Computing in Cardiology 2011, 18-21 September 2011, Zhejiang University, Hangzhou, ChinaElevated QT interval variability (QTV) has been asso- ciated with increased cardiac mortality, but the underlying mechanisms are incompletely understood. Sympathetic ac- tivity is thought to be a main contributor to QTV. The aim of this study was to investigate the relation between car- diac sympathetic integrity and QTV in 15 patients with type 2 diabetes mellitus and varying degrees of cardiac autonomic neuropathy. Cardiac sympathetic innervation was assessed by 123I-mIBG scintigraphy based on heart- to-mediastinum ratio of 123I-mIBG uptake 4 hours after infusion. To assess QTV high resolution ECGs (1000 Hz) were recorded during standing. Beat-to-beat QT inter- vals were calculated over a period of 5 minutes, using a template-stretching algorithm. QTV was quantified using time and frequency domain measures as well as non-linear approaches (symbolic dynamics, fractal dimension). The group mean and standard deviation of HMR values were 1.07 ± 0.48. Time and frequency domain QTV parame- ters were significantly increased in subjects with sympa- thetic dysinnervation and inversely correlated with HMR (r = −0.7, p < 0.001). In conclusion, there is a clear link between sympathetic dysinnervation and elevated QTV in patients with type 2 diabetes mellitus during sympathetic activation. Sympathetic dysinnervation is associated with increased ventricular repolarization lability.Mathias Baumert, Julian Sacre and Bennett Franjichttp://www.cinc.org/archives/2011

Baumert, M.

Franjic, B.

Sacre, J.

Elevated QT interval variability (QTV) has been associated with increased cardiac mortality, but the underlying mechanisms are incompletely understood. Sympathetic activity is thought to be a main contributor to QTV. The aim of this study was to investigate the relation between cardiac sympathetic integrity and QTV in 15 patients with type 2 diabetes mellitus and varying degrees of cardiac autonomic neuropathy. Cardiac sympathetic innervation was assessed by I-mIBG scintigraphy based on heart-to-mediastinum ratio of I-mIBG uptake 4 hours after infusion. To assess QTV high resolution ECGs (1000 Hz) were recorded during standing. Beat-to-beat QT intervals were calculated over a period of 5 minutes, using a template-stretching algorithm. QTV was quantified using time and frequency domain measures as well as non-linear approaches (symbolic dynamics, fractal dimension). The group mean and standard deviation of HMR values were 1.07 ± 0.48. Time and frequency domain QTV parameters were significantly increased in subjects with sympathetic dysinnervation and inversely correlated with HMR (r = -0.7, p < 0.001). In conclusion, there is a clear link between sympathetic dysinnervation and elevated QTV in patients with type 2 diabetes mellitus during sympathetic activation. Sympathetic dysinnervation is associated with increased ventricular repolarization lability

Baumert, Mathias

Sacre, Julian

Franjic, Bennett

University of Queensland eSpace

Relation between QT interval variability and cardiac sympathetic innervation in patients with diabetes mellitus

