal axis (n Z 3,011) Borderline (n Z 638) 1.27 (1.05–1.64) 1.22 (1.00–1.70) Abnormal (n Z 161) 2.90 (1.79–4.69) 1.79 (1.04–3.17) Number of MetS components relative to no MetS components (n Z 456) 1 (n Z 769) 1.95 (0.90–7.38) 1.71 (0.30–7.28) 2 (n Z 879) 3.35 (1.00–1.13) 1.97 (0.41–9.90) 3 (n Z 758) 4.46 (1.42–1.36) 2.55 (0.62–11.29) 4 (n Z 948) 7.83 (2.50–0.03) 3.62 (0.70–14.20) p Trend !0.01 p Trend !0.01 MetS Z metabolic syndrome; OR Z odds ratio; CI Z confidence interval; p Trend Z p value for testing trend. *Adjusted for age, race, body mass index categories, heart rate, daily alcohol consumption, and left ventricular hypertrophy.
There was a dose-response relationship between the number of the MetS components and frontal T-wave axis deviation (see Table 2). Participants who had one, two, three, and four components or greater were 1.71, 1.95, 2.55, and 3.62 times. respectively, as likely as those who did not have any component to have abnormal T-wave axis deviation after covariate adjustment. The populationattributable risk of frontal T-wave axis shift associated with MetS was 23.94%. The weighted logistic regression revealed that high systolic blood pressure was the only significant predictor for frontal T-wave axis deviation (Table 3).
DISCUSSION In this population-based study, a strong, positive, and significant relationship between MetS and abnormal frontal Twave axis deviation was found. Participants with MetS had 1.79 times greater odds of having abnormal frontal Twave axis than those who did not have the syndrome. Additionally, 23.94%. of abnormal frontal T-wave axis cases in the NHANES III population may be attributable to MetS. The risk for abnormal frontal T-wave axis increased
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progressively with a higher number of MetS components. These relationships were independent of age, race, BMI categories, and other potential risk factors. The abnormal frontal T-wave axis deviation is an indicator for impaired ventricular repolarization (8). Therefore these data indicate that MetS is associated with ventricular repolarization impairment, which means that individuals with MetS are at a higher risk for ventricular arrhythmias. Frontal T-wave axis deviation is a simple ECG measurement (8, 18). Whether calculated automatically or manually, measurement of the frontal T-wave axis is likely to be less susceptible to noise and problems of the existing axis and amplitude measurements in the electrocardiogram (8, 18). Therefore, frontal T-wave axis was utilized as an indicator for ventricular repolarization disturbance in the present analysis. More than 50% of the participants who underwent ECG examination were ineligible to be included in the current analysis; therefore they were excluded, either because they had heart disease or they did not fast for 8 hours or more. Persons with cardiac disease were not eligible to be included in the analysis because cardiac disease could be associated with abnormal T-wave axis deviation. Fasting is important to detect high triglyceride levels and impaired fasting blood glucose concentration accurately so that individuals who have one or two of these MetS components could be captured without misclassification. Hence, participants who did not fast for 8 hours or more were excluded. The excluded participants had a higher prevalence of heart disease as well as higher mean systolic arterial blood pressure (p ! 0.05). Including such participants would overestimate the magnitude of the association between MetS and frontal T-wave axis deviation because the excluded participants were sicker than those who fulfilled the selection criteria. This study has several strengths. To our knowledge, it is the first population-based study to report a relationship between MetS, defined by the ATP III guidelines, and the risk of having abnormal T-wave axis deviation. In addition, careful measures of the study-independent MetS and dependent variables (frontal T-wave axis deviation) allowed precise estimation of the association.
TABLE 3. Relationship of the different MetS components with frontal T-wave axis deviation
MetS components Elevated SBP (n Z 1,816) relative to normal SBP (n Z 1,994) Elevated DBP (n Z 740) relative to normal DBP High triglyceride concentration (n Z 1,375) relative to normal concentration (n Z 2,435) Increased waist circumference (n Z 1,990) relative to normal waist circumference (n Z 1,820) High blood glucose level (n Z 1,673) relative to normal level (n Z 2,137) Low HDL level (n Z 1,375) relative to normal level (n Z 2,435)
Unadjusted, n Z 3,810
Adjusted,* n Z 3,810
OR (95% CI)
OR (95% CI)
3.91 (2.36–6.45) 1.68 (1.00–2.85) 1.93 (1.23–3.22) 1.88 (1.22–3.02) 1.83 (1.20–2.87) 1.46 (0.93–2.28)
3.46 (2.00–5.99) 0.95 (0.52–1.69) 1.52 (0.90–2.10) 1.38 (0.84–2.27) 1.30 (0.84–2.04) 1.14 (0.70–1.50)
MetS Z metabolic syndrome; OR Z odds ratio; CI Z confidence interval; SBP Z systolic blood pressure; DBP Z diastolic blood pressure); HDL Z high-density lipoprotein. *Multivariate logistic regression included increased waist circumference (O102 cm for men, O88 cm for women), high triglyceride levels (>150 mg/dL), increased SBP (>130 mm Hg), increased DBP (>85 mm Hg), low HDL (!40 mg/dL for men, !50 mg/dL for women), high blood glucose level (impaired fasting blood glucose if values are >100/mg/dL.
