Abstract
INTRODUCTION
The QT interval reflects the time between the initial fast depolarization of the ventricle and its repolarization. Because of the variability of the left sympathetic nerve activity, the QT interval has to be corrected fort heart rate to compare individuals with different heart rates. A prolonged QTc interval (heart rate corrected using Bazett’s formula) has been associated with an increased risk for coronary heart disease, subclinical atherosclerosis, higher levels of certain indicators of metabolic syndrome and uncomplicated metabolic syndrome in developed countries (1-4). In USA, the QT-associated risk was reported higher in the black populations than whites (5). Thus, significant associations between prolonged QTc and ethnic ancestry, but not cholesterol or triglycerides levels, suggest that genetic factors may play a more important role in determining QTc interval length than conventional biochemical and metabolic cardiovascular disease risk factors (6).
In sub-Saharan Africa (SSA), higher levels of HDL-cholesterol (HDL-C) are contrasting with low levels of total cholesterol (TC) and triglycerides (TG) (7). Furthermore, in SSA, HDL-C, TC, and TG have not been useful to predict the metabolic syndrome, stroke and coronary heart disease (8-11). Applying to people from SSA without atherogenic dyslipidaemia the different definitions of metabolic syndrome predominantly used in USA (NCEP ATPIII criteria) (12) and Europe (IDF) (13), it is not surprising that its definition and its indicators have been subject to considerable debate and individual variability between the combination of the risk factors associated with metabolic syndrome (14-17). For this reason, it appears to be very difficult to understand the Pathophysiology of metabolic syndrome through traditional components among black Africans. Data on conventional components (12, 13) and other indicators of metabolic syndrome such as QT interval (3) and uric acid (18) are lacking in SSA.
OBJETIVE
Therefore, we investigated the relations of QT interval with different components and definitions of metabolic syndrome in black and Central African patients.
MATERIAL AND METHODS
Study populations
This observational and clinical research was retrospectively carried out among consecutive series of black patients and a case-control study within the age group 30 to 60 years and admitted to the Heart of Africa Cardiovascular Center of LOMO Medical, Kinshasa Limete, DRC, between January, 2004 and December, 2006. Anyone with carotid artery intima-media thickness, sickle cell disease, stroke, drugs (antimalarial, cephalosporim, amiodarone, digitalis, beta-blockers, ACE inhibitors, antiarrhythmics type I-A and type III) known to lead to QT prolongation, bundle branch block, insulin treatment, sarcoidosis, hypokalemia and tachyarythmia was excluded. Thus, the study was approved by the ethics committee of the University of Kinshasa.
Relative frequencies of metabolic syndrome (MetSyn) defined by different criteria and the relations of QT intervals with the components of the metabolic syndrome were investigated among 2950 consecutive patients from 3150 eligible patients (response rate:93.7%).
Out of 2950 consecutive patients included in the study, 0 patients (0%), 10 patients (0.3%) and 400 patients (13.6%) presented metabolic syndrome defined according to the Adult Treatment Panel III criteria NCEP (12), The International Diabetes Federation (IDF) criteria (13), and locally specific diagnosis (16), respectively. MetSyn SSA for sub-Saharan Africa was defined by waist circumference (WC) of 94 cm or greater for both men and women, plus systolic blood pressure (SBP)/diastolic blood pressure (DBP) 130/85 mmHg or greater, and fasting plasma glucose (FPG) 100 mg/dL or greater (or in drug treatment for specific risk factors). Triglycerides 1.7 mmol/L or greater or HDL-cholesterol less than 1.03 mmol/L were absent among these black African patients suffering from coronary heart disease around 40%, arterial hypertension around 80%, and diabetes mellitus around 20%.
These 400 patients with MetSyn SSA were matched with 1400 controls for sex, age, the level of BP, the level of FPG and the period of admission to the LOMO Medical Center.
