Effects of E-Cigarettes & Tobacco on Heart Rate Variability Parameters, A Case-Control Study. Which One is Worse?  

Effects of E-Cigarettes & Tobacco on Heart Rate Variability Parameters, A Case-Control Study. 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Which One is Worse? Volkan Çamkıran, Batool Achmar, Ahmad Achmar, İlhan Kutlu, Emel kurul, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7115478/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study aims to examine how heart rate variability (HRV) parameters, hemodynamic parameters and serum cotinine levels differ across the following groups: tobacco smokers, e-cigarette users and healthy non-smokers. This study explicitly examined a group of healthy people. Methods: Ninety healthy volunteers, aged between 18 and 45, were enrolled in the study (32 men and 58 women). Participants were divided into three groups of thirty each: e-cigarette users, traditional cigarette smokers and non-smokers. To examine the amount of tobacco exposure, serum cotinine levels were measured. Additionally, HRV data were examined in both the frequency and temporal domains. Statistical analyses were performed in Python 3.8 using the scikit-learn, pandas, and SciPy packages. Results: E-cigarette users had a noticeably longer smoking history than tobacco smokers (p=0.027). The baseline systolic blood pressure (SBP) and mean blood pressure (MBP) of the groups varied considerably (p=0.037 and p=0.021, respectively), with the non-smoker group exhibiting significantly higher SBP and MBP. For diastolic blood pressure (DBP), the change was borderline (p=0.073) but nearly significant. All HRV variables displayed standard deviation of normal-to-normal intervals (SDNN): -0.72 ms, root mean square of successive differences (RMSSD): -0.86 ms, proportion of consecutive RR intervals that is different by more than 50 ms (pNN50): -0.60% year and triangular interpolation of normal-to-normal intervals (TINN): -3.79 ms annually as age increased (all p ≤ 0.01). However, there were no significant differences in serum cotinine levels or HRV indices between e-cigarette users, non-smokers and traditional smokers (all p > 0.3). Conclusions: There were no discernible variations in cotinine levels or HRV indices among traditional cigarette smokers, e-cigarette users and non-smokers. The long-term effects of nicotine products require more research, including longitudinal designs, higher cohort sizes and other physiological and biochemical inflammatory indicators, such as endothelial function markers and cytokines. Heart Rate Variability E-Cigarette Cardiovascular Autonomic Function Smoking Background Smoking is a well-established public health problem leading to disabilities and death throughout the globe. It has a crucial role in major preventable diseases including atherosclerotic cardiovascular disease, stroke and cancer. Despite increased awareness regarding its harms, it continues to contribute to impaired health status globally ( 1 ). In recent years, the prevalence of smoking tends to decrease worldwide however a certain period is needed to observe the health benefits of smoking cessation especially in chronic disorders ( 2 ). Nevertheless, electronic cigarettes (EC) have been gaining popularity, particularly among younger individuals ( 3 , 4 ). There are several reports and ongoing research that EC can be as harmful as tobacco cigarettes (TC) despite previous conflicting results from some studies ( 5 , 6 ). Although EC may be helpful to quit TC smoking, it was shown to increase the risk of tobacco smoking especially in previously non-smokers and younger individuals ( 3 ). Besides data regarding acute adverse effects of ECs, several pathophysiologic mechanisms have been linked to increased cardiovascular disease risk including oxidative stress and inflammation ( 5 ). Heart rate variability (HRV), as a non-invasive applicable tool for the diagnosis of autonomic dysfunction, measures the variation of consecutive R-R intervals on electrocardiogram ( 7 ). HRV indicates the balance between sympathetic and parasympathetic tones and is accepted as a biomarker for general health status. For an optimally functioning autonomic nervous system (ANS), higher HRV is needed which shows an increased ability of the heart to effectively adapt to prompt ANS changes ( 7 , 8 ). In this case control study, we aimed to reveal differences in HRV parameters among tobacco smokers, e-cigarette users and non-smoker healthy individuals. Materials and methods Study Population and design A total of 90 healthy volunteers (32 males, 58 females) between the ages of 18–45 were included in the study. The study individuals were selected among university students and hospital employees including health care providers and non-clinical services staff. Participants were selected randomly using convenience sampling from the dataset of individuals who met the inclusion criteria. Thirty individuals per group (non-smokers, traditional cigarette smokers and e-cigarette users) were included to ensure equal group sizes for comparison. The demographic and lifestyle information of the individuals were collected at the beginning of the study. This basic information included the age, height in meters, weight in kilograms, smoking status including duration and daily number of conventional and/or electronic cigarettes, brand names, nicotine concentration and aroma preferences. The data regarding health status includes the presence of any chronic illness, pregnancy, medications including vitamin supplements and nicotine replacement therapy, alcohol use and drug abuse. Body mass index (BMI) was calculated by the formula: weight in kilograms/square of height in meters. Obese and underweight individuals (BMI equals or higher than 30 kg/m² and equals or lower than 18 kg/m², respectively), pregnant women, individuals with any chronic or acute illness including asthma, hypertension, heart disease, diabetes, hyperlipidemia, chronic kidney disease, and active infection or cancer, the ones who consume illicit drugs and 2 or more alcoholic drinks/day, prescription drug users (except oral contraceptives) including licensed nicotine replacement therapies, and athletes were excluded. The healthy individuals fulfilling the study criteria were divided into 3 groups. The first group was explicit TC smokers for at least 6 months. The second group was explicit electronic cigarette smokers for at least 6 months. The third group was “control group” including nonsmokers that are not exposed to cigarette smoke for more than 2 hours daily for at least 6 months. Each study group included 30 self-identified e-cigarette smokers, 30 self-identified TC smokers and 30 self-identified nonsmoker control group. To reveal the chronic or non-acute effects and to prevent interference of acute withdrawal of electronic or TC, subjects were asked to refrain from e-cigarettes and smoking tobacco for twelve hours prior to the study. Alcohol, caffeine and nicotine intake are also restricted. The systolic and diastolic blood pressures were measured in both arms by a single study nurse by using the one calibrated sphygmomanometer and the greater arm values were recorded for everyone. The blood samples were taken to measure serum cotinine levels to show tobacco exposure in the study population. Blood test Heparinized tubes were used to collect blood samples from the patients, which were carefully done by a professional nurse in the study hospital. The blood samples were drawn 10 minutes after the HRV measurements. The left antecubital vein was used to collect blood in all patients. The blood sample was centrifuged, and the separated serum was stored at -40° C for cotinine analysis. Thermo Scientific DRI Cotinine Assay was utilized to measure cotinine levels by an analyzer (ARCHITECT c16000 clinical chemistry analyzer; Abbott Laboratories, Abbott Park, IL, USA). HRV Measurements The HRV measurements were performed in the supine position in a quiet hospital room with a controlled temperature of 21 degrees Celsius (C). During measurements, no mobile phones or other digital devices were permitted, and unnecessary speech was prohibited during data collection. The HRV test was carried out after 15 minutes of rest in the supine position. To avoid the influence of circadian rhythm on autonomic tone, participants were studied between 9 a.m. and 2 p.m. The