Adelaide Research &amp; Scholarship

PUBLISHED VERSION   Mathias Baumert, Julian Sacre and Bennett Franjic Relation between QT interval variability and cardiac sympathetic innervation in patients with diabetes mellitus Computing in Cardiology, 2011; 38:57-60 Articles in this volume are copyright (C) 2011 by their respective authors, and are licensed by their authors under the Creative Commons Attribution License 2.5 (CCAL).                    PERMISSIONS   http://creativecommons.org/licenses/by/2.5/        http://hdl.handle.net/2440/71319  Relation Between QT Interval Variability and Cardiac SympatheticInnervation in Patients with Diabetes MellitusMathias Baumert1, Julian Sacre2, Bennett Franjic21The University of Adelaide, Adelaide, Australia2University of Queensland, Brisbane, AustraliaAbstractElevated QT interval variability (QTV) has been asso-ciated with increased cardiac mortality, but the underlyingmechanisms are incompletely understood. Sympathetic ac-tivity is thought to be a main contributor to QTV. The aimof this study was to investigate the relation between car-diac sympathetic integrity and QTV in 15 patients withtype 2 diabetes mellitus and varying degrees of cardiacautonomic neuropathy. Cardiac sympathetic innervationwas assessed by 123I-mIBG scintigraphy based on heart-to-mediastinum ratio of 123I-mIBG uptake 4 hours afterinfusion. To assess QTV high resolution ECGs (1000 Hz)were recorded during standing. Beat-to-beat QT inter-vals were calculated over a period of 5 minutes, using atemplate-stretching algorithm. QTV was quantified usingtime and frequency domain measures as well as non-linearapproaches (symbolic dynamics, fractal dimension). Thegroup mean and standard deviation of HMR values were1.07 ± 0.48. Time and frequency domain QTV parame-ters were significantly increased in subjects with sympa-thetic dysinnervation and inversely correlated with HMR(r = −0.7, p < 0.001). In conclusion, there is a clear linkbetween sympathetic dysinnervation and elevated QTV inpatients with type 2 diabetes mellitus during sympatheticactivation. Sympathetic dysinnervation is associated withincreased ventricular repolarization lability.1. IntroductionQT interval variability reflects temporal fluctuations inthe duration of ventricular repolarization. Elevated QTvariability has been associated with increased sudden car-diac death risk in chronic heart failure [1] and ventricu-lar arrhythmogenesis in patients with structural heart dis-ease [2]. The origins of QT variability, particularly theinfluences of autonomic nervous system activity, remainincompletely understood. Acute elevation of QT vari-ability has been observed in response to sympathetic ac-tivation [3] and in disease states characterized by sympa-thetic overactivity [1, 2]. Conflicting results on associa-tions with cardiac norepinephrine spillover, the gold stan-dard in assessment of cardiac sympathetic activity, havebeen reported. Positive correlations were described in hy-pertension [4] but not major depression/panic disorder [5].The cardiac autonomic neuropathy (CAN) of type 2 di-abetes mellitus (T2DM) is independently associated withpoor prognosis [6]. Vagal impairment of cardiac controlcan be readily assessed by measuring heart rate variability[7]. Sympathetic involvement in CAN may be identifiedwith high specificity by radionuclide imaging using thetracer iodine 123-metaiodobenzylguanidine (123I-mIBG),which shares the neuronal uptake and storage mechanismsof norepinephrine. Reduced heart rate variability as wellas globally reduced cardiac uptake of 123I-mIBG, consis-tent with vagal and sympathetic dysinnervation, have beenidentified in patients with diabetes mellitus [6]. In a re-cent study we reported negative correlations between car-diac 123I-mIBG uptake and QT variability in patients withT2DM, suggestive of an association between myocardialsympathetic dysinnervation and repolarization lability. [8].Importantly, this association was only observed during aperiod of sympathetic activation (i.e. standing), but notduring rest. The aim of this study was to further identifyspecific features of QT interval variability that might beprimarily reflective of sympathetic dysinnervation.2. Methods2.1. SubjectsSubjects with T2DM (n = 15) with no history of car-diovascular disease, cancer or psychiatric or other severeillness were recruited from the community (Table 1). 