Data Collection
Demographic data (sex and age), cigarette smoking, alcohol intake, anthropometric parameters (body mass index or BMI and WC), components of BP (SBP, DBP, pulse pressure as SBP-DBP), and other traditional components of MetSyn measured in blood (glucose, uric acid, total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol) were obtained from each medical chart. Blood samples are measured in our laboratory using the commercial kits of Biomérieux (France) and standard techniques, as described previously (9, 10).
QT interval (non adjusted for heart rate) and QTc interval (adjusted for heart rate according to Bazett’s formula: QTc=QT/√RR, where RR obtained from simultaneous 12-lead resting ECG records using an automated EASTOTE-ACTA Cardiograph (Italy) at a sampling frequency of 500Hz during 10s with a resolution of 5 mV and stored digitally. All QT and QTc intervals were gathered by a senior Resident (MZL) blinded for other study parameters.
Statistical analysis
Data were expressed as means ± standard deviation (SD) or percentages. Analyses of variance were used to assess differences among groups for the continuous variables. Chi-squared tests were used to compare percentages of categorical variables among all groups. Relations of QT intervals (QT and QTc as dependent variables) with other components of the metabolic syndrome were assessed through simple correlation r coefficient and linear multiple regression. Logistic regression analyses were conducted using MetSyn as the dependent variable (presence 1, absence 0) and the following as the independent variables: sex (male 1, female 0), age (continuous), QT (continuous) or QTc (continuous), BMI (continuous), uric acid (continuous), total cholesterol (continuous), HDL-cholesterol (continuous), triglycerides (continuous), and LDL-cholesterol (continuous). Smoking status (confounding effect on BMI) and alcohol intake (colinearity with QT and QTc) were not entered in the logistic regression analyses. All statistical analyses were performed using SPSS 11.0 for windows (SPSS Inc., Chicago, Illinois, USA) and statistical significance was set at Pvalue <0.05.
RESULTS
Consecutive Series
The consecutive series included 49.8% of men, 50.2% of women, 22.7% of cigarette smokers and 38.6% with alcohol intake. Table 1 shows the mean values of the study characteristics and their univariate associations with QT (0.377±0.021 ms) and QTc (0.432±0.032 ms). Only the respective univariate associations between age, SBP, pulse pressure and QT were significantly positive (Table 1 and Figure 1A,1B,1C). The levels of age and DBP were positively and significantly correlated with QTc, whereas BMI, HDL-cholesterol and triglycerides were negatively and significantly correlated with QTc, respectively (Table 1). QT for men (0.373±0.01 ms vs 0.380±0.01 ms) and QTc for men (0.428±0.01 ms vs 0.435±0.01 ms).
|
Figure 1A. Respective relationship between age (A), pulse pressure (B), systolic blood pressure (C), and QT interval in the study population. |
|
|
Figure 1B. Respective relationship between age (A), pulse pressure (B), systolic blood pressure (C), and QT interval in the study population. |
|
|
Figure 1C. Respective relationship between age (A), pulse pressure (B), systolic blood pressure (C), and QT interval in the study population. |
QT interval was significantly increased by alcohol intake but not by cigarette smoking status, whereas QTC interval was significantly increased by both cigarette smoking status and alcohol intake (Table 2). The negative and significant association between QTc and BMI remained among cigarette smokers but not in non smokers (Figure 2). There was no significant relationship (P=0.598) between QTc and BMI in drinkers but a negative and significant relation (Beta -0.213; P=0.043) between QTc and BMI in non alcohol drinkers (Figure 3).
|
| Figure 2. Relationship between QTc and BMI in smokers and no smokers. |
|
Figure 3. Relationship between QTc and BMI in alcohol drinkers (Yes) and non drinkers (No). |
The only determinant of QT interval was age as follows: Y:QT=0.337 + 0.289 age (years); standard error of 0.008 and P<0.0001. However, in considering the heart rate and after adjusting for triglycerides, the independent determinants of QTc interval were DBP increasing (P<0.01), BMI decreasing (P<0.01) and HDL-C decreasing (P<0.01) as follows: R² of 7.1%; Y: QTc=0.461 ± 0.194 DBP-0.179 BMI-0.185 HDL-C.
