Analysis of coronary angiography related psychophysiological responses

Şükrü Okkesim email@example.com
Institute of Biomedical Engineering
Fatih University
34500Buyukcekmece, Istanbul

Sadık Kara
Institute of Biomedical Engineering
Fatih University
34500Buyukcekmece, Istanbul

Mehmet G Kaya
Institute of Biomedical Engineering
Fatih University
34500Buyukcekmece, Istanbul

Musa H Asyali
Institute of Biomedical Engineering
Fatih University
34500Buyukcekmece, Istanbul

Analysis of coronary angiography related psychophysiological responses
R E S E A R C H Open Access * Correspondence: Turkey Full list of author information is available at the end of the article
Background: Coronary angiography is an important tool in diagnosis of cardiovascular diseases. However, it is the administration is relatively stressful and emotionally traumatic for the subjects. The aim of this study is to evaluate psychophysiological responses induced by the coronary angiography instead of subjective methods such as a questionnaire. We have also evaluated the influence of the tranquilizer on the psychophysiological responses. Methods: Electrocardiography (ECG), Blood Volume Pulse (BVP), and Galvanic Skin Response (GSR) of 34 patients who underwent coronary angiography operation were recorded. Recordings were done at three phases: "1 hour before," "during," and "1 hour after" the coronary angiography test. Total of 5 features obtained from the physiological signals were compared across these three phases. Sixteen of the patients were administered 5 mg of a tranquilizer (Diazepam) before the operation and remaining 18 were not. Results: Our results indicate that there is a strong correlation between features (LF/ HF, Bk, DN1/DN2, skin conductance level and seg_mean) in terms of reflecting psychophysiological responses. However only DN1/DN2 feature has statistically significant differences between angiography phases (for diazepam: p = 0.0201, for non_diazepam p = 0.0224). We also note that there are statistically significant differences between the diazepam and non-diazepam groups for seg_mean features in "before", "during" and "after" phases (p = 0.0156, 0.0282, and 0.0443, respectively).Conclusions:The most intense sympathetic activity is observed in the "during" angiography phase for both of the groups. The obtained features can be used in some clinical studies where generation of the customized/individual diagnoses styles and quantitative evaluation of psychophysiological responses is necessary. Okkesim et al. BioMedical Engineering OnLine 2011, 10:71

Background

Many diseases are associated with stress and anxiety, and it is well known that stress is a strong contributing factor in the development of some important conditions like heart and cerebrovascular diseases, hypertension, and peptic ulcer [1][2][3]. Conversely, the role of "being in a good mood" in reducing the risk of disease, shortening the treatment period, and strengthening the immune system has been shown in the literature [4]. Although psychophysiological changes have great influence on people, stress and anxiety do not have a standardized tabulation or scoring system for their quantitative or objective assessment.

Invasive tests like coronary angiography cause anxiety, fear, and stress that lead to a decrease in quality of life while awaiting the procedure and an increase in complication rates during and after the test [5][6][7][8][9].

Coronary angiography is the most common medical test for the diagnosis of cardiovascular diseases, which are the major cause of death in the developed countries and in many developing countries. Coronary angiography test is carried out on more than 1 million people in the United States in 1993 and it is estimated that this number reached 3 million in 2010 [10,11].

The risk of major complications during angiography, like perforation of the coronary vessels, low blood pressure due to severe allergic reaction, and rhythm disturbances that would require temporary cardiac pacing are rarely seen (< 2%) [10]. Other than its physical effects, angiography is also a psychologically important event and most patients find coronary angiography to be extremely stressful [6,12,13].

In order to evaluate anxiety, fear or stress during angiography, questionnaires are filled while patients were on the operation table in the catheterization laboratory. Using Visual Analogy Scale (VAS), Hospital Anxiety Depression (HAD), and State Trait Anxiety Inventory (STAI), Heikkila et al. reported that 80% of the 243 patients had anxiety because of coronary angiography [5]. Mott carried out a similar study on 30 patients using STAI test and concluded that coronary angiography causes anxiety and psychological preparation is beneficial in reducing patients' anxiety before cardiac catheterization [6]. Same findings were obtained on 60 adult patients scheduled for cardiac catheterization by Anderson and Masur [7]. Caldwell et al. investigated fears and beliefs of patients regarding cardiac catheterization. They asked questions like 'Can you tell me more about that issue/fear/concern/belief' to 20 patients and concluded that cardiac catheterization causes fear because of lack of control about physical and psychosocial aspects of the procedure, unknown test results, and possible medical complications [8]. De-Jong-Watt and Arthur investigated the impact of waiting for first-time coronary angiography (CA) on patients' anxiety and health-related quality of life. They used Seattle Angina Questionnaire (SAQ) and a direct-estimation, 4-point Likert scale to measure health-related quality of life and perceived anxiety. According to their results, waiting for CA may have a negative impact on anxiety level and health-related quality of life [9].