123I-mIBG imaging and heart rate variability data for this co-hort has been reported previously [9]. Exercise echocar-diography studies were performed in all patients to verifynormal ejection fraction (> 50%) and the absence of coro-nary artery disease (i.e. no inducible wall motion abnor-malities indicative of ischemia). Patients provided writteninformed consent and the study protocol was approved byISSN 0276−6574 57 Computing in Cardiology 2011;38:57−60.Table 1. Patient characteristics.Male [%] 60Age [yrs] 57.9± 8.1Diabetes duration [yrs] 7 [6-14]HbA1c [mmol/mol] 8.2± 2.2BMI [kg/m2] 30.2± 5.4Heart rate [bpm] 71± 12Systolic BP [mmHg] 125± 19Diastolic BP [mmHg] 72± 9Hypertension [%] 9Heart-to-mediastinum ratio 1.84± 0.16hospital and university human research ethics committees.2.2. 123I-MIBG imagingProtocols for recording and analysis of 123I-mIBG im-ages have been described in detail [9]. Patients were pre-medicated with 600 mg potassium perchlorate to blockthyroid uptake of radioiodine. A low-energy, high-resolution collimator (Symbia, Siemens, Erlangen, Ger-many) was used in the acquisition of anterior planar andsingle photon emission computed tomography (SPECT; 32projections for 50 s each) images 15 minutes (early) and4 hours (delayed) following injection of 150 MBq of 123I-mIBG. Global cardiac uptake of 123I-mIBG was calculatedfrom both early and delayed planar images by the ratio oftracer activity (mean count per pixel) in the heart and me-diastinum. Due to non-neuronal uptake affecting early im-ages, the delayed heart-to-mediastinum ratio (HMR) wasprimarily used in analyses and to define the presence ofcardiac sympathetic dysinnervation (HMR < 1.8) [10].2.3. ECG recording and QT variabilityanalysisStudies were performed in accordance with standardconditions for clinical autonomic testing. Subjects wereassessed in the morning and following a light meal and ad-ministration of diabetes medications. Pre-test abstinencefrom smoking and caffeine (12 hours), and alcohol, heavyexercise and anti-hypertensive medications (24 hours) wasrequired. Following at least 20 minutes supine rest in aquiet room, an ECG (lead II) was recorded continuouslyover 5-minutes during standing at a sampling frequencyof 1kHz using a Powerlab 8SP data acquisition systemlinked with commercially available software (LabChartPro v6.1.3, AD Instruments, Sydney, Australia). After vi-sual inspection of all ECG recordings to remove artifacts,beat-to-beat QT intervals were calculated using the algo-rithm proposed by Berger et al [1]. Here, an operator-defined QT interval template is selected for one beat basedTable 2. QT variability measures.meanQT mean QT interval duration, in ssdQT standard deviation of QT intervals, in sRMSSD RMS of QT interval differences, in sLF low frequency power, in s2HF high frequency power, in s2DH Higuchi’s fractal dimension0V words with zero variation, in %1V words with one variation, in %2LV words with 2 likewise variations, in %2ULV words with 2 unlike variation, in %on the beginning of the QRS complex and the beginningand end of the T wave. The algorithm then calculates theQT intervals of all other beats by determining the degreeto which the template must be stretched or compressed intime to optimally match each T wave. In contrast with al-ternative methods, consideration of the whole T wave en-ables a relatively robust estimation of beat-to-beat QT in-terval changes. Of the beat-to-beat QT interval time serieswe computed the measures summarized in Table 2.3.Time and frequency domain measures were computedaccording to the Heart Rate Variability Task Force guide-lines [11]. To compute the fractal dimension DH ofa graph, Higuchi considers a finite set of observationsX(j), j = 1, 2, ..., N taken at a regular interval k, andevaluates the length Lm(k) of the corresponding graphfor different interval lengths k from sequences Xkm :X(m), X(m+k), X(m+2k), ..., X(m+[N−mk]), wherem = 1, 2, ..., k and[N−mk]denotes the integer part of(N −m)/k. The length of the graph is calculated asLm(k) =[N−mk ]∑i=1|X(m+ ik)−X(m+ (i− 1)k)| (1)N − 1[N−mk]k2.If the behaviour of the graph has fractal characteristics overthe available range k thenL(k) ∝ k−DH , (2)where DH is the fractal dimension and L(k) is the aver-age value over k partial lengths of the graph. For a straightline, DH = 1. For Brownian motion, DH = 1.5, andfor Gaussian white noise, DH saturates at two. For timeseries with 1/fβ power spectra, DH = (5 − β)/2. Thisrelationship is valid for 1 < β < 3. Numerical exper-iments have shown that time series with the same β canshow different DH values depending on the phase distri-bution [12]. We have previously employed Higuch’s frac-tal