Polar H10 heart rate monitor (Polar Electro Oy, Kempele, Finland) was used to assess resting HRV data. The participants wore the chest strap sensor during the entire 7-minute recording session. The Polar H10 was linked via Bluetooth to the Kubios HRV mobile application (Kubios Oy, Kuopio, Finland), which enabled data collection in real time. The beat-to-beat R-R interval was recorded using the Kubios application which subsequently provided accurate HRV readings. After completing the recording, the data was exported in an appropriate format to undertake further studies by the Kubios HRV software. This approach is in accordance with standard methods of HRV data collection and analysis as provided in the Kubios HRV Scientific User's Guide ( 9 ). The time and frequency domain HRV parameters were analyzed in this study. The time-domain HRV parameters including the standard deviation of normal-to-normal intervals (SDNN) (shows the overall variation within the RR interval series), the root mean square of successive differences (RMSSD), and the proportion of consecutive RR intervals that is different by more than 50 ms (pNN50) (shows parasympathetic cardiac modulation) and triangular interpolation of normal-to-normal intervals (TINN) (This is baseline width of the distribution that approximates the normal to normal (NN) interval distribution.) ( 10 , 11 ). The frequency domain analysis which converts signals into frequency bands by fast Fourier transform (FFT) is represented by very low frequency (VLF) (0.003–0.04 Hz) (represents sympatho-vagal balance) ( 12 ), low frequency (LF) (0.04–0.15 Hz) (shows both sympathetic and vagal tone) and high frequency (HF) (0.15–0.40 Hz) (shows parasympathetic modulation) ( 10 ). The LF/HF ratio indicates a balance between sympathetic and parasympathetic tone. A low LF/HF ratio means parasympathetic, but a high LF/HF ratio shows sympathetic dominance ( 7 , 9 , 10 ). Total power (TOTpow_FFT) shows the total variance in heart rate pattern during recording and is reported as an average of every 5 min ( 13 ). The study was approved by the local ethics committee with the protocol number: 24–169, and written informed consent was obtained from each study participant. Statistical Analyses Statistical analyses were performed in Python 3.8 using the scikit-learn, pandas and SciPy packages. Continuous variables are summarized as median/ interquartile range (IQR), mean ± standard deviation and categorical variables as counts and percentages. For comparison of categorical variables across groups and any expected cell count < 5, then Fisher's exact test is used. To compare non-smokers, smokers and e-cigarette users, we first assessed normality with the Shapiro–Wilk test. The non-normal variables were compared using the Kruskal–Wallis's test (with Dunn's multiple comparisons). One-way ANOVA (Analysis of Variance) was used to compare the means of the groups. Multivariate linear regression analysis was performed to search for the association between smoking status, age, sex and BMI with heart-rate variability indices and serum cotinine levels. Beta and 95% confidence intervals were utilized to search for the relationship between determinants and smoking groups. A critical p-value of 0.05 was chosen, and contingency tables yielding p < 0.05 were considered to indicate statistically significant differences between classification approaches. Results This dataset comprises 90 participants, divided into three equal groups of 30 each: non-smokers (N), traditional cigarette smokers (T), and electronic cigarette users (E). It includes 114 variables in total. The demographic continuous variables are gender, age, height, weight, BMI and smoking duration. There were 58 females (64%), and 32 (%36) males in the total study group. There was no significant difference between study groups in terms of basic demographic characteristics. In our data, traditional smokers reported a median smoking duration of 5.0 [3.0–9.5] years, while e-cigarette users had a shorter history averaging 3.0 [1.8–4.5] years; non-smokers, by definition, had no prior smoking exposure. Smoking duration was significantly higher in tobacco smokers than the e-cigarette users (p = 0.027). Table 1 demonstrates the key demographic characteristics, blood pressure and cotinine data stratified by smoking status. Data are presented as median (IQR), mean ± SD or n (%). There were no statistically significant differences in the median [IQR] values of age, height, weight, and BMI among the three groups (p-values ranging from 0.351 to 0.584). Similarly, the distribution of sex was balanced across groups (p = 0.712). However, the duration of prior use differed significantly between the "Smoker" and "E-cigarette" groups (p = 0.027), indicating distinct levels of exposure across these groups. Statistically significant differences between groups were observed for baseline SBP and MBP (p = 0.037 and p = 0.021, respectively) with significantly higher SBP and MBP values in the non-smoker group. For DBP, the difference approached significance however remained borderline with p = 0.073. Serum cotinine levels were also similar among groups (p = 0.118). In summary, while demographic and basic anthropometric characteristics were balanced across groups, some differences were observed in smoking exposure and certain hemodynamic parameters. In the comparison of median heart rate (HR) and HRV parameters by smoking status, there was not any significant difference between study groups. Table 2 summarizes the findings regarding HR and HRV parameters by smoking status. The median [IQR] values of both time-domain (Mean HR, Mean RR, SDNN, RMSSD, pNN50, TINN) and frequency-domain (VLF, LF, HF power, LF/HF ratio, total power) parameters are highly similar across the three groups. All Kruskal–Wallis p-values ranged between 0.387 and 0.977, remaining well above the threshold for statistical significance (p < 0.05). These findings indicate that, regardless of smoking status (non-smoker, smoker, e-cigarette user), there is no statistically significant difference in ANS indicators under resting conditions among groups (Table 2 ). In the multivariate model, a significant decrease was observed in all HRV indices with increasing age (SDNN: −0.72 ms per year; RMSSD: −0.86 ms per year; pNN50: −0.60% per year; TINN: −3.79 ms per year; all p ≤ 0.01). However, no statistically significant differences were found in HRV indices or serum cotinine levels between conventional cigarette smokers, e-cigarette users, and non-smokers (all p > 0.3). The effect of sex was significant particularly for SDNN (+ 7.02 ms; p = 0.049) and TINN (+ 61.31 ms; p = 0.015), with higher values observed in males for these two indices. Additionally, serum cotinine levels were found to be lower in males compared to females (p = 0.041) (Table 3 ). Table 1 Demographic characteristics, blood pressures and cotinine levels of the study participants by smoking status. Variable Non-smoker (N = 30) Smokers (N = 30) E-cigarette (N = 30) Total (N = 90) p value Age (years) 30.0 [24.0–37.0] 25.0 [22.0–32.8] 24.5 [22.0–39.0] 26.0 [22.0–36.0] 0.452 Height (cm) 168.0 [161.0–173.0] 166.0 [162.0–172.0] 170.0 [164.0–175.8] 168.0 [162.0–173.0] 0.404 Weight (kg) 70.0 [60.0–78.0] 63.5 [56.5–75.0] 66.0 [55.2–78.0] 66.0 [56.0–78.0] 0.584 BMI (kg/m²) 24.2 [22.4–25.7] 22.4 [20.9–26.3] 22.7 [20.7–25.4] 23.1 [21.0–25.8] 0.351 Female, N (%) 18 (60%) 21 (70%) 19 (63%) 58 (65%) 0.712 Male, N (%) 12 (40%) 9 (30%) 11 (37%) 32 (35%) Smoking (years) - 5.0 [3.0–9.5] 3.0 [1.8–4.5] – 0.027 SBP (mmHg) 110.0 [100.0–127.5] 110.0 [100.0–120.0] 110.0 [ 90.0–110.0] 110.0 [100.0–120.0] 0.037 DBP (mmHg) 70.0 [65.0– 80.0] 70.0 [70.0– 80.0] 70.0 [60.0– 75.0] 70.0 [60.0– 80.0] 0.073 MBP, (mmHg) 87.5 [77.5– 95.8] 86.7 [80.0– 93.3] 80.0 [70.0– 90.0] 83.3 [73.3– 93.3] 0.021 Mean serum Cotinine ± SD (ng/mL) 40.93 ± 27.71 44.73 ± 26.53 35.67 ± 27.57 - 0.118 Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; MBP, mean blood pressure; N, number; SBP, systolic blood pressure, SD; standart deviation. Table 2 The Relationship Between Heart Rate, HRV Parameters and Frequency-Domain HRV measures by Smoking Status. Parameter Non-smoker (N = 30) Smoker (N = 30) E-cigarette (N = 30) p-value HR (bpm) 83.4 [76.3–87.5] 82.2 [77.4–91.9] 80.8 [76.7–91.5] 0.923 RR (ms) 719.8 [686.1–786.0] 730.1 [652.5–774.8] 742.8 [656.1–781.8] 0.923 SDNN (ms) 42.2 [29.3–55.9] 39.0 [31.9–50.2] 45.7 [31.6–56.6] 0.676 RMSSD (ms) 29.6 [19.1–46.1] 27.4 [22.0–37.4] 33.9 [26.9–49.8] 0.533 pNN50 (%) 7.0 [1.8–18.8] 5.9 [3.2–13.7] 10.4 [3.6–18.6] 0.844 TINN (ms) 187.5 [150.0–250.0] 180.0 [142.5–235.0] 192.5 [155.0–257.5] 0.789 VLFpow_FFT (log) 4.8 [4.3–5.2] 4.8 [4.3–5.3] 4.7 [4.1–5.3] 0.387 LFpow_FFT (log) 6.8 [6.2–7.4] 6.8 [6.2–7.4] 6.8 [6.1–7.3] 0.948 HFpow_FFT (log) 5.7 [4.8–6.4] 5.7 [5.2–6.4] 5.8 [4.9–6.5] 0.830 LF/HF ratio (FFT) 3.8 [2.2–5.4] 4.0 [2.5–5.5] 3.9 [2.3–6.0] 0.939 TOTpow_FFT (ms2) 1930.0 [1100.0–2900.0] 1850.0 [1150.0–2750.0] 1820.0 [950.0–2800.0] 0.977 Abbreviations: bpm, beats per minute; FFT, fast Fourier transform; HR, heart rate; HRV, heart rate variability; IQR, interquartile range; ms, milliseconds. Table 3 Multivariable linear regression analysis of the association between smoking status, age, sex and BMI with heart-rate variability indices and serum cotinine levels in adults. Variable Independent Predictors Beta, 95% CI P SDNN Non-smoker (ref) Smoker E-cigarette Age Sex BMI - -3.63 (-11.45-4.19) 0.47 (-7.34-8.28) -0.72 (-1.12-0.33) 7.02 (0.002–14.01) 0.15 (-0.872-1.17) - 0.359 0.905 0.0004 0.049 0.767 RMSSD Non-smoker (ref) Smoker E-cigarette Age Sex - -2.90 (-13.29-7.50) 4.38 (-5.94-14.69) -0.86 (-1.38-0.35) 3.59 (-5.23-12.41) - 0.581 0.401 0.001 0.420 pNN50 Non-smoker (ref) Smoker E-cigarette Age Sex - -1.21 (-7.20-4.78) 0.31 (-5.63-6.25) -0.60(-0.90-0.31) 2.73(-2.34-7.81) - 0.689 0.918 0.0001 0.287 TINN Non-smoker (ref) Smoker E-cigarette Age Sex - -25.86(-83.92-2.20) 16.39(-41.21-74.00) –3.79 (–6.65–0.92) 61.31(12.07– 10.56) - 0.378 0.573 0.010 0.015 cotinine Non-smoker (ref) Smoker E-cigarette Age Sex - 1.71 (-12.41-15.83) -6.53(-20.54-7.48) 0.01 (–0.68– 0.71) –12.51(–24.49–0.54) - 0.810 0.357 0.974 0.041 Abbreviations: CI, confidence interval; BMI, body mass index. Discussion This study investigated the effects of smoking status—specifically conventional tobacco, EC use, and non-smoking—on HRV, hemodynamic parameters, and serum cotinine levels in a sample of healthy adults. The findings provide several insights into the cardiovascular and autonomic effects of smoking and vaping, as well as the potential influence of demographic variables such as age and sex. The findings of the study demonstrated some interesting and controversial results regarding autonomic responses among EC users, tobacco smokers and non-smokers. Herein we aimed to show the probable chronic effects of several smoking modalities on HRV after 12 hours of cessation. Despite clear differences in smoking duration between traditional cigarette smokers and e-cigarette users, no significant differences were found in key demographic and anthropometric characteristics among the three groups, including age, sex distribution, height, weight, and BMI. This balanced distribution strengthens the internal validity of the study and supports a more accurate attribution of observed physiological differences to smoking status rather than confounding variables. Interestingly, the non-smoker group exhibited significantly higher systolic blood pressure (SBP) and mean blood pressure (MBP) values compared to the other groups. Although diastolic blood pressure (DBP) did not reach statistical significance, the trend toward higher values in non-smokers necessitates further investigation. In the previous literature, it was reported that acute elevation in blood pressure was common in smokers due to nicotine-induced sympathetic activation and vasoconstriction ( 14 ). One possible explanation is that acute nicotine tolerance in habitual smokers may lead to blunted hemodynamic responses at rest, whereas non-smokers maintain a baseline sympathetic tone that appears comparatively higher ( 15 ). Another explanation is the study measurements may not reflect the usual blood pressures of the individuals including smokers ( 16 ). In the study by Primatesta P. et al, they did not find any independent differences of clinical significance in BP values between smokers and nonsmokers. They reported that BP differences associated with smoking differed with age and between men and women and underlined the confounding effects of BMI and alcohol intake ( 16 ). Serum cotinine levels—a biomarker of nicotine exposure—were not significantly different among groups, which may reflect variability in individual metabolism or timing of last nicotine use ( 17 ). Nevertheless, cotinine levels were significantly lower in males compared to females, in consistency with the previous evidence suggesting sex-based differences in nicotine metabolism, possibly mediated by hormonal factors such as estrogen ( 18 , 19 ). However, interestingly, higher levels of cotinine in non smokers may indicate second hand smoke exposure or raise the question of misinformation by the study individuals as hidden smokers ( 17 , 20 ). The absence of significant differences in HRV parameters across smoking groups suggests that under resting conditions, ANS function—as assessed through both time-domain (e.g., SDNN, RMSSD, pNN50) and frequency-domain (e.g., LF, HF, LF/HF ratio) indices—is not substantially altered by either conventional or electronic cigarette use. This is consistent with some previous reports indicating that habitual smokers may develop adaptive mechanisms that mask autonomic dysregulation at rest, with changes becoming more apparent under stress or during recovery phases ( 21 – 23 ). The similar HRV findings with non smokers in our study may indicate that even acute cessation of either TC or EC improves autonomic balance and HRV. Our multivariate analysis revealed an inverse association between age and HRV indices, confirming well-documented age-related declines in autonomic flexibility ( 24 , 25 ). Each year of increased age was associated with significant reductions in SDNN, RMSSD, pNN50, and TINN, underscoring age as a major determinant of cardiac autonomic tone. Sex differences were also evident, with males demonstrating higher values in SDNN and TINN. These findings are consistent with prior studies showing that males may exhibit greater overall HRV, potentially due to differences in parasympathetic and sympathetic balance or cardiovascular conditioning ( 26 , 27 ). The higher HRV indices in males may reflect a more adaptive autonomic profile, although the clinical implications of these differences remain to be clarified ( 26 ). Although this study did not find significant differences in HRV across smoking groups and non smokers, the results do not preclude the potential for long-term autonomic dysfunction related to chronic smoking ( 28 ). The relatively short smoking duration among e-cigarette users (median 3.0 years) compared to traditional smokers (median 5.0 years) could partially account for the lack of observed differences. It is possible that longer exposure durations or dynamic assessments (e.g., during exercise or postural changes) may reveal more pronounced group disparities ( 28 ). Additionally, the elevated BP in non-smokers observed in this cohort highlights the complexity of cardiovascular regulation and suggests the need for further research into baseline autonomic tone and environmental or psychological stressors that may influence these parameters in non-smokers. Conclusion Future research should employ longitudinal designs, larger sample sizes, and integrate additional physiological and biochemical markers (e.g., inflammatory cytokines, endothelial function) to better elucidate the long-term effects of nicotine products, including emerging alternatives like heated tobacco and nicotine pouches, on autonomic and cardiovascular health. Study limitations There are several limitations in our study. Being a single center study with relatively lower number of patients is the major limitation of this study. Time to HRV analysis may have some effect on the results that was determined from previous studies. The relatively shorter period of EC use of the patients is another limitation. Abbreviations ANOVA: Analysis of Variance ANS: autonomic nervous system BMI: Body mass index DBP: diastolic blood pressure EC: electronic cigarettes FFT: fast Fourier transform HF: high frequency HRV: heart rate variability LF: low frequency MBP: mean blood pressure pNN50: proportion of consecutive RR intervals that is different by more than 50 ms RMSSD: root mean square of successive differences SBP: systolic blood pressure SDNN: standard deviation of normal-to-normal intervals TC: tobacco cigarettes TINN: triangular interpolation of normal-to-normal intervals Declarations Ethics approval and consent to participate This study was approved by the Human Research Ethics Committee of the Istinye University Medical Faculty on 18 October 2024, protocol number: 24-169. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to the inclusion of sensitive patient information and institutional confidentiality policies, but are available from the corresponding author on reasonable request. Competing interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Clinical Trial Number Not applicable. Author Contributions All authors contributed to: (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and (3) final approval of the version to be published. Acknowledgements The authors would like to thank all who supported this research. 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Association between smoking and blood pressure: evidence from the health survey for England. Hypertension. 2001;37(2):187 – 93. 10.1161/01.hyp.37.2.187 . PMID: 11230269. Florescu A, Ferrence R, Einarson T, Selby P, Soldin O, Koren G. Methods for quantification of exposure to cigarette smoking and environmental tobacco smoke: focus on developmental toxicology. Ther Drug Monit. 2009;31(1):14–30. 10.1097/FTD.0b013e3181957a3b . PMID: 19125149; PMCID: PMC3644554. Benowitz NL, Jacob P. Metabolism of nicotine to cotinine studied by a dual stable isotope method. Clin Pharmacol Ther. 1994;56(5):483–93. Pérez-Stable EJ, Herrera B, Jacob P, Benowitz NL. Nicotine metabolism and intake in black and white smokers. JAMA. 1998;280(2):152–6. Kim S. Overview of Cotinine Cutoff Values for Smoking Status Classification. Int J Environ Res Public Health. 2016;13:1236. https://doi.org/10.3390/ijerph13121236 . Minami J, Ishimitsu T, Matsuoka H. Effects of smoking cessation on blood pressure and heart rate variability in habitual smokers. Hypertension. 1999;33(1 pt 2):586–90. Thayer JF, Åhs F, Fredrikson M, Sollers JJ, Wager TD. A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neurosci Biobehavioral Reviews. 2010;33(2):81–8. Munjal S, Koval T, Muhammad R, Jin Y, Demmel V, Roethig HJ, Mendes P, Unverdorben M. Heart rate variability increases with reductions in cigarette smoke exposure after 3 days. J Cardiovasc Pharmacol Ther. 2009;14(3):192–8. 10.1177/1074248409340340 . Epub 2009 Jul 10. PMID: 19592602. Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: Relations to age and gender over nine decades. J Am Coll Cardiol. 1998;31(3):593–601. Zhang J. Effect of age and sex on heart rate variability in healthy subjects. J Manip Physiol Ther. 2007;30(5):374–9. Koenig J, Thayer JF. Sex differences in healthy human heart rate variability: A meta-analysis. Neurosci Biobehav Rev. 2016; 64:288–310. 10.1016/j.neubiorev.2016.03.007 . Epub 2016 Mar 7. PMID: 26964804. Huikuri HV, Pikkujämsä SM, Airaksinen KE, Ikäheimo MJ, Rantala AO, Kauma H, Lilja M, Kesäniemi YA. Sex-related differences in autonomic modulation of heart rate in middle-aged subjects. Circulation. 1996;94(2):122-5. 10.1161/01.cir.94.2.122 . PMID: 8674168. Makhoul N, Avivi I, Barak Lanciano S, Haber Kaptsenel E, Bishara H, Palacci H, Chaiat C, Jacob G, Nussinovitch U. Effects of Cigarette Smoking on Cardiac Autonomic Responses: A Cross-Sectional Study. Int J Environ Res Public Health. 2020;17:8571. https://doi.org/10.3390/ijerph17228571 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7115478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501270883,"identity":"73f9d12c-0854-458f-9b50-f6177dce6add","order_by":0,"name":"Volkan Çamkıran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFAC5oaPDQwMdvzsQJLBwIIYLYyNM4GKkyV7DoC0SBCvhXHDjAQQjwgtBscbGxtn1NxhNpB8fnXDjwIJBv727gT8Ws4cbGzccOwZn7l0TtnNHqDDJM6c3YBXi9mNxPaHD9gOM1vOzkm7wQPUYiCRS1BLY+ODf4cZN9w8k3bzD9FaNrYBtdxgP3abKFvsQX6Z2XcYGMg5bLdlDCR4CPpFsr35YGPPt8PAqDz+7OabPzZy/O29+LUgAR4DMEmschBgf0CK6lEwCkbBKBhBAAD7R1LL8ydJ3wAAAABJRU5ErkJggg==","orcid":"","institution":"Istinye University","correspondingAuthor":true,"prefix":"","firstName":"Volkan","middleName":"","lastName":"Çamkıran","suffix":""},{"id":501270884,"identity":"2e3cc241-24e2-4e48-95c9-2a40e69a22d3","order_by":1,"name":"Batool Achmar","email":"","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":false,"prefix":"","firstName":"Batool","middleName":"","lastName":"Achmar","suffix":""},{"id":501270887,"identity":"b4c16ec5-7c82-43b2-b576-3972b6077877","order_by":2,"name":"Ahmad Achmar","email":"","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Achmar","suffix":""},{"id":501270888,"identity":"5fe66dfa-2529-42e7-ba2f-210f2588e5cd","order_by":3,"name":"İlhan Kutlu","email":"","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":false,"prefix":"","firstName":"İlhan","middleName":"","lastName":"Kutlu","suffix":""},{"id":501270889,"identity":"320bd050-cb5f-42ab-969d-29c621dae1ea","order_by":4,"name":"Emel kurul","email":"","orcid":"","institution":"Yıldız Technical University","correspondingAuthor":false,"prefix":"","firstName":"Emel","middleName":"","lastName":"kurul","suffix":""},{"id":501270890,"identity":"513dba10-70bb-4a17-8851-e355b696596b","order_by":5,"name":"Özlem Unay Demirel","email":"","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":false,"prefix":"","firstName":"Özlem","middleName":"Unay","lastName":"Demirel","suffix":""},{"id":501270891,"identity":"35d86e80-1357-4836-8492-4f56d7fc1466","order_by":6,"name":"Hüseyin Tunç","email":"","orcid":"","institution":"Bahçeşehir University","correspondingAuthor":false,"prefix":"","firstName":"Hüseyin","middleName":"","lastName":"Tunç","suffix":""},{"id":501270892,"identity":"a2783cf4-0542-4293-81af-d2d043aeaed0","order_by":7,"name":"Emrah İpek","email":"","orcid":"","institution":"Nişantaşı University","correspondingAuthor":false,"prefix":"","firstName":"Emrah","middleName":"","lastName":"İpek","suffix":""}],"badges":[],"createdAt":"2025-07-13 21:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7115478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7115478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104783163,"identity":"35e7de1c-d428-425a-b60e-851b9c7eac87","added_by":"auto","created_at":"2026-03-17 07:58:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":730160,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7115478/v1/bcac5b77-fb7f-47b2-b249-382486ee666f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of E-Cigarettes \u0026 Tobacco on Heart Rate Variability Parameters, A Case-Control Study. Which One is Worse? ","fulltext":[{"header":"Background","content":"\u003cp\u003eSmoking is a well-established public health problem leading to disabilities and death throughout the globe. It has a crucial role in major preventable diseases including atherosclerotic cardiovascular disease, stroke and cancer. Despite increased awareness regarding its harms, it continues to contribute to impaired health status globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In recent years, the prevalence of smoking tends to decrease worldwide however a certain period is needed to observe the health benefits of smoking cessation especially in chronic disorders (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Nevertheless, electronic cigarettes (EC) have been gaining popularity, particularly among younger individuals (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). There are several reports and ongoing research that EC can be as harmful as tobacco cigarettes (TC) despite previous conflicting results from some studies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Although EC may be helpful to quit TC smoking, it was shown to increase the risk of tobacco smoking especially in previously non-smokers and younger individuals (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Besides data regarding acute adverse effects of ECs, several pathophysiologic mechanisms have been linked to increased cardiovascular disease risk including oxidative stress and inflammation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Heart rate variability (HRV), as a non-invasive applicable tool for the diagnosis of autonomic dysfunction, measures the variation of consecutive R-R intervals on electrocardiogram (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). HRV indicates the balance between sympathetic and parasympathetic tones and is accepted as a biomarker for general health status. For an optimally functioning autonomic nervous system (ANS), higher HRV is needed which shows an increased ability of the heart to effectively adapt to prompt ANS changes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In this case control study, we aimed to reveal differences in HRV parameters among tobacco smokers, e-cigarette users and non-smoker healthy individuals.