These studies showed that invasive tests like coronary angiography cause fear, stress, and anxiety. However, throughout these studies mostly questionnaires were used to measure fear, stress, or anxiety, and therefore only subjective or individual-dependent results were obtained. In some cases, subjects may not identify and/or express their own feelings or they may not want to share their private information with others [14,15]. Based on finding of these studies, further studies were carried out to investigate how fear, stress or anxiety could be reduced on patients who undergo an invasive test in the hospital. They used guided imagery techniques, music and progressive muscle relaxation to reduce stres and anxiety [6, 13 and 15]. On the other hand diazepam is the accepted method in the clinical world to deescalate anxiety and used widely [16]. If the tranquilizer effect of the diazepam can be evaluated using physiological responses, the effectiveness of the diazepam and other methods (guided imagery techniques, music etc.) can be compare and discuss quantitatively.

The above literature survey reveals that the investigation of psychophysiological responses due to coronary angiography has not been dealt with objective methods and only subjective or individual-dependent results can be obtained from those studies.

Therefore, in this study we want to investigate psychophysiological responses to coronary angiography (CA) to bring out whether psychological changes originated from CA can be evaluated using measurable values e.g. physiological responses. More specifically, we would like to use physiological signals and their appropriate features to measure and evaluate angiography related psychophysiological responses in an objective manner. One of these benefits will be the quantitative assessment of the psychological conditions of the patients who will undergo invasive operations. With the help such knowledge, invasive operations will be performed more effectively, the health of the patients will be risked less, customized treatment styles may be performed in the future and new methods may be developed in the diagnosis and treatment of depressive disorders like depression that have direct influence on human health.

The studies conducted since Canon [17], who introduced the fight-flight concept in his study, have demonstrated that when a stress source is encountered, Sympathetic Nervous System (SNS) is stimulated and consequently changes occur in the physiological parameters controlled by the SNS. In our study, two different groups were gathered: One group was administered with diazepam and the other was not. Diazepam is a commonly used drug for treating anxiety, insomnia and muscle spasms. It is also used before some painful medical procedures to deescalate anxiety, and in some surgical operation to cause amnesia [16]. In this way, we considered that differences between the non-diazepam group and the diazepam group in which SNS was suppressed via diazepam may be evaluated statistically.

As physiological signals reflecting psychophysiological responses, we have recorded Electrocardiography (ECG), Blood Volume Pulse (BVP), and Galvanic Skin Response (GSR) from 34 patients who have undergone coronary angiography.

Sympathetic and parasympathetic nervous systems control the heart rate. SNS tends to increase heart rate and its response is slow. Parasympathetic nervous system (PNS), on the other hand, tends to decrease heart rate and mediates faster [18,19]. Therefore, Heart Rate Variability (HRV) could be used in quantitative assessment of stress and anxiety. Frequency domain analysis of HRV was performed and the Power Spectral Density (PSD) for low frequency (LF: 0.04-0.15 Hz) and high frequency (HF: 0.15-0.40 Hz) bands were computed. The HF and LF components of the HRV are controlled by PNS and SNS respectively [18,19]. Thus the LF/HF ratio gives an index of autonomic balance between sympathetic and parasympathetic activities.

The vasomotor activity, controlling the wall width of the veins is controlled by SNS. Therefore, the alterations in the BVP signal amplitude gives information about the stimulation of SNS. An increase in the BVP signal amplitude shows that there is a decrease in SNS stimulation, which leads to widening of vein walls and more blood circulation.