dimension to measure the roughness of R-R time series58[13]. For symbolic analysis of QTV we employed the ap-proach proposed by Porta et al. [14]. Time series of QTintervals were transformed into an alphabet of 6 symbols0, 1, 2, 3, 4, 5. As a transform rule nonuniform quanti-zation was applied, keeping constant the number of pointsassociated with each quantization level. All the patterns(symbolic sequences) with a length of three were groupedinto four families according to the number and types ofvariations from one symbol to the next. The pattern fami-lies are: 1) patterns with no variation (0V—all three sym-bols are equal); 2) patterns with one variation (1V—twoconsecutive symbols are equal and the remaining one isdifferent); 3) patterns with two like variations (2LV—thethree symbols form an ascending or descending ramp), 4)patterns with two unlike variations (2ULV—the three sym-bols form a peak or a valley).2.4. StatisticsFor statistical analysis we computed mean values andstandard deviations of QTV measures as well as 123I-mIBG markers. To investigate the relationship betweenQTV measures and cardiac sympathetic dysinnervation,we computed Pearson’s correlation coefficients.3. ResultsPatients displayed a wide range of cardiac 123I-mIBGuptake, ranking from normal (HMR > 1.8; n = 9) toabnormal (HMR < 1.8; n = 5). Measures of QTV aredisplayed in Table 3 subdivided based on presence of sym-pathetic dysinnervation (Table3) All time and frequencydomain measures indicate elevated QTV in patients withsympathetic dysinnervation. Non-linear measures, whichassess the complexity rather than the magnitude of QTVshow no significant group differences Pearson’s correla-tion coefficients as indicated in the last column of Ta-ble 3 show a similar degree of correlation between all QTVtime and frequency domain measures and HMR aroundr = 0.7, with the exception of HF. None of the complexitymeasures shows a significant correlation with HMR.4. Discussion and conclusionsThe main finding of our study is that magnitude ratherthan complexity of beat-to-beat QT interval variabilityelicited by sympathetic activation is associated with sym-pathetic dysinnervation. Time and frequency domain anal-ysis of QTV suggest a lack of a specific frequency rangebeing primarily responsible for this association. Assess-ment of QTV complexity and its lack of association with123I-mIBG uptake further suggests that there is no specifictemporal pattern of QTV indicative of sympathetic dysin-nervation. In a recent study we investigated temporal dy-Table 3. Group means and standard deviations of QT vari-ability measures in patients with normal sympathetic in-nervation (T2DM+) and with sympathetic dysinnervation(T2DM-), t-test p-values as well as Pearson’s correlationcoefficient r with HMR.T2DM+ T2DM- p rmeanQT 356.2± 33.2 378.0± 33.2 0.16 -0.38sdQT 3.6± 0.8 6.7± 0.8 0.02 -0.75RMSSD 2.9± 1.4 7.7± 1.4 0.01 -0.77LF 0.9± 0.4 4.6± 0.4 0.01 -0.71HF 1.4± 1.1 5.4± 1.1 0.07 -0.57DH 1.9± 0.1 2.0± 0.1 0.09 -0.330V 32.9± 14.6 25.1± 14.6 0.30 0.231V 39.5± 4.7 42.0± 4.7 0.32 -0.192LV 4.3± 3.5 6.2± 3.5 0.45 -0.262ULV 23.3± 9.2 26.7± 9.2 0.43 -0.13namics of QTV by means of detrended fluctuation analysisand multiscale entropy and observed no long-range corre-lations and multiscale entropy patterns, which were similarto those of random data [15]. Despite the close physiologi-cal relationship between average heart rate and average QTinterval, temporal short-term dynamics differ notably, thelatter being more erratic. With regard to sympathetic in-volvement in the generation of QTV, our current findingspoint towards a rather non-specific association.Our data support the hypothesis that QT variabilityreflects sympathetic function in the context of acute orchronic sympathetic activation, as with standing, selectivepharmacological intervention, or disease. However, 123I-mIBG scintigraphy is not synonymous with measuringsympathetic activity per se, but instead reflects the holisticintegrity of postganglionic presynaptic sympathetic nerveterminals, including norepinephrine uptake, storage andrelease mechanisms [16]. Indeed, the dysinnervation iden-tified by low HMR in our patients may relate to structural,rather than functional defects, characterized by anatomicalneuronal loss (i.e. denervation).In conclusion, there is a clear link between sympatheticdysinnervation and elevated QTV in patients with type 2diabetes mellitus during sympathetic activation. Sympa-thetic dysinnervation is associated with increased ventric-ular repolarization lability.AcknowledgementsThis study was partly supported by a grant from the Aus-tralian Research Council (110102049).59References[1] Berger RD, Kasper EK, Baughman KL, Marban E, Calkins H,Tomaselli GF. Beat-to-beat QT interval variability: novel evidencefor repolarization lability in ischemic and nonischemic dilated car-diomyopathy. Circulation 1997;96:1557–65.