Comparisons of parameters were not possible with the underestimated proportions of NCEP ATPIII MetSyn (0%) and IDF MetSyn (0.3%). Table 3 shows respective and significant associations between age, BMI, QT non corrected for heart rate, uric acid, triglycerides (although low average), and MetSyn SSA (13.6% in the study population), whereas QTc, TC, HDL-C and LDL-C were similar (P>0.05) between presence and absence of MetSyn SSA. The logistic regression analysis identified QT interval non corrected for heart rate and uric acid as the independent predictors of the metabolic syndrome in these black patients after adjusting for sex, age, BMI, TC, HDL-C, LDL-C and triglycerides (Table 4). Thus, the increase of 0.001 ms of QT interval multiplied 3 times the risk of the MetSyn SSA, and the increase of 1 mg/dL of uric acid multiplied 1.3 time the risk of the MetSyn SSA.
DISCUSSION
This observational study examined the relations between components of MetSyn, definitions of MetSyn and the QT intervals (initial fast depolarization and repolarization of the ventricle) in Central African patients. These associations were independent of sex and the majority of indicators of dyslipidemia. Paradoxical associations were also shown.
Determinants of QT Intervals
The present study demonstrates the importance of both QT non corrected for heart rate and QTc adjusted for heart rate in managing the risk of the metabolic syndrome and coronary heart diseases in Africans with low lipid profile (7) and in course of epidemiologic, demographic and nutrition transitions (20). Electrocardiography (ECG) is usually available in developing countries, but not echocardiography, coronarography, CT scan and MRI. Accurate ECG-derived QT and QTc intervals were significantly correlated with age, SBP, DBP, pulse pressure, HDL-cholesterol, cigarette smoking, and alcohol intake. Age was the significant and independent determinant of QT interval, whereas DBP, BMI and HDL-C were identified as significant and independent determinants of QTc interval. These findings confirm many data reported from developed and rich societies (1-5).
The negative association between triglycerides and QT as well as the negative association between BMI, triglycerides, LDL-C and QTc, are not ease to understand in these black patients with low lipid profile (7) and nutrition transition (concomittent presence of traditional and westernized diet, presence of both malnutrition and obesity). Sarcopenic obesity reported in elderly from developed countries (21) may be present in both young and adult Africans in course of demographic transition.
In this study, there was no significant between sex, waist circumference, blood glucose, uric acid, total cholesterol, HDL-cholesterol, LDL-cholesterol and QT intervals. The gender difference for QT interval, longer QT interval in women than men reported elsewhere (4), is till now unknown. For other researchers, sex hormones may play a role in regulating cardiac repolarization and thus QT interval (22). The significant and independent association between the decrease of HDL-C and the increase of QTc interval may exist beyond the effects of DBP and pulse pressure towards atherosclerosis in these Africans (23). Indeed, the present data are opposite to those reported in developed societies: BMI and triglycerides positively related to QTc interval, but LDL-C and HDL-C not related to QTc interval in a multi-ethnic population from USA (4). Alternatively, though entirely speculative at this time, a common gene might exist which modifies both the cardiometabolic risk, the atherosclerosis process and the repolarization anomalies reflected by QT intervals (4).