The moment the left ventricle pumps blood to the body, the BVP signal amplitude is at the maximum. In some cases, a second visible increase may occur in the descending margin of this signal. The notch, formed by the closing of the valves belonging to the aorta is called "Dicrotic Notch". When a stimulus that would increase heart beat pressure is present, left ventricular contraction pressure overcomes the negative effect formed due to the aorta valves and dicrotic notch cannot be seen in the BVP signal [20]. Correspondingly, the power of the second harmonic, which corresponds to the dicrotic notch, decreases in the frequency spectrum [21].

The activity of sweat glands is also controlled by SNS. When a person is scared or stimulated by an anxiety rising stimulus, the activity of the sweat glands increase till the glands reach saturation and a rapid increase in the skin conductivity or GSR occurs. Therefore, the change seen in the skin conductivity reflects the change in the stimulation level of SNS [22,14].

The recordings were repeated at three phases or stages: "1 hour before," "during," and "1 hour after" the coronary angiography test. The recorded signals were then processed for feature extraction and obtained features were analyzed statistically across groups (diazepam, non-diazepam) and different angiography phases.

Methods

Measurement of Physiological Signals

ECG, BVP, and GSR signals were recorded from 34 patients consisting of 22 males and 12 females (Mean Age: 59.6; Standard Deviation: 11.5). These patients were randomly selected among the admitted patients for CA at Cardiology Center of Erciyes University Hospital, Kayseri, Turkey in December 2008. There were no age or gender limitations. The medical part of the study was designed by a cardiologist in our research team. In that design, patients were assigned to the diazepam or non-diazepam groups randomly. All the patients have undergone coronary angiography operation at the catheter laboratory of the Cardiology Center of Erciyes University. Prior to the study, informed consent of the patients and approval of the Erciyes University Ethical Committee was obtained. The ECG, BVP, and GSR recordings were done at three phases as "1 hour before," "during," and "1 hour after" the angiography test for a duration of at least 5 minutes. Sixteen of the patients were administered 5 mg of diazepam just before the angiography. Except for the diazepam, they were not administered any other medications that may affect the ANS. The MP150 biomedical data acquisition unit (Biopac Systems, Goleta, CA, USA) was used for recording signals. All the signals were sampled at 1000 Hz and digitized (A/D converted) at a resolution of 12 bits per sample by the MP150 unit.

The ECG is one of the best known biomedical signals in psychophysiological research and serves as the most accurate measure of cardiac function. As such, it is the basis for many studies including Heart Rate Variability (HRV) analysis [18]. The ECG100C amplifier module of the MP150 unit was used to amplify and filter with the following settings: 35 Hz Low-Pass (LP) filter, 0.05 Hz High-Pass (HP) filter, and 500 gain. In order to reduce electrode impedance, skin surface was abraded and electrode gel was applied before electrode placement.

Photo-plethysmograph is a method for detecting blood volume in peripheral vessels. A Light Emitting Diode (LED) placed at a fingertip sends an infrared light beam and a photo-detector placed next to the LED receives reflected light from the tissues underneath. With each contraction of the heart, blood is pumped to the vascular system so that the vessels including the capillaries under the LED are filled with blood, and the amount of light reaching photo-detector changes. The resultant nearly periodic signal is referred to as the BVP signal [23]. BVP signals were recorded using PPG 100C amplifier and TSD200 transducer modules of the MP150 unit. The probe of the TSD200 module, which operates on 860 ± 60 nm wave length, was placed on the ring finger. The module filter settings were as 3 Hz LP filter and 0.5 Hz HP filter.

The GSR, also known as the electro-dermal activity, is the signal obtained by measurement of skin conductivity by passing a small current (10 μA/cm 2 ) between the two electrodes placed at the fingers [22]. GSR signals were recorded using GSR100C amplifier and TSD203 transducer modules of the MP150 unit in DC mode. The 6 mm diameter Ag-AgCl electrodes were filled with conductive gel and placed on the middle finger and forefinger of the non-dominant hand. The GSR signal is directly influenced by the condition of skin surface where electrode is in contact. Different parts of skin may show different resistance change due to stress related sweating. In order to minimize the variance that may arise due to this effect, recordings were taken from the non-dominant hands of the subjects. Figure 1 shows some sample raw signals obtained from a non-diazepam subject during the "before angiography" phase.