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy Population and design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 90 healthy volunteers (32 males, 58 females) between the ages of 18\u0026ndash;45 were included in the study. The study individuals were selected among university students and hospital employees including health care providers and non-clinical services staff. Participants were selected randomly using convenience sampling from the dataset of individuals who met the inclusion criteria. Thirty individuals per group (non-smokers, traditional cigarette smokers and e-cigarette users) were included to ensure equal group sizes for comparison. The demographic and lifestyle information of the individuals were collected at the beginning of the study. This basic information included the age, height in meters, weight in kilograms, smoking status including duration and daily number of conventional and/or electronic cigarettes, brand names, nicotine concentration and aroma preferences. The data regarding health status includes the presence of any chronic illness, pregnancy, medications including vitamin supplements and nicotine replacement therapy, alcohol use and drug abuse. Body mass index (BMI) was calculated by the formula: weight in kilograms/square of height in meters. Obese and underweight individuals (BMI equals or higher than 30 kg/m\u0026sup2; and equals or lower than 18 kg/m\u0026sup2;, respectively), pregnant women, individuals with any chronic or acute illness including asthma, hypertension, heart disease, diabetes, hyperlipidemia, chronic kidney disease, and active infection or cancer, the ones who consume illicit drugs and 2 or more alcoholic drinks/day, prescription drug users (except oral contraceptives) including licensed nicotine replacement therapies, and athletes were excluded. The healthy individuals fulfilling the study criteria were divided into 3 groups. The first group was explicit TC smokers for at least 6 months. The second group was explicit electronic cigarette smokers for at least 6 months. The third group was \u0026ldquo;control group\u0026rdquo; including nonsmokers that are not exposed to cigarette smoke for more than 2 hours daily for at least 6 months. Each study group included 30 self-identified e-cigarette smokers, 30 self-identified TC smokers and 30 self-identified nonsmoker control group. To reveal the chronic or non-acute effects and to prevent interference of acute withdrawal of electronic or TC, subjects were asked to refrain from e-cigarettes and smoking tobacco for twelve hours prior to the study. Alcohol, caffeine and nicotine intake are also restricted. The systolic and diastolic blood pressures were measured in both arms by a single study nurse by using the one calibrated sphygmomanometer and the greater arm values were recorded for everyone. The blood samples were taken to measure serum cotinine levels to show tobacco exposure in the study population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBlood test\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHeparinized tubes were used to collect blood samples from the patients, which were carefully done by a professional nurse in the study hospital. The blood samples were drawn 10 minutes after the HRV measurements. The left antecubital vein was used to collect blood in all patients. The blood sample was centrifuged, and the separated serum was stored at -40\u0026deg; C for cotinine analysis. Thermo Scientific DRI Cotinine Assay was utilized to measure cotinine levels by an analyzer (ARCHITECT c16000 clinical chemistry analyzer; Abbott Laboratories, Abbott Park, IL, USA).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHRV Measurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe HRV measurements were performed in the supine position in a quiet hospital room with a controlled temperature of 21 degrees Celsius (C). During measurements, no mobile phones or other digital devices were permitted, and unnecessary speech was prohibited during data collection. The HRV test was carried out after 15 minutes of rest in the supine position. To avoid the influence of circadian rhythm on autonomic tone, participants were studied between 9 a.m. and 2 p.m. The Polar H10 heart rate monitor (Polar Electro Oy, Kempele, Finland) was used to assess resting HRV data. The participants wore the chest strap sensor during the entire 7-minute recording session. The Polar H10 was linked via Bluetooth to the Kubios HRV mobile application (Kubios Oy, Kuopio, Finland), which enabled data collection in real time. The beat-to-beat R-R interval was recorded using the Kubios application which subsequently provided accurate HRV readings. After completing the recording, the data was exported in an appropriate format to undertake further studies by the Kubios HRV software. This approach is in accordance with standard methods of HRV data collection and analysis as provided in the Kubios HRV Scientific User's Guide (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The time and frequency domain HRV parameters were analyzed in this study. The time-domain HRV parameters including the standard deviation of normal-to-normal intervals (SDNN) (shows the overall variation within the RR interval series), the root mean square of successive differences (RMSSD), and the proportion of consecutive RR intervals that is different by more than 50 ms (pNN50) (shows parasympathetic cardiac modulation) and triangular interpolation of normal-to-normal intervals (TINN) (This is baseline width of the distribution that approximates the normal to normal (NN) interval distribution.) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The frequency domain analysis which converts signals into frequency bands by fast Fourier transform (FFT) is represented by very low frequency (VLF) (0.003\u0026ndash;0.04 Hz) (represents sympatho-vagal balance) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), low frequency (LF) (0.04\u0026ndash;0.15 Hz) (shows both sympathetic and vagal tone) and high frequency (HF) (0.15\u0026ndash;0.40 Hz) (shows parasympathetic modulation) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The LF/HF ratio indicates a balance between sympathetic and parasympathetic tone. A low LF/HF ratio means parasympathetic, but a high LF/HF ratio shows sympathetic dominance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Total power (TOTpow_FFT) shows the total variance in heart rate pattern during recording and is reported as an average of every 5 min (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e The study was approved by the local ethics committee with the protocol number: 24\u0026ndash;169, and written informed consent was obtained from each study participant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses were performed in Python 3.8 using the scikit-learn, pandas and SciPy packages. Continuous variables are summarized as median/ interquartile range (IQR), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical variables as counts and percentages. For comparison of categorical variables across groups and any expected cell count\u0026thinsp;\u0026lt;\u0026thinsp;5, then Fisher's exact test is used. To compare non-smokers, smokers and e-cigarette users, we first assessed normality with the Shapiro\u0026ndash;Wilk test. The non-normal variables were compared using the Kruskal\u0026ndash;Wallis's test (with Dunn's multiple comparisons). One-way ANOVA (Analysis of Variance) was used to compare the means of the groups. Multivariate linear regression analysis was performed to search for the association between smoking status, age, sex and BMI with heart-rate variability indices and serum cotinine levels. Beta and 95% confidence intervals were utilized to search for the relationship between determinants and smoking groups. A critical p-value of 0.05 was chosen, and contingency tables yielding p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate statistically significant differences between classification approaches.