Metabolic Syndrome Definitions
This study shows the underestimation of rates of MetSyn using the NCEP ATPIII criteria (12) and IDF criteria (13) in defining the MetSyn in sub-Saharan Africa. For this reason, WC cut-off point of 94 cm in men and women used as marker of abdominal obesity in SSA (16) and significant risk factor of arterial hypertension in Africans (24) and insulin resistance (16), clustered with blood glucose and BP, but not with HDL-C and triglycerides, in defining MetSyn SSA for 13.6% patients. Although the diagnostic criteria of metabolic syndrome are problematic (14-17), the present study showed a significant univariate association between age increasing, higher BMI (total obesity), QT increasing, higher uric acid, low triglycerides and MetSyn SSA in accord with the literature data (1-5, 10, 14, 15, 18). Lower cut point of triglycerides should be used in diagnosing the metabolic syndrome in Africans. Our multivariate analysis found an increased and independent association between QT interval, uric acid, and MetSyn SSA. Traditional low fat diet or genetics may explain the transient neutral effect of dyslipidemia on metabolic syndrome development in sub-Saharan Africa. The predictive value of QT interval continuously for MetSyn SSA may be attributed to high sympathetic activity and stress. Therefore, the new worldwide definition of metabolic syndrome (25) should propose specific new criteria for Africa, in which the Europoid WC cut-points and the controversial lipids (HDL-C, LDL-C, triglycerides) (8, 14, 15) are replaced by WC≥94 cm, QT non corrected and corrected for heart rate, uric acid, fibrinogen (or CRP) and Helicobacter pylori infection. Uric acid, fibrinogen and Helicobacter pylori (significantly correlated with total cholesterol increase, triglycerides increase and HDL-C decrease) have been established as independent risk factors for metabolic syndrome (10) and atherosclerotic diseases (stroke and coronary heart disease) (9, 10) in these Africans.
Limitations of the Study
The study, being an observational study, is subject to a number of potential errors. However, there is no reason to suspect important limitations as bias because on differential error in QT measurements, interobserver difference, drugs and certain diseases effects were excluded.
CONCLUSIONS
There is a strong and significant relationship between age, smoking, alcohol intake, HDL-C, metabolic syndrome without dyslipidemia, uric acid, and QT intervals duration.
Because waist circumference, QT intervals and uric acid are easily measured and significantly associated with metabolic syndrome in these African patients, they may provide additional information for cardiovascular risk stratification and management MetSyn SSA despite the transient low and less atherogenic lipid profile in Central Africans.
Acknowledgements
This work was supported by the LOMO Medical grant.
REFERENCES
1. Dekker JM, Schouten EG, Klootwijk P, Pool J, Kromhout D. Association between QT interval and coronary heart disease in middle-aged and elderly men. The Zutphen study. Circulation 1994; 90 :779-785.
2. Rautaharju PM, Manolio TA, Psaty BM, Borhani NO, Furberg CD. For the Cardiovascular Health Study collaborative Research group. Correlates of QT prolongation in older adults (the Cardiovascular Health Study). Am J Cardiol 1994; 73:999–1002.
3. Soydinc Serdar, Davutoglu Vedat, Akcay Murat. Uncomplicated Metabolic Syndrome Is Associated with Prolonged Electrocardiographic QTc Interval and QTc Dispersion. Annals of Noninvasive Electrocardiology 2006; 11: 313-317.
4. Festa A. D’Agostino R. Rautaharju P. O’Leary DH, Rewers M, Mykkänen L. Haffner SM. Is QT interval a marker of subclinical atherosclerosis in nondiabetic subjects? The Insulin Resistance Atherosclerosis Study (IRAS). Stroke 1999; 30: 1566-1571.
5. Dekker JM. Crow RS. Hannan PJ. Schouten EG. Folson AR. Heart rate-corrected QT interval prolongation predicts risk of coronary heart disease in black and white middle-aged men and women. J Am coll Cardiol 2004;43:565-571.
6. Grandinetti A, Seifried S, Mor J, Chang HK, Theriault AG. Prevalence and risk factors for prolonged QTc in a multiethnic cohort in rural Hawaï. Clin Biochem 2005;38(2):116-122.
7. Fezeu L, Balkau B, Kengue AP, Sobngwi E, Mbanya JC. Metabolic syndrome in a sub-Saharan African setting: Central obesity may be the key determinant. Atherosclerosis 2007; 193(1): 70–76
8. Isezuo SA. Is high density lipoprotein cholesterol useful in diagnosis of metabolic syndrome in native Africans with type 2 diabetes? Ethn Dis 2005; 15(1):6-10.