Analysis of Physiological Signals

The segment of the ECG waveform that corresponds to ventricular depolarization is named as QRS complex. The QRS complex can easily be identified on normal ECG due to its relatively large acceleration and amplitude deflection, compared to other parts of the ECG. Since the R point of the QRS complex marks the peak of ventricular depolarization, the beat instants are taken at these points and consequently beat-tobeat intervals are determined as the length in time from one R wave to the next one. These intervals are called RR or inter beat intervals. While carrying out the HRV analysis, we have followed the methodological guidelines listed in [18]. To detect R peaks, we applied the algorithm suggested by Manriquez and Zhang [24,25]. RR interval series is inherently nonuniformly sampled series. Interpolation is necessary to produce a uniformly sampled HRV time series out of an RR interval series. The interpolation frequency was chosen as 4 Hz [26]. After interpolation, PSD analysis was performed using Welch's method with Hann window. Figure 1 The raw ECG, BVP and GSR signals recorded from a patient in the non-diazepam group during the "before angiography" phase.

The Welch method calculates an average over the modified periodograms. Periodogram is defined as a classical nonparametric method to obtain PSD. The main drawback of the periodogram method is the effect of offside lobe leakage due to finite data sets. To minimize the effect of the offside lobe leakage, time series of the signal is divided into overlapping sequences then each data sequence is windowed in order to smooth the edges of the signals [27,28]. Due to this improvement Welch method was used to estimate the power spectral density of the interpolated HRV signal. n = 0,1, ..., (L-1), i = 0,1, ..., (K-1),
x i (n) = x(n + i × S) S
The ith modified periodogram is:
P i (f ) = 1 U × L L−1 n=0 x i (n) × W(n) × e (−j×2×π ×f ×n) 2 (2)
where U is the normalizing constant.
U = 1 L × L−1 n=0 W 2 (n)(3)
The power spectral density is:
P w (f ) = 1 K × K−1 i=0P i (f )(4)
L is the length of the interpolated HRV signal and W(n) is the Hann function [27]. In our case, the window size of 256 samples and 50% overlap were chosen so that 0.0156 Hz spectral resolution was achieved ( Figure 2). In this way, power in LF and HF bands and their ratio, LF/HF, were calculated for each patient.

In the analysis of the BVP signal, we first find local maximum and minimum points of the signal. In Figure 3, we present a sample BVP signal with dicrotic notchs and the peaks detected on this signal. Defining a k max and a k min as the local maximum and minimum amplitude values of the kth pulse in the BVP signal, a series of pulse amplitude values are obtained as:
B k = a k max −a k min .(5)
As BVP signal features to be compared across different phases of the angiography, we use the median value of these pulse amplitude series obtained from each subject.

The PSD analysis was also performed on each subject's BVP signal to obtain the amplitudes of 1 st and 2 nd harmonics. Along with these two features, the ratio of these first two harmonics was also calculated as a third BVP signal feature to be used in the statistical comparisons. BVP signals PSDs were computing using the Welch method (Eq.s 1, 2, and 3). The Hann windowing was used and analysis window length was taken as one twentieth of the length of the data. Figure 4 shows the PSD of a sample BVP signal.

In GSR studies in which signal recording is conducted during the sudden stimulusfree period (tonic skin conductance level), the most common feature is the mean amplitude value of the signal and is named as skin conductance level [22]. As features to be used in GSR analysis, mean amplitude values of the signals were calculated for each patient at each angiography stage. In addition, the vector forming GSR signal was divided into 1 second long segments, and mean values for each of these segments were also calculated:   Here, t min and t max are given as:
seg mean(k) = 1 f s × t max (k) i=t min (k) GSR(i),(6)t min (k) = f s × (k − 1) + 1 (7) t max (k) = f s × k (8)
where, f s is the sampling frequency, k = 1, 2, 3,...,N/f, and is N the length of the GSR signal.

Since we had two groups of subjects (diazepam and non-diazepam) and three different angiography phases to be analyzed (before, during, and after), we have carried out statistical comparisons of feature values across both dimensions. We used one way ANOVA with a 95% confidence limit and a p-value of less than 0.05 was considered as statistically significant. Bonferroni type of critical value was used for multiple comparisons to find out not just whether there are any differences among the means, but particularly which pairs of means are significantly different.

All the data processing and statistical analyzing was carried out using in-house programs developed under MATLAB R2009b Software (MathWorks Inc., Natick MA, USA).