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The conclusions of the study by Soydinc et al. (19) among a small Turkish population were in line with the conclusion of the present study. Nevertheless, some aspects of the current study exist to make it unique. The results of our study are among a larger, representative sample of the U.S. adult population, including data from different racial and ethnic backgrounds. In addition, unlike our study which examined the association of the MetS with T-wave axis deviation as a subclinical risk factor for ventricular arrhythmias, the study of Soydinc et al. explored the association of MetS with a prolonged QT interval. Although a prolonged QTc interval duration is an important predictor of mortality risk, it has some limitations (20). Measurement of QTc duration is tedious and time consuming because it requires analysis of all ECG leads (18). In contrast, frontal plane T-wave axis deviation can be easily estimated from standard electrocardiograms, is less prone to problems of definition than most of the existing ECG interval measurements, and is a more sensitive indicator of altered ventricular repolarization than QT interval prolongation (8). T-wave axis deviation is a more sensitive marker than prolonged QT interval because QT interval prolongation can result from action potential duration prolongation (or delayed activation) in the last repolarizing ventricular region, whereas frontal T-wave axis deviation is influenced by action potential duration alterations, shortening or prolongation, in any myocardial region (9). The present analysis showed that high systolic blood pressure was the only significant predictor for frontal Twave axis deviation. The previous MetS components were not statistically related to frontal T-wave axis deviation. Nevertheless, the magnitudes of the association of these components were not small, and they were above 1. Although the previous components were not significant, it is noteworthy to mention that the presence of these components with the high systolic blood pressure could play a role in predicting the frontal T-wave axis deviation. This perspective is supported by the significantly observed dose-response relationship between the number of the MetS components and the frontal T-wave axis deviation. The previous components may lead to frontal T-wave axis deviation through their association with cardiac disease and hypertension. Excluding participants who suffered from heart disease and controlling for hypertension in the regression analysis may explain why impaired fasting blood glucose levels and the other MetS components were not found to be independent risk factors for frontal T-wave axis deviation in the current study. There is a relationship between high systolic blood pressure and impaired endothelial function (21–23). High systolic blood pressure leads to endothelial dysfunction by affecting nitric oxide availability, which leads to coronary angiopathy (24). The other MetS components (namely, diastolic blood pressure,
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impaired blood glucose tolerance, increased waist circumference, and dyslipidemia [low HDL and high triglyceride levels]) are known to be risk factors for endothelial dysfunction (25). Endothelial dysfunction will eventually lead to abnormal angiogenesis and decrease of blood flow to the different body organs, such as the heart. Therefore we hypothesize that heart disease–free participants with MetS suffer from undiagnosed or subclinical myocardial disease attributable to endothelial dysfunction and impairment of blood flow to myocardial muscle. Myocardial disease may lead to ventricular repolarization abnormalities represented by frontal T-wave axis deviation. Frontal T-wave axis deviation in individuals indicates a higher risk for development of adverse cardiovascular outcomes, such as ‘‘torsade de pointes’’ and cardiac arrhythmias. The findings of this study have important public health implications because MetS is becoming a pandemic in Western societies as a result of changing worldwide patterns of physical activity and food intake (1). In the United States, the overall prevalence of MetS is higher than 20% in men and women over the age of 20 years and higher than 40% in men and women over 60 years of age. Between 1988 and 1994 and between 1999 and 2000, a significant increase in the prevalence of MetS occurred among U.S. adults aged 20 years or older. Epidemiological studies have shown that T-wave axis abnormality has been associated with an increased risk of sudden death due to fatal ventricular arrhythmia (5), CHD, and mortality in a broad range of clinical populations (6, 7) as well as in healthy subjects (9). The data reported from this study also have clinical implications. The pro and con arguments on identifying the MetS as a disease are directed toward its utility in clinical practice (26–29). Some researchers believe that identifying the aggregation of hypertension, hyperglycemia, hyperlipidemia, and obesity as MetS and tailoring a treatment specifically for this syndrome have no advantage over targeting treatment toward the previous cardiovascular risk factors individually in the traditional manner (30). On the other hand, some researchers believe that clinical benefits of identifying the previous cardiovascular risk factors cluster as a syndrome do exist. On the basis of the present study results, diagnosing the syndrome with the knowledge of its strong relationship with a subclinical factor associated with fatal cardiac arrhythmias (T-wave axis deviation) will stimulate more aggressive longterm follow-up with ongoing lifestyle interventions and the use of risk-reducing drugs (26). Also, clinical experience demonstrates that when persons know that they have this syndrome, they are motivated to work on modifying the MetS components to reduce the risk of heart disease (26). The results of this study should be interpreted with caution. The cross-sectional nature of these analyses does not allow for inference of causality or for establishment of temporality between MetS and T-wave axis abnormalities. The