9. Longo-Mbenza B, Lukoki Luila E, Phanzu Mbete, Kintoki Vita E. Is hyperuricemia a risk factor of stroke and coronary heart disease among Africans? Int J Cardiol 1999;71:17-22.
10. Longo-Mbenza B, Nkondi Nsenga J, Vangu Ngoma D. Prevention of the metabolic syndrome insulin resistance and the atherosclerotic diseases in Africans infected by Helicobacter pylori infection and treated by antibiotics. Int J Cardiol 2007;121(3):229-238
11. Longo-Mbenza B, Tonduangu K, Muyeno K, et al. Predictors of stroke - associated mortality in Africans. Rev Epidemiol et Sante Publique 2000;48:31-39.
12. Anonymous. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on detection, Evaluation and Treatment of High Blood cholesterol in Adults (Adult Treatment Panel III).JAMA 2001; 285:2486-2497.
13. International Diabetes Federation. The IDf Consensus worldwide definition of the metabolic syndrome. April 14, 2005: http://www.idf.org/webdata/docs/Metac.syndrome_def.pdf (accessed March 20, 2006).
14. Longo-Mbenza B, Agongola Mambune HF, Kasiam JB, Kintoki Vita E, Mbungu Fuele S, Nkondi Nsenga J, Mabwa L, Nzuzi V. Relationship Between Waist Circumference, and Cholesterol in Central Africans With Congestive Heart Failure. In Press.
15. Longo-Mbenza B, Kasiam Lasi On’kin JB, Nge Okwe A, Kangola Kabangu N. Paradoxical and curvilinear relationship between components of metabolic syndrome, atherosclerosis, and HDL-cholesterol in Africans with type 2 diabetes mellitus: proposition of metabolic syndrome definition for sub-Saharan Africa.
16. Longo-Mbenza B on behalf of LOMO.Prevalence and appropriate cut-off points of overall and abdominal obesity for sub-Saharan Africa.Eur Heart J 2007; 28 Abstract Supplement: 783 (Abstract 4478).
17. Van Zwieten PA, Mancia G.The metabolic syndrome – a therapeutic challenge. ESH Van Zuiden Communications, Alphen aan den Rijn, the Netherlands, 2005.
18. Reaven G. Role of insulin resistance in human disease. Diabetes 1988;37:1595-1607.
19. Bazett HC. An Analysis Of Time Relations Of The Electrocardiogram. Heart 1920;7:353-370.
20. Omran AR. The Epidemiologic Transition, The Key Of The Epidemiology Population Change. Milibank Memorial Fund Q 1971;49:509-538.
21. Dominguez LI, Barbagallo M. The Cardiometabolic Syndrome And Sarcopenic Obesity In Older Persons. J Cardiometab Syndr 2007;2(3):183-9.
22. Drici MD, Burklow TR, Haridasse V, Glazer RI, Woosley RL. Sex Hormones Prolong QT Interval And Downregulate Potassium Channel Expression In The Rabbit Heart. Circulation 1996; 94:1471-1474.
23. Longo-Mbenza B, Bieleli E, Muls E, Vangu N, Ditu Mpadamadi S. The Role Of Early Hemodynamic Impairment And Disease Duration On Diabetic Cardiomyopathy And Hypertension In Central Africans With Atherosclerosis. J Diabetes Complications 2002; 16(2):146-152.
24. Longo-Mbenza B, Nkoy Belila J, Vangu Ngoma D, Mbungu S. Prevalence And Risk Factors Of Arterial Hypertension Among Urban Africans In Workplace: The Obsolete Role Of Body Mass Index. Niger J Med 2007;16(1):42-49.
25. Alberti KGMM, Zimmet P, SHAW J. Metabolic Syndrome: A New Worldwide Definition: A Consensus Statement From The International Diabetes Federation. Diabet Med 2006;23:469-480.
Publication: September - November/2009
|
CETIFAC