Results

In this study, in order to evaluate the psychophysiological responses that are caused by coronary angiography, an invasive process that is used for the diagnosis of atherosclerosis, the ECG, BVP and GSR signals were recorded at different stages of the operation and some features that could reflect the changes in the sympathetic nervous system were calculated and analyzed. In addition, the influence of diazepam, a tranquilizer drug which is typically administered during angiography operations, on psychophysiological responses is also investigated. To this end, 16 of the 34 patients participating in the study were administered diazepam and 18 were not. Detailed demographic data of the patients is reported in Table 1.

Groups mean and standard error of the mean (SEM) values were calculated for the each feature and line graphs with angiography phase on the x-axis were figured using group mean and SEM to provide a comparison. Groups mean and SEM. were pointed on the graph as Mean ± SEM.

In the tables, we present the p-values for both "across angiography phase" and "across group" comparisons with an estimate of the difference in group means and a confidence interval.

In the analysis of HRV signal, the LF/HF ratio was used as a feature. An increase in this ratio corresponds to imbalance of activity in the nervous system. In the table 2 and 3, we present the p-values with an estimate of the difference in group means and a confidence interval for both "across angiography phase" and "across group" comparisons. In reference to tables 2, and 3 for both diazepam and non-diazepam groups, we do not observe any statistically significant change across different angiography phases. As shown in Figure 5, the LF/HF is relatively higher at the "during angiography" phase for both groups, which confirms that there is an increased SNS activity during the operation. We notice that the SEM has the largest value in the "during angiography" phase for both groups. In other words, deviation for the "during angiography" phase is more than the other situations. One possible reason of this is extrasystoles due to the catheterization. Another reason may be the different recording duration of the signals. Recording duration could not be fixed due to the angiography operation. All the recordings were done for a duration of at least 5 minutes and PSD analysis were performed for the whole duration of the HRV signals to minimize the effect of the different recording duration [18].

On the other hand, a different feature that will be a more standard index or criteria can be computed like organization index (OI) [29]. If such a feature will be used as an indicator, physiologist must find out how the psychophysiological response is related to the feature, regarding the physiological meanings.

We also note that LF/HF ratio is elevated in the diazepam group at all angiography phases. Further, at the "before angiography" phase there is a statistically significant difference (p = 0.0281) diazepam and non-diazepam groups. Interpretation of these observations is relatively cumbersome, as one would normally expect a fall in the sympathetic activity with the action/influence of diazepam and consequently a lower LF/ HF ratio in the diazepam group. However, when we refer to recent literature in order to check the influence of diazepam on HRV (especially on LF/HF ratio) we come across studies [30,31] confirming our relatively interesting results. For instance, the study in [30] concludes that administration of diazepam did not cause a significant   [31] argues "One possible explanation is that the response of the cardiac sympathetic nerve to diazepam is dissociated with muscle sympathetic nerve activity, which modulates peripheral vascular resistance, but not the heart rate itself." Therefore, we conclude/observe that HRV is not a good indicator to evaluate the influence of the tranquilizer on the psychophysiological responses. When sympathetic activity increases, vein walls narrow and the volume of blood in the veins decreases and consequently the amount of infrared light reflected from the veins and BVP amplitude decreases. When the sympathetic activity decreases, the contrary occurs. Therefore, the amplitude of BVP signal is inversely proportional to SNS activity. In this case, since the distribution of BVP amplitude values, namely B k 's in Eq.4, is not necessarily symmetric, therefore we have used median instead of mean, as a measure of central tendency. This way, we have also lumped (summarized) many BVP amplitude values obtained from a subject during an angiography phase into a single figure. In the table 4 and 5, we present the p-values with an estimate of the difference in group means and a confidence interval for both "across angiography phase" and "across group" comparisons. Variation of the group mean of the B k medians according to the angiography phase is seen in Figure 6.

We notice that the mean BVP amplitude decreases during the angiography operation for both groups (Figure 6). This is expected, as increased SNS activity due to the operation reduces BVP amplitude. Results of the across phase and group comparison indicate that there is no statistically significant difference among different phases and groups (Tables 4 and 5). We conclude that BVP amplitude value is not a good indicator to evaluate psychophysiological responses that the subjects undergo during coronary angiography.