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history of cardiac disease was acquired from patients’ responses, which were not validated by medical records. Although self-reporting is a valid tool to collect accurate data about chronic diseases such as heart disease (29, 30), self-reporting could introduce misclassification bias. Had misclassification occurred, it would probably be non-differential with respect to MetS and thus would reduce the strength of association between these factors and T-wave axis deviation (18). Also, although adjustment for potential key confounders was performed, as in many observational studies, the presence of unknown or residual confounders which were not adjusted for is possible. Longitudinal studies are required to confirm the findings in this study. More epidemiological studies are needed to determine whether successful treatment of blood pressure, lipid profile components and waist circumference, and impaired fasting blood glucose levels among persons with MetS reduce frontal Twave axis deviation, ultimately reducing the risk of ventricular arrhythmia and sudden death in such persons. In summary, this study demonstrates that MetS could be an independent risk factor for having an abnormal frontal Twave axis in the general population. There is a graded relationship between the number of MetS components and risk for having T-wave axis deviation. Careful ECG monitoring in clinical practice as well as screening programs of persons with MetS for early detection of frontal T-wave axis deviation, an easily detected subclinical marker for the risk of incident CHD events, is warranted to identify individuals predisposed to develop adverse cardiovascular outcomes.
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PURPOSE: We sought to study the association between metabolic syndrome (MetS) and abnormal T-wave axis deviation. METHODS: A representative sample of the adult U.S. population, 3810 individuals 40 years of age or older, was categorized as having metabolic syndrome and not having the syndrome as defined by Adult Treatment Panel III. T-wave axis deviation was measured from the standard 12-lead electrocardiogram. RESULTS: The odds of having abnormal T-wave axis deviation was calculated for those with metabolic syndrome versus those without after multivariable adjustment for age, race, daily alcohol consumption, body mass index categories, left ventricular hypertrophy, and heart rate. In multivariable weighted regression analysis, the odds were 2.03 times greater in those persons with MetS compared to those without (odds ratio, 1.79; 95% confidence interval, 1.04–3.11). The population-attributable risk percentage of abnormal T-wave axis deviation associated with MetS was 23.94%. Additionally, a graded relationship was observed between the number of MetS components and the odds ratio of abnormal T-wave axis (p trend ! 0.01). CONCLUSION: These data indicate that MetS is independently associated with an abnormal T-wave axis shift. This study calls for careful electrocardiographic monitoring among persons with MetS for early detection of abnormal T-wave axis in clinical practice to prevent severe and often fatal arrhythmias. Ann Epidemiol 2008;18:702–707. Ó 2008 Elsevier Inc. All rights reserved. KEY WORDS:
Metabolic Syndrome, Electrocardiography, Cross-sectional Studies.
INTRODUCTION Metabolic syndrome (MetS) is becoming a pandemic in Western countries (1). The prevalence of MetS in the United States is higher than 20% in men and women over the age of 20 years and higher than 40% in men and women over the age of 60 years (1). Epidemiological studies have shown that MetS is associated with cardiovascular diseases, such as coronary heart disease (CHD) (2–4). However, the association of this syndrome with electrocardiographic (ECG) subclinical risk factors such as T-wave axis deviation is poorly elucidated. Studies have reported a relationship between T-wave axis deviation and an increased risk of sudden death due to fatal ventricular arrhythmias (5) and CHD mortality in a broad range of clinical populations (6, 7) as well as in healthy subjects in population based studies (8, 9). As little is known about the association of MetS with frontal T-wave axis orientation in population-based studies,
From theDepartment of Preventive Medicine (M.F.F.) and the Department of Internal Medicine (M.Y.A.K.), Menufiya University, Egypt; and the Department of Epidemiology, Tulane School of Public Health & Tropical Medicine, New Orleans, LA (M.S.). Address correspondence to: Mohammed F. Faramawi, MD, PhD, Department of Preventive Medicine, Menufiya University, Shebeen El kum, Menufiya 11341, Egypt. Tel.: 202 2415 4532; fax: 202 2415 4531. E-mail: [email protected]. Received February 19, 2008; accepted June 3, 2008. Ó 2008 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010
this analysis aimed to examine the association between MetS with abnormal T-wave axis in a nationally representative sample of the adult U.S. population.