[2] Tereshchenko LG, Fetics BJ, Berger RD. Intracardiac QT variabil-ity in patients with structural heart disease on class III antiarrhyth-mic drugs. J Electrocardiol 2009;42:505–510.[3] Yeragani VK, Pohl R, Jampala VC, Balon R, Kay J, Igel G. Ef-fect of posture and isoproterenol on beat-to-beat heart rate and QTvariability. Neuropsychobiology 2000;41:113–123.[4] Baumert M, Schlaich MP, Nalivaiko E, Lambert E, Sari CI, KayeDM, Elser MD, Sanders P, Lambert G. Relation between QT in-terval variability and cardiac sympathetic activity in hypertension.Am J Physiol Heart Circ Physiol 2011:300:H1412–1417.[5] Baumert M, Lambert GW, Dawood T, Lambert EA, Esler MD, Mc-Grane M, Barton D, Nalivaiko E. QT interval variability and car-diac norepinephrine spillover in patients with depression and panicdisorder. Am J Physiol Heart Circ Physiol 2008;295:H962–H968.[6] Mantysaari M, Kuikka J, Mustonen J, Tahvanainen K, Vanni-nen E, Lansimies E, Uusitupa M. Noninvasive detection of car-diac sympathetic nervous dysfunction in diabetic patients using[123I]metaiodobenzylguanidine. Diabetes 1992;41:1069–1075.[7] Javorka M, Trunkvalterova Z, Tonhajzerova I, Javorkova J, Ja-vorka K, Baumert M. Short-term heart rate complexity is re-duced in patients with type 1 diabetes mellitus. Clin Neurophysiol.2008;119:1071–81.[8] Sacre JW, Franjic B, Coombes JS, Marwick TH, Baumert M. QTinterval variability index is associated with 123I-MIBG cardiacsympathetic activity in type 2 diabetes. European Society of Cardi-ology Meeting, Paris 2011.[9] Sacre JW, Franjic B, Jellis CL, Jenkins C, Coombes JS, MarwickTH. Association of cardiac autonomic neuropathy with subclinicalmyocardial dysfunction in type 2 diabetes. JACC Cardiovasc Imag-ing 2010;3:1207–1215.[10] Scholte AJ, Schuijf JD, Delgado V, Kok JA, Bus MT, Maan AC,Stokkel MP, Kharagitsingh AV, Dibbets-Schneider P, van der WallEE, Bax JJ. Cardiac autonomic neuropathy in patients with diabetesand no symptoms of coronary artery disease: comparison of 123I-metaiodobenzylguanidine myocardial scintigraphy and heart ratevariability. Eur J Nucl Med Mol Imaging 2010;37:1698–1705.[11] Malik M. Heart rate variability. Standards of measurement, physio-logical interpretation, and clinical use. Task Force of the EuropeanSociety of Cardiology and the North American Society of Pacingand Electrophysiology. Eur Heart J 1996;17:354–81.[12] Higuchi T. Relationship between the fractal dimension and thepower law index for a time series: a numerical investigation. Phys-ica D 1990;46,254–264.[13] Baumert M, Wessel N, Schirdewan A, Voss A, Abbott D. Scalingcharacteristics of heart rate time series before the onset of ventric-ular tachycardia. Ann Biomed Eng 2007;35:201–207.[14] Porta A, Tobaldini E, Guzzetti S, Furlan R, Montano N, Gnecchi-Ruscone T. Assessment of cardiac autonomic modulation duringgraded head-up tilt by symbolic analysis of heart rate variability.Am J Physiol Heart Circ Physiol. 2007;293:H702–H208.[15] Baumert M, Javorka M, Seeck A, Faber R, Sanders P, Voss A. Mul-tiscale entropy and detrended fluctuation analysis of QT intervaland heart rate variability during normal pregnancy. Comput BiolMed. 2011 Apr 28[16] Wakabayashi T, Nakata T, Hashimoto A, Yuda S, Tsuchihashi K,Travin MI, Shimamoto K. Assessment of underlying etiology andcardiac sympathetic innervation to identify patients at high risk ofcardiac death. J Nucl Med 2001;42:1757–1767.Address for correspondence:Mathias BaumertThe University of AdelaideSchool of Electrical and Electronic EngineeringSA 5005, AustraliaE-mail address: mbaumert@eleceng.adelaide.edu.au60

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Relation between QT interval variability and cardiac sympathetic innervation in patients with diabetes mellitus

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University of Queensland eSpace

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Relation between QT interval variability and cardiac sympathetic innervation in patients with diabetes mellitus

Abstract

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University of Queensland eSpace

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Adelaide Research & Scholarship