Another set of features obtained from the BVP signal is the "dicrotic notch." When the spectrum of BVP signal is examined, along with the first/fundamental harmonic that is due to the periodic nature of the BVP signal, a second peak that is associated with the presence of dicrotic notch is noticed [20,21]. In the study, peak values of first (DN1) and second (DN2) harmonics were obtained from the PSD of BVP signal. In the table 6 and 7, we present the p-values with an estimate of the difference in group means and a confidence interval for both "across angiography phase" and "across group" comparisons for DN1/DN2 values. Variation of the group mean of the DN1, DN2 and DN1/DN2 values according to the angiography phase is seen in Figure 7, 8 and 9, respectively.

As seen in Figure 9, the mean of the DN1/DN2 is relatively higher at the "during angiography" phase for both groups, which confirms that there is an increased SNS activity during the operation. Looking at the mean DN1 and DN2 values in the two groups, we also note that the decrease in the DN2 values cause an increase in the DN1/DN2 values for "during angiography" phase and a dramatic increase in the DN2 values cause a decrease in the DN1/DN2 values for the "after angiography" phase (Figure 7, 8). Upon reviewing table 6 and 7, we notice that the mean of "during angiography" phase minus the mean of "after angiography" phase is estimated to be 5.6280, and a 95% confidence interval for this difference is [0.7950, 10.4610] for diazepam group. And for the non-diazepam group the estimated difference in means is 8.0277 and the confidence interval is [0.8497, 15.2057]. Therefore, we can conclude that the during angiography phase's DN1/DN2 values and after angiography phase's DN1/DN2 values are different, statistically.

Actually, hospitalization and the waiting period for an invasive test or surgery is difficult for all the patients and some research showed that waiting for surgery impairs the quality of life for patients [32]. Anxiety and fear caused by waiting for angiography, increased values but not as much as the process itself. Therefore, it is concluded that the anxiety and fear of the person increase before angiography and feels most  comfortable after angiography compared to other phases. p-values and confidence interval confirm our interesting results. These findings support the anticipation that when a person encounters a stressful situation, the left ventricular contraction pressure overcomes the negative effect due to the aorta valves and as a result the power of the second harmonic (DN2) decreases. Therefore, we conclude that DN1/DN2 ratio is a feature that has the potential to properly reflect the psychophysiological responses.

The most common feature used in GSR analysis is the mean amplitude value of the signal, namely "skin conductance level" [22]. In the table 8 and 9, we present the pvalues with an estimate of the difference in group means and a confidence interval for both "across angiography phase" and "across group" comparisons for skin conductance level. Variation of the group mean of the skin conductance level according to the angiography phase is seen in Figure 10.

We find out that the mean skin conductance obtains its highest value during the angiography operation for both groups (Figures 10). This is expected, as increased SNS activity due to the operation increases GSR amplitude. The mean skin conductance for  "before angiography" phase is smaller than the mean skin conductance of "during angiography" phase. On the other hand, the difference between them was much less compared to a difference between "during" and "after". The mean skin conductance for "after angiography" is the smallest value of three.

This observation is in-line with the finding obtained for the DN1/DN2, which confirms that waiting for angiography cause anxiety and fear.

Results of the across phase comparison indicate that there are no statistically significant difference among different phases in both of the groups (Table 8). We also note that there are statistically significant differences between diazepam and non-diazepam group for skin conductance level in the "before" and "after" angiography phases (Table  9).

In addition, as mentioned above, the vector forming GSR signal was first divided into 1-second long segments, and the mean values for each of these segments (seg_means) were calculated (Eq.s 5 through 7). Next, median of these seg_mean values were computed. In the table 10 and 11, we present the p-values with an estimate of the difference in group means and a confidence interval for both "across angiography phase" and "across group" comparisons for seg_mean median values. Variation of the group  mean of the seg_mean median values according to the angiography phase is seen in Figure 11. As seen in Figure 11, mean value of seg_mean medians is higher at the "during angiography" phase for both groups. This observation is in-line with the finding obtained for other features (physiological signal), which confirms that there is an increased SNS activity during the operation. Also estimated difference in mean value obtained for "before-during" comparison (Table 10) support the expectation that waiting for angiography increases the intensity of the psychophysiological responses. The  Okkesim et al. BioMedical Engineering OnLine 2011, 10:71 http://www.biomedical-engineering-online.com/content/10/1/71 estimated differences in mean for "before-during" comparison is almost ten times smaller than others for diazepam and non-diazepam group.