METHODS Study Population Between 1988 and 1994, the National Center for Health Statistics conducted the third National Health and Nutrition Examination Survey (NHANES III). This crosssectional study consisted of a multistage, stratified, clustered probability sample of the civilian noninstitutionalized U.S population. Because NHANES III is based on a complex multistage probability sample design, appropriate probability sampling weights were assigned to produce correct population estimates. The sampling weights incorporate the differential probabilities of selection and adjust for noncoverage, non-response and oversampling. With the large oversampling of older persons, black persons, and Mexican Americans in NHANES III, it was essential that the sampling weights be used in all analyses to produce an unbiased national estimate. Otherwise misinterpretation of results would likely occur. NHANES III consisted of a standardized questionnaire administered in the home by a trained interviewer followed by a detailed physical examination at a mobile examination 1047-2797/08/$–see front matter doi:10.1016/j.annepidem.2008.06.002
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Selected Abbreviations and Acronyms MetS Z metabolic syndrome CHD Z coronary heart disease ECG Z electrocardiographic NHANES III Z Third National Health and Nutrition Examination Survey MEC Z mobile examination center BMI Z body mass index HDL Z high-density lipoprotein LDL Z low-density lipoprotein LVH Z left ventricular hypertrophy
center (MEC). Data collected at the home interview relevant to the current analysis included self-report demographics (age, race, and gender). Questions about cardiac disease (e.g., heart failure), coronary artery disease (e.g., myocardial infarction and angina pectoris) were asked in the same questionnaire. Up to six blood pressure measurements were taken on two occasions according to a standard protocol in the MEC. The first set of three blood pressures was measured in the home by a trained and certified interviewer and a physician obtained the second set during the medical examination. The averages of all available blood pressure measurements were used for data analysis. The body weights (measured in kilograms) and heights (in meters) were measured for each participant. The body mass index (BMI) was calculated as weight in kilograms divided by height in square meters and was rounded to the nearest tenth. Normal body weight was defined as BMI >18.5 and BMI <24.9, overweight as a BMI of 25.0–29.9 and obesity as a BMI of >30.0 (10). During the physical examination at the MEC, a 24-hour dietary recall was administered, which assessed the amount of alcohol consumed during the previous day. The data collected were used to calculate the daily intake of alcohol (in grams). The daily alcohol consumption was categorized into >1 g/d or !1 g/d. Measurements of the MetS Components During the visit to the MEC, a fasting venous blood specimen was drawn from each participant according to a standardized protocol (11). Plasma glucose was measured at the University of Missouri Diabetes Diagnostic Laboratory using a hexokinase enzymatic method (12). Fasting serum insulin, total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides were measured at other centralized laboratories (11, 13). The Adult Treatment Panel III guidelines were used to define the MetS as the presence of three or more of the following (14): Systolic blood pressure >130 mm Hg and/or diastolic blood pressure >85 mm Hg
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HDL !40 mg/dL for men and !50 mg/dL for women Triglycerides >150 mg/dL Fasting blood glucose >100 mg/dL Waist circumference O102 cm for men and O88 cm for women Cotinine concentration in the blood, a metabolite of nicotine, was used as a biomarker to classify participants into groups of current and non-current smokers. Current smokers were defined as those who had cotinine levels O15 ng/mL, whereas those with serum cotinine <15 ng/mL were classified as non-current smokers (15, 16). Measurement of the Frontal T-wave Axis Orientation A standard 12-lead resting electrocardiogram was performed on all men and women aged 40 years or over using a Marquette MAC 12 (Marquette Medical Systems, Inc., Milwaukee, WI. ECG data were recorded with eight independent components of the 12 standard leads simultaneously and were sampled at 250 samples per second per channel. The electrocardiograms were initially processed in a central laboratory at the EPICORE Center (University of Alberta, Edmonton, Alberta, Canada) and during later phases of the study at the EPICARE Center (Wake Forest University, Winston-Salem, NC). All electrocardiographic processing was done by the Dalhousie ECG Program. An ECG program which relies on the use of selective averaging was used to derive a representative P-QRS-T complex for analysis of wave durations and to detect left ventricular hypertrophy (LVH) (17). The mean QRS duration of each individual was calculated by this program. This program has greatly improved the precision and accuracy of ECG interval measurements. Values of T-wave integrals (i.e., net T-wave areas determined from the six limb leads) were used for the mean frontal plane T-wave axis calculation. Three separate frontal T-wave axis angle values were also calculated. The angle values were calculated from three pairs of limb leads (I, AVF), (II, AVL), and (III, AVR); the final mean frontal plane axis was taken as the mean value of these three separate angle determinations. Based on the frontal T-wave axis, patients were categorized as having normal T-wave axis (>15 to <75 ), borderline (O75 to <105 or !15 to > 15 ), and abnormal (! 