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis dataset comprises 90 participants, divided into three equal groups of 30 each: non-smokers (N), traditional cigarette smokers (T), and electronic cigarette users (E). It includes 114 variables in total. The demographic continuous variables are gender, age, height, weight, BMI and smoking duration. There were 58 females (64%), and 32 (%36) males in the total study group. There was no significant difference between study groups in terms of basic demographic characteristics. In our data, traditional smokers reported a median smoking duration of 5.0 [3.0\u0026ndash;9.5] years, while e-cigarette users had a shorter history averaging 3.0 [1.8\u0026ndash;4.5] years; non-smokers, by definition, had no prior smoking exposure. Smoking duration was significantly higher in tobacco smokers than the e-cigarette users (p\u0026thinsp;=\u0026thinsp;0.027). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates the key demographic characteristics, blood pressure and cotinine data stratified by smoking status. Data are presented as median (IQR), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%). There were no statistically significant differences in the median [IQR] values of age, height, weight, and BMI among the three groups (p-values ranging from 0.351 to 0.584). Similarly, the distribution of sex was balanced across groups (p\u0026thinsp;=\u0026thinsp;0.712). However, the duration of prior use differed significantly between the \"Smoker\" and \"E-cigarette\" groups (p\u0026thinsp;=\u0026thinsp;0.027), indicating distinct levels of exposure across these groups. Statistically significant differences between groups were observed for baseline SBP and MBP (p\u0026thinsp;=\u0026thinsp;0.037 and p\u0026thinsp;=\u0026thinsp;0.021, respectively) with significantly higher SBP and MBP values in the non-smoker group. For DBP, the difference approached significance however remained borderline with p\u0026thinsp;=\u0026thinsp;0.073. Serum cotinine levels were also similar among groups (p\u0026thinsp;=\u0026thinsp;0.118). In summary, while demographic and basic anthropometric characteristics were balanced across groups, some differences were observed in smoking exposure and certain hemodynamic parameters. In the comparison of median heart rate (HR) and HRV parameters by smoking status, there was not any significant difference between study groups. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the findings regarding HR and HRV parameters by smoking status. The median [IQR] values of both time-domain (Mean HR, Mean RR, SDNN, RMSSD, pNN50, TINN) and frequency-domain (VLF, LF, HF power, LF/HF ratio, total power) parameters are highly similar across the three groups. All Kruskal\u0026ndash;Wallis p-values ranged between 0.387 and 0.977, remaining well above the threshold for statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings indicate that, regardless of smoking status (non-smoker, smoker, e-cigarette user), there is no statistically significant difference in ANS indicators under resting conditions among groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the multivariate model, a significant decrease was observed in all HRV indices with increasing age (SDNN: \u0026minus;0.72 ms per year; RMSSD: \u0026minus;0.86 ms per year; pNN50: \u0026minus;0.60% per year; TINN: \u0026minus;3.79 ms per year; all p\u0026thinsp;\u0026le;\u0026thinsp;0.01). However, no statistically significant differences were found in HRV indices or serum cotinine levels between conventional cigarette smokers, e-cigarette users, and non-smokers (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.3). The effect of sex was significant particularly for SDNN (+\u0026thinsp;7.02 ms; p\u0026thinsp;=\u0026thinsp;0.049) and TINN (+\u0026thinsp;61.31 ms; p\u0026thinsp;=\u0026thinsp;0.015), with higher values observed in males for these two indices. Additionally, serum cotinine levels were found to be lower in males compared to females (p\u0026thinsp;=\u0026thinsp;0.041) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics, blood pressures and cotinine levels of the study participants by smoking status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmokers\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.0 [24.0\u0026ndash;37.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.0 [22.0\u0026ndash;32.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.5 [22.0\u0026ndash;39.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.0 [22.0\u0026ndash;36.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168.0 [161.0\u0026ndash;173.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166.0 [162.0\u0026ndash;172.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170.0 [164.0\u0026ndash;175.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e168.0 [162.0\u0026ndash;173.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.0 [60.0\u0026ndash;78.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.5 [56.5\u0026ndash;75.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.0 [55.2\u0026ndash;78.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.0 [56.0\u0026ndash;78.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.2 [22.4\u0026ndash;25.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.4 [20.9\u0026ndash;26.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.7 [20.7\u0026ndash;25.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.1 [21.0\u0026ndash;25.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58 (65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (35%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0 [3.0\u0026ndash;9.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0 [1.8\u0026ndash;4.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.0 [100.0\u0026ndash;127.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110.0 [100.0\u0026ndash;120.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.0 [ 90.0\u0026ndash;110.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110.0 [100.0\u0026ndash;120.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.0 [65.0\u0026ndash; 80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.0 [70.0\u0026ndash; 80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.0 [60.0\u0026ndash; 75.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.0 [60.0\u0026ndash; 80.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBP, (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.5 [77.5\u0026ndash; 95.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.7 [80.0\u0026ndash; 93.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.0 [70.0\u0026ndash; 90.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.3 [73.3\u0026ndash; 93.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean serum Cotinine\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.93\u0026thinsp;\u0026plusmn;\u0026thinsp;27.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.73\u0026thinsp;\u0026plusmn;\u0026thinsp;26.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.67\u0026thinsp;\u0026plusmn;\u0026thinsp;27.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: BMI, body mass index; DBP, diastolic blood pressure; MBP, mean blood pressure; N, number; SBP, systolic blood pressure, SD; standart deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Relationship Between Heart Rate, HRV Parameters and Frequency-Domain HRV measures by Smoking Status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.4 [76.3\u0026ndash;87.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.2 [77.4\u0026ndash;91.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.8 [76.7\u0026ndash;91.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRR (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e719.8 [686.1\u0026ndash;786.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e730.1 [652.5\u0026ndash;774.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e742.8 [656.1\u0026ndash;781.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.2 [29.3\u0026ndash;55.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.0 [31.9\u0026ndash;50.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.7 [31.6\u0026ndash;56.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.6 [19.1\u0026ndash;46.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.4 [22.0\u0026ndash;37.