In Tables 11, results of the across group comparison indicate that there are statistically significant changes between different group. These observations underline the tranquilizing (SNS suppressing) influence of the diazepam. We conclude that skin conductance (GSR amplitude) and seg_mean vector are potentially good indicators to evaluate the psychophysiological responses during angiography and the influence of the tranquilizer on the psychophysiological responses.

Discussion and Conclusion

In the studies conducted to measure psychophysiological responses, facial expressions and voice tone analyses are also performed [33]. However, these variables change very much according to culture, age and gender compared to physiological signals. For this purpose, physiological signals were used. In addition to HRV, BVP and GSR signals, there are also studies showing that psychophysiological responses may also be evaluated over electromyogram, electroencephalogram signals and pupil diameter. Therefore, new physiological signals will be included in a future study.

A recent review report that in most studies specific emotions like fear, anger, anxiety, embarrassment etc. are utilized as stimulants, in order to evaluate emotional responses that cause changes in the ANS function [34]. However, in this study, a non-invasive test which is encountered in a real-life situation and that comprises anxiety and fear [5][6][7][8][9] was evaluated in terms of psychophysiological responses.

All features obtained for the two groups show that SNS was more active during angiography. Making this result more comprehensible is only possible by increasing the number of subjects participating in the study. Since the results obtained for DN1/ DN2 and GSR signal are statistically significant, they are the most fundamental indicators that have to be added to the studies in which psychophysiological responses are examined.  The activities of the sweat glands increase until the glands reach saturation. After an adequate resting period, in the case of re-stimulation, sweat glands will be emptied again. A new feature, like RR interval, can be computed using GSR signals. This feature should contain the knowledge of the deviations as a consequence of emptying of the sweat glands until saturation and then being emptied again with a new stimulus. Therefore, stimulation frequencies of the sweat glands were tried to be obtained by calculating the seg_mean. A problem for the seg_mean is that unlike RR intervals, sweat glands cannot produce psychophysiological responses for every stimulus due to the resting period.

It is not clear what is the resting time of a sweat gland or inter response intervals [35]. Therefore, it is not clear how long the seg_mean should be so as to capture psychophysiological responses. In fact, if resting time for sweat glands of a person can be determined, it will be possible to understand whether there is a correspondence with RR intervals or not.

The maximum sympathetic activation is anticipated during the angiography phase, while a balanced level of activity is expected between SNS and PNS at before and after  angiography stages. Although waiting for angiography (at before angiography stage) causes psychophysiological responses, stimulation of the sympathetic activation in this stage will not be comparable to what it is at "during angiography" stage. Therefore, we typically anticipate a significant difference for "before-during" and "during-after" stage comparisons. In accordance with this expectation, we would like to identify a signal and/or feature that exhibit this characteristic as a potential indicator for quantification of the psychophysiological responses during angiography. Similarly, a good indicator signal/feature should not produce any statistically significant difference for "beforeafter" comparison. As for the analysis across the group dimension (i.e., diazepam vs. non-diazepam comparisons at three angiography stages), a good indicator may yield statistically significant differences at any stage. However, in terms of being significant, comparison results at before and after stages should be in agreement. Other combinations of results may be attributed to the different action/influence of diazepam drug at different angiography stages. When we analyze our findings/results along these lines we conclude that the line graphs are in the same direction with the predictions. We also note that the differences are not always statistically significant. The main reason for this result may be the individual differences on the perception of and capacity to cope with stress. Therefore, maybe each person should be considered separately instead of analyzing differences  across different stages and groups. We would like to address this issue in a future study.

We would like to underline following biomedical engineering contributions in our study: 1) We have recorded and analyzed several biomedical signals (ECG, BVP, GSR) at three phases of angiography (before, during, and after). This comprehensive multiparameter signal analysis approach is "original" in terms of evaluation of psychophysiological responses. Previously, questionnaire type of "non-quantitative" techniques, which are prone to errors, were utilized mostly.

2) Further, in our study, underlying problem or cause for psychophysiological responses, i.e. coronary angiography, is of utmost clinical importance. Actually, this is the inception point of our