15 to > 180 or O105 to <180 ), according to Kors et al. (8) Statistical Analysis Of 8,561 subjects who underwent ECG testing, 110 were excluded because of missing data about frontal T-wave axis, heart rate, left ventricular enlargement or QRS duration. Other participants were excluded because they did not fast for 8 hours or more (n Z 3,536), they had missing information about the key variables of MetS (n Z 435) or daily
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alcohol consumption (n Z 138). Additionally, individuals who had electrocardiograms showing atrial or ventricular conduction abnormalities (n Z 141) or history of heart disease (n Z 391) were excluded. This left 3,810 participants for the current analyses. All analyses were performed by using Stata version 9, which took into account the complex sampling techniques used in NHANES III. Observations were weighted using weights calculated for that purpose by the National Center for Health Statistics to reflect the general U.S. population. These weights were also designed to adjust for biases attributable to non-response and over-sampling. Basic descriptive statistics, including calculation of means and their standard deviations for the continuous variables, numbers and percentages for categorical variables were performed to characterize the study patients. Comparisons between groups were made using the Student t test for continuous variables and the chi-square test for categorical variables. Univariate and multivariate weighted logistic regression analyses were conducted to examine the association of the MetS (independent variable) with frontal T-wave axis orientation (dependent variable). The strength of the association was reported as odds ratio (OR) with the 95% confidence interval. Additionally, multivariate models, testing for the interaction terms (effect modifier) between the MetS and age, MetS and gender, MetS and race separately, were performed. Trends across the number of MetS components were determined using the Cochran-Armitage test for trend. To determine the percentage of total risk of an abnormal frontal T-wave axis attributable to MetS in the study population, population-attributable risk was calculated as (Pe [OR 1]/[Pe [OR – 1] þ 1), where Pe is the prevalence of MetS and OR is the fully adjusted odds ratio. Finally, a multivariable weighted logistic regression was conducted to determine which MetS components which can predict frontal T wave axis deviation. The model included increased waist circumference, high triglyceride levels, high systolic blood pressure, high diastolic blood pressure, low HDL and impaired fasting blood glucose level.
RESULTS The overall prevalence of the MetS was 39.84%, while the overall prevalences of borderline and abnormal frontal Twave axis deviation were 14.82% and 2.97%, respectively. Participants with MetS were more likely to be female, obese, and older than those without MetS (Table 1). The daily alcohol consumption of individuals with MetS was less than that of MetS-free participants (see Table 1). In regard to the MetS components, the mean values of systolic blood pressure, diastolic blood pressure, fasting blood glucose level, waist circumference, and triglyceride levels were higher in
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TABLE 1. Personal characteristics, personal habits, ECG findings, and MetS components by MetS status Variables*
MetS n Z 2,104
Age, yr 53.92 (12.12) Male 1,080 (56.06) Race White 1,512 (86.82) Black 526 (08.95) Other 66 (04.23) Smokers 652 (29.90) Alcohol consumption >1 g/d 467 (25.40) BMI categoriesy Normal weight 1,007 (51.97) Overweight 780 (34.79) Obese 317 (13.24) QRS complex duration, ms 97.25 (12.29) Heart rate, beats/min 65.43 (10.30) LVH 148 (5.62) T-wave axis deviation Normal 1,719 (84.35) Borderline 333 (13.88) Abnormal 52 (1.77) Fasting blood glucose, mg/dL 93.14 (14.99) Systolic blood pressure, mm Hg 120.49 (14.97) Diastolic blood pressure, mm Hg 73.16 (8.10) HDL, mg/dL 55.32 (14.39) Triglycerides, mg/dL 112.13 (61.90) Waist circumference, cm 89.95 (11.70)
þ MetS n Z 1,706
p Value
58.75 (12.18) !0.01 900 (50.00) 0.01 1,305 (89.46) 0.01 359 (08.17) 42 (02.37) 445 (27.41) 0.40 262 (16.4) !0.01 232 (37.06) !0.01 705 (36.57) 769 (26.37) 99.62 (14.00) 70.10 (11.64) !0.01 225 (10.94) !0.01 1,292 (78.84) 305 (16.23) 109 (4.93) 115.80 (40.12) 137.23 (17.35) 80.28 (10.20) 43.81 (13.99) 207.15 (69.11) 103.47 (12.25)
!0.01 !0.01 !0.01 !0.01 !0.01 !0.01 !0.01 !0.01 !0.01
Z absent; þ Z present; ECG Z electrocardiographic; MetS Z metabolic syndrome; BMI Z body mass index; LVH Z left ventricular hypertrophy; HDL Z high-density lipoprotein. *Variable values expressed as mean (standard deviation) or number (percentage), as applicable. y BMI categories: normal (between >18.5 and <24.9); overweight (>25.0 and <29.9); obese (>30.0).
the MetS group, whereas the mean value of HDL was lower in the same group. Additionally, the mean QRS interval was longer and LVH was more prevalent among participants who had the syndrome, as shown in Table 1. The obtained results from the multivariate logistic regression showed that gender, smoking, and QRS duration were not statistically significant predictors for frontal Twave axis deviation. Another logistic model including the significant predictors (i.e., age, race, daily alcohol consumption, heart rate, LVH, and BMI categories) was conducted. The OR of MetS after excluding smoking, gender, and QRS duration did not differ substantially from that of MetS before excluding the previous variables. Therefore the final model included the significant predictors only. Participants who had MetS had 2.90 times greater odds of an abnormal frontal T-wave axis deviation than those without the syndrome (Table 2). The OR remained significant and became 1.79 after adjustment for age, race, alcohol drinking, heart rate, BMI categories, and LVH (see Table 2). Additionally, the interaction terms in the weighted logistic models were not significant (all p values, O0.05).
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TABLE 2. Relationship of MetS and MetS components number with frontal T-wave axis orientation Unadjusted, n Z 3,810
Adjusted,* n Z 3,810
OR (95% CI)
OR (95% CI)
T-wave axis deviation relative to normal axis (n Z 3,011) Borderline (n Z 638) 1.27 (1.05–1.64) 1.22 (1.00–1.70) Abnormal (n Z 161) 2.90 (1.79–4.69) 1.79 (1.04–3.17) Number of MetS components relative to no MetS components (n Z 456) 1 (n Z 769) 1.95 (0.90–7.38) 1.71 (0.30–7.28) 2 (n Z 879) 3.35 (1.00–1.13) 1.97 (0.41–9.90) 3 (n Z 758) 4.46 (1.42–1.36) 2.55 (0.62–11.29) 4 (n Z 948) 7.83 (2.50–0.03) 3.62 (0.70–14.20) p Trend !0.01 p Trend !0.01 MetS Z metabolic syndrome; OR Z odds ratio; CI Z confidence interval; p Trend Z p value for testing trend. *Adjusted for age, race, body mass index categories, heart rate, daily alcohol consumption, and left ventricular hypertrophy.
There was a dose-response relationship between the number of the MetS components and frontal T-wave axis deviation (see Table 2). Participants who had one, two, three, and four components or greater were 1.71, 1.95, 2.55, and 3.62 times. respectively, as likely as those who did not have any component to have abnormal T-wave axis deviation after covariate adjustment. The populationattributable risk of frontal T-wave axis shift associated with MetS was 23.94%. The weighted logistic regression revealed that high systolic blood pressure was the only significant predictor for frontal T-wave axis deviation (Table 3).
DISCUSSION In this population-based study, a strong, positive, and significant relationship between MetS and abnormal frontal Twave axis deviation was found. Participants with MetS had 1.79 times greater odds of having abnormal frontal Twave axis than those who did not have the syndrome. Additionally, 23.94%. of abnormal frontal T-wave axis cases in the NHANES III population may be attributable to MetS. The risk for abnormal frontal T-wave axis increased
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progressively with a higher number of MetS components. These relationships were independent of age, race, BMI categories, and other potential risk factors. The abnormal frontal T-wave axis deviation is an indicator for impaired ventricular repolarization (8). Therefore these data indicate that MetS is associated with ventricular repolarization impairment, which means that individuals with MetS are at a higher risk for ventricular arrhythmias. Frontal T-wave axis deviation is a simple ECG measurement (8, 18). Whether calculated automatically or manually, measurement of the frontal T-wave axis is likely to be less susceptible to noise and problems of the existing axis and amplitude measurements in the electrocardiogram (8, 18). Therefore, frontal T-wave axis was utilized as an indicator for ventricular repolarization disturbance in the present analysis. More than 50% of the participants who underwent ECG examination were ineligible to be included in the current analysis; therefore they were excluded, either because they had heart disease or they did not fast for 8 hours or more. Persons with cardiac disease were not eligible to be included in the analysis because cardiac disease could be associated with abnormal T-wave axis deviation. Fasting is important to detect high triglyceride levels and impaired fasting blood glucose concentration accurately so that individuals who have one or two of these MetS components could be captured without misclassification. Hence, participants who did not fast for 8 hours or more were excluded. The excluded participants had a higher prevalence of heart disease as well as higher mean systolic arterial blood pressure (p ! 0.05). Including such participants would overestimate the magnitude of the association between MetS and frontal T-wave axis deviation because the excluded participants were sicker than those who fulfilled the selection criteria. This study has several strengths. To our knowledge, it is the first population-based study to report a relationship between MetS, defined by the ATP III guidelines, and the risk of having abnormal T-wave axis deviation. In addition, careful measures of the study-independent MetS and dependent variables (frontal T-wave axis deviation) allowed precise estimation of the association.
TABLE 3. Relationship of the different MetS components with frontal T-wave axis deviation
MetS components Elevated SBP (n Z 1,816) relative to normal SBP (n Z 1,994) Elevated DBP (n Z 740) relative to normal DBP High triglyceride concentration (n Z 1,375) relative to normal concentration (n Z 2,435) Increased waist circumference (n Z 1,990) relative to normal waist circumference (n Z 1,820) High blood glucose level (n Z 1,673) relative to normal level (n Z 2,137) Low HDL level (n Z 1,375) relative to normal level (n Z 2,435)
Unadjusted, n Z 3,810
Adjusted,* n Z 3,810
OR (95% CI)
OR (95% CI)
3.91 (2.36–6.45) 1.68 (1.00–2.85) 1.93 (1.23–3.22) 1.88 (1.22–3.02) 1.83 (1.20–2.87) 1.46 (0.93–2.28)
3.46 (2.00–5.99) 0.95 (0.52–1.69) 1.52 (0.90–2.10) 1.38 (0.84–2.27) 1.30 (0.84–2.04) 1.14 (0.70–1.50)
MetS Z metabolic syndrome; OR Z odds ratio; CI Z confidence interval; SBP Z systolic blood pressure; DBP Z diastolic blood pressure); HDL Z high-density lipoprotein. *Multivariate logistic regression included increased waist circumference (O102 cm for men, O88 cm for women), high triglyceride levels (>150 mg/dL), increased SBP (>130 mm Hg), increased DBP (>85 mm Hg), low HDL (!40 mg/dL for men, !50 mg/dL for women), high blood glucose level (impaired fasting blood glucose if values are >100/mg/dL.
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The conclusions of the study by Soydinc et al. (19) among a small Turkish population were in line with the conclusion of the present study. Nevertheless, some aspects of the current study exist to make it unique. The results of our study are among a larger, representative sample of the U.S. adult population, including data from different racial and ethnic backgrounds. In addition, unlike our study which examined the association of the MetS with T-wave axis deviation as a subclinical risk factor for ventricular arrhythmias, the study of Soydinc et al. explored the association of MetS with a prolonged QT interval. Although a prolonged QTc interval duration is an important predictor of mortality risk, it has some limitations (20). Measurement of QTc duration is tedious and time consuming because it requires analysis of all ECG leads (18). In contrast, frontal plane T-wave axis deviation can be easily estimated from standard electrocardiograms, is less prone to problems of definition than most of the existing ECG interval measurements, and is a more sensitive indicator of altered ventricular repolarization than QT interval prolongation (8). T-wave axis deviation is a more sensitive marker than prolonged QT interval because QT interval prolongation can result from action potential duration prolongation (or delayed activation) in the last repolarizing ventricular region, whereas frontal T-wave axis deviation is influenced by action potential duration alterations, shortening or prolongation, in any myocardial region (9). The present analysis showed that high systolic blood pressure was the only significant predictor for frontal Twave axis deviation. The previous MetS components were not statistically related to frontal T-wave axis deviation. Nevertheless, the magnitudes of the association of these components were not small, and they were above 1. Although the previous components were not significant, it is noteworthy to mention that the presence of these components with the high systolic blood pressure could play a role in predicting the frontal T-wave axis deviation. This perspective is supported by the significantly observed dose-response relationship between the number of the MetS components and the frontal T-wave axis deviation. The previous components may lead to frontal T-wave axis deviation through their association with cardiac disease and hypertension. Excluding participants who suffered from heart disease and controlling for hypertension in the regression analysis may explain why impaired fasting blood glucose levels and the other MetS components were not found to be independent risk factors for frontal T-wave axis deviation in the current study. There is a relationship between high systolic blood pressure and impaired endothelial function (21–23). High systolic blood pressure leads to endothelial dysfunction by affecting nitric oxide availability, which leads to coronary angiopathy (24). The other MetS components (namely, diastolic blood pressure,
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impaired blood glucose tolerance, increased waist circumference, and dyslipidemia [low HDL and high triglyceride levels]) are known to be risk factors for endothelial dysfunction (25). Endothelial dysfunction will eventually lead to abnormal angiogenesis and decrease of blood flow to the different body organs, such as the heart. Therefore we hypothesize that heart disease–free participants with MetS suffer from undiagnosed or subclinical myocardial disease attributable to endothelial dysfunction and impairment of blood flow to myocardial muscle. Myocardial disease may lead to ventricular repolarization abnormalities represented by frontal T-wave axis deviation. Frontal T-wave axis deviation in individuals indicates a higher risk for development of adverse cardiovascular outcomes, such as ‘‘torsade de pointes’’ and cardiac arrhythmias. The findings of this study have important public health implications because MetS is becoming a pandemic in Western societies as a result of changing worldwide patterns of physical activity and food intake (1). In the United States, the overall prevalence of MetS is higher than 20% in men and women over the age of 20 years and higher than 40% in men and women over 60 years of age. Between 1988 and 1994 and between 1999 and 2000, a significant increase in the prevalence of MetS occurred among U.S. adults aged 20 years or older. Epidemiological studies have shown that T-wave axis abnormality has been associated with an increased risk of sudden death due to fatal ventricular arrhythmia (5), CHD, and mortality in a broad range of clinical populations (6, 7) as well as in healthy subjects (9). The data reported from this study also have clinical implications. The pro and con arguments on identifying the MetS as a disease are directed toward its utility in clinical practice (26–29). Some researchers believe that identifying the aggregation of hypertension, hyperglycemia, hyperlipidemia, and obesity as MetS and tailoring a treatment specifically for this syndrome have no advantage over targeting treatment toward the previous cardiovascular risk factors individually in the traditional manner (30). On the other hand, some researchers believe that clinical benefits of identifying the previous cardiovascular risk factors cluster as a syndrome do exist. On the basis of the present study results, diagnosing the syndrome with the knowledge of its strong relationship with a subclinical factor associated with fatal cardiac arrhythmias (T-wave axis deviation) will stimulate more aggressive longterm follow-up with ongoing lifestyle interventions and the use of risk-reducing drugs (26). Also, clinical experience demonstrates that when persons know that they have this syndrome, they are motivated to work on modifying the MetS components to reduce the risk of heart disease (26). The results of this study should be interpreted with caution. The cross-sectional nature of these analyses does not allow for inference of causality or for establishment of temporality between MetS and T-wave axis abnormalities. The
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history of cardiac disease was acquired from patients’ responses, which were not validated by medical records. Although self-reporting is a valid tool to collect accurate data about chronic diseases such as heart disease (29, 30), self-reporting could introduce misclassification bias. Had misclassification occurred, it would probably be non-differential with respect to MetS and thus would reduce the strength of association between these factors and T-wave axis deviation (18). Also, although adjustment for potential key confounders was performed, as in many observational studies, the presence of unknown or residual confounders which were not adjusted for is possible. Longitudinal studies are required to confirm the findings in this study. More epidemiological studies are needed to determine whether successful treatment of blood pressure, lipid profile components and waist circumference, and impaired fasting blood glucose levels among persons with MetS reduce frontal Twave axis deviation, ultimately reducing the risk of ventricular arrhythmia and sudden death in such persons. In summary, this study demonstrates that MetS could be an independent risk factor for having an abnormal frontal Twave axis in the general population. There is a graded relationship between the number of MetS components and risk for having T-wave axis deviation. Careful ECG monitoring in clinical practice as well as screening programs of persons with MetS for early detection of frontal T-wave axis deviation, an easily detected subclinical marker for the risk of incident CHD events, is warranted to identify individuals predisposed to develop adverse cardiovascular outcomes.
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