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.9 [26.9\u0026ndash;49.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epNN50 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.0 [1.8\u0026ndash;18.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.9 [3.2\u0026ndash;13.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.4 [3.6\u0026ndash;18.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTINN (ms)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e187.5 [150.0\u0026ndash;250.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180.0 [142.5\u0026ndash;235.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e192.5 [155.0\u0026ndash;257.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVLFpow_FFT (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.8 [4.3\u0026ndash;5.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.8 [4.3\u0026ndash;5.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7 [4.1\u0026ndash;5.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLFpow_FFT (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.8 [6.2\u0026ndash;7.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8 [6.2\u0026ndash;7.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.8 [6.1\u0026ndash;7.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHFpow_FFT (log)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.7 [4.8\u0026ndash;6.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.7 [5.2\u0026ndash;6.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.8 [4.9\u0026ndash;6.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLF/HF ratio (FFT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.8 [2.2\u0026ndash;5.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.0 [2.5\u0026ndash;5.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9 [2.3\u0026ndash;6.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOTpow_FFT (ms2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1930.0 [1100.0\u0026ndash;2900.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1850.0 [1150.0\u0026ndash;2750.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1820.0 [950.0\u0026ndash;2800.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: bpm, beats per minute; FFT, fast Fourier transform; HR, heart rate; HRV, heart rate variability; IQR, interquartile range; ms, milliseconds.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable linear regression analysis of the association between smoking status, age, sex and BMI with heart-rate variability indices and serum cotinine levels in adults.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndependent Predictors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeta, 95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDNN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker (ref)\u003c/p\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e-3.63 (-11.45-4.19)\u003c/p\u003e\u003cp\u003e0.47 (-7.34-8.28)\u003c/p\u003e\u003cp\u003e-0.72 (-1.12-0.33)\u003c/p\u003e\u003cp\u003e7.02 (0.002\u0026ndash;14.01)\u003c/p\u003e\u003cp\u003e0.15 (-0.872-1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e0.359\u003c/p\u003e\u003cp\u003e0.905\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.0004\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker (ref)\u003c/p\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e-2.90 (-13.29-7.50)\u003c/p\u003e\u003cp\u003e4.38 (-5.94-14.69)\u003c/p\u003e\u003cp\u003e-0.86 (-1.38-0.35)\u003c/p\u003e\u003cp\u003e3.59 (-5.23-12.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e0.581\u003c/p\u003e\u003cp\u003e0.401\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epNN50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker (ref)\u003c/p\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e-1.21 (-7.20-4.78)\u003c/p\u003e\u003cp\u003e0.31 (-5.63-6.25)\u003c/p\u003e\u003cp\u003e-0.60(-0.90-0.31)\u003c/p\u003e\u003cp\u003e2.73(-2.34-7.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e0.689\u003c/p\u003e\u003cp\u003e0.918\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTINN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker (ref)\u003c/p\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e-25.86(-83.92-2.20)\u003c/p\u003e\u003cp\u003e16.39(-41.21-74.00)\u003c/p\u003e\u003cp\u003e\u0026ndash;3.79 (\u0026ndash;6.65\u0026ndash;0.92)\u003c/p\u003e\u003cp\u003e61.31(12.07\u0026ndash; 10.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e0.378\u003c/p\u003e\u003cp\u003e0.573\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecotinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-smoker (ref)\u003c/p\u003e\u003cp\u003eSmoker\u003c/p\u003e\u003cp\u003eE-cigarette\u003c/p\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e1.71 (-12.41-15.83)\u003c/p\u003e\u003cp\u003e-6.53(-20.54-7.48)\u003c/p\u003e\u003cp\u003e0.01 (\u0026ndash;0.68\u0026ndash; 0.71)\u003c/p\u003e\u003cp\u003e\u0026ndash;12.51(\u0026ndash;24.49\u0026ndash;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003cp\u003e0.810\u003c/p\u003e\u003cp\u003e0.357\u003c/p\u003e\u003cp\u003e0.974\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: CI, confidence interval; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the effects of smoking status\u0026mdash;specifically conventional tobacco, EC use, and non-smoking\u0026mdash;on HRV, hemodynamic parameters, and serum cotinine levels in a sample of healthy adults. The findings provide several insights into the cardiovascular and autonomic effects of smoking and vaping, as well as the potential influence of demographic variables such as age and sex. The findings of the study demonstrated some interesting and controversial results regarding autonomic responses among EC users, tobacco smokers and non-smokers. Herein we aimed to show the probable chronic effects of several smoking modalities on HRV after 12 hours of cessation. Despite clear differences in smoking duration between traditional cigarette smokers and e-cigarette users, no significant differences were found in key demographic and anthropometric characteristics among the three groups, including age, sex distribution, height, weight, and BMI. This balanced distribution strengthens the internal validity of the study and supports a more accurate attribution of observed physiological differences to smoking status rather than confounding variables.\u003c/p\u003e\u003cp\u003eInterestingly, the non-smoker group exhibited significantly higher systolic blood pressure (SBP) and mean blood pressure (MBP) values compared to the other groups. Although diastolic blood pressure (DBP) did not reach statistical significance, the trend toward higher values in non-smokers necessitates further investigation. In the previous literature, it was reported that acute elevation in blood pressure was common in smokers due to nicotine-induced sympathetic activation and vasoconstriction (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). One possible explanation is that acute nicotine tolerance in habitual smokers may lead to blunted hemodynamic responses at rest, whereas non-smokers maintain a baseline sympathetic tone that appears comparatively higher (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Another explanation is the study measurements may not reflect the usual blood pressures of the individuals including smokers (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In the study by Primatesta P. et al, they did not find any independent differences of clinical significance in BP values between smokers and nonsmokers. They reported that BP differences associated with smoking differed with age and between men and women and underlined the confounding effects of BMI and alcohol intake (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSerum cotinine levels\u0026mdash;a biomarker of nicotine exposure\u0026mdash;were not significantly different among groups, which may reflect variability in individual metabolism or timing of last nicotine use (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Nevertheless, cotinine levels were significantly lower in males compared to females, in consistency with the previous evidence suggesting sex-based differences in nicotine metabolism, possibly mediated by hormonal factors such as estrogen (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, interestingly, higher levels of cotinine in non smokers may indicate second hand smoke exposure or raise the question of misinformation by the study individuals as hidden smokers (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe absence of significant differences in HRV parameters across smoking groups suggests that under resting conditions, ANS function\u0026mdash;as assessed through both time-domain (e.g., SDNN, RMSSD, pNN50) and frequency-domain (e.g., LF, HF, LF/HF ratio) indices\u0026mdash;is not substantially altered by either conventional or electronic cigarette use. This is consistent with some previous reports indicating that habitual smokers may develop adaptive mechanisms that mask autonomic dysregulation at rest, with changes becoming more apparent under stress or during recovery phases (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The similar HRV findings with non smokers in our study may indicate that even acute cessation of either TC or EC improves autonomic balance and HRV. Our multivariate analysis revealed an inverse association between age and HRV indices, confirming well-documented age-related declines in autonomic flexibility (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Each year of increased age was associated with significant reductions in SDNN, RMSSD, pNN50, and TINN, underscoring age as a major determinant of cardiac autonomic tone. Sex differences were also evident, with males demonstrating higher values in SDNN and TINN. These findings are consistent with prior studies showing that males may exhibit greater overall HRV, potentially due to differences in parasympathetic and sympathetic balance or cardiovascular conditioning (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The higher HRV indices in males may reflect a more adaptive autonomic profile, although the clinical implications of these differences remain to be clarified (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough this study did not find significant differences in HRV across smoking groups and non smokers, the results do not preclude the potential for long-term autonomic dysfunction related to chronic smoking (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The relatively short smoking duration among e-cigarette users (median 3.0 years) compared to traditional smokers (median 5.0 years) could partially account for the lack of observed differences. It is possible that longer exposure durations or dynamic assessments (e.g., during exercise or postural changes) may reveal more pronounced group disparities (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Additionally, the elevated BP in non-smokers observed in this cohort highlights the complexity of cardiovascular regulation and suggests the need for further research into baseline autonomic tone and environmental or psychological stressors that may influence these parameters in non-smokers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFuture research should employ longitudinal designs, larger sample sizes, and integrate additional physiological and biochemical markers (e.g., inflammatory cytokines, endothelial function) to better elucidate the long-term effects of nicotine products, including emerging alternatives like heated tobacco and nicotine pouches, on autonomic and cardiovascular health.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere are several limitations in our study. Being a single center study with relatively lower number of patients is the major limitation of this study. Time to HRV analysis may have some effect on the results that was determined from previous studies. The relatively shorter period of EC use of the patients is another limitation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANOVA: Analysis of Variance\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eANS: autonomic nervous system\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEC: electronic cigarettes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFFT: fast Fourier transform\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHF: high frequency\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHRV: heart rate variability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLF: low frequency\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMBP: mean blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003epNN50: proportion of consecutive RR intervals that is different by more than 50 ms\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRMSSD: root mean square of successive differences\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSDNN: standard deviation of normal-to-normal intervals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC: tobacco cigarettes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTINN: triangular interpolation of normal-to-normal intervals\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Research Ethics Committee of the Istinye University Medical Faculty on 18 October 2024, protocol number: 24-169.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to the inclusion of sensitive patient information and institutional confidentiality policies, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to: (1) substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content, and (3) final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all who supported this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRicha S, Praveen S, Albariqi AA, Abullais SS, Mahmood SE, Alsamghan A, Bharti RK, Bahamdan GK. 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Int J Environ Res Public Health. 2020;17:8571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph17228571\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17228571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heart Rate Variability, E-Cigarette, Cardiovascular Autonomic Function, Smoking","lastPublishedDoi":"10.21203/rs.3.rs-7115478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7115478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to examine how heart rate variability (HRV) parameters, hemodynamic parameters and serum cotinine levels differ across the following groups: tobacco smokers, e-cigarette users and healthy non-smokers. This study explicitly examined a group of healthy people.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNinety healthy volunteers, aged between 18 and 45, were enrolled in the study (32 men and 58 women). Participants were divided into three groups of thirty each: e-cigarette users, traditional cigarette smokers and non-smokers. To examine the amount of tobacco exposure, serum cotinine levels were measured. Additionally, HRV data were examined in both the frequency and temporal domains. Statistical analyses were performed in Python 3.8 using the scikit-learn, pandas, and SciPy packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE-cigarette users had a noticeably longer smoking history than tobacco smokers (p=0.027). The baseline systolic blood pressure (SBP) and mean blood pressure (MBP) of the groups varied considerably (p=0.037 and p=0.021, respectively), with the non-smoker group exhibiting significantly higher SBP and MBP. For diastolic blood pressure (DBP), the change was borderline (p=0.073) but nearly significant.\u003c/p\u003e\n\u003cp\u003eAll HRV variables displayed standard deviation of normal-to-normal intervals (SDNN): -0.72 ms, root mean square of successive differences (RMSSD): -0.86 ms, proportion of consecutive RR intervals that is different by more than 50 ms (pNN50): -0.60% year and triangular interpolation of normal-to-normal intervals (TINN): -3.79 ms annually as age increased (all p ≤ 0.01). However, there were no significant differences in serum cotinine levels or HRV indices between e-cigarette users, non-smokers and traditional smokers (all p \u0026gt; 0.3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no discernible variations in cotinine levels or HRV indices among traditional cigarette smokers, e-cigarette users and non-smokers. The long-term effects of nicotine products require more research, including longitudinal designs, higher cohort sizes and other physiological and biochemical inflammatory indicators, such as endothelial function markers and cytokines.\u003c/p\u003e","manuscriptTitle":"Effects of E-Cigarettes \u0026amp; Tobacco on Heart Rate Variability Parameters, A Case-Control Study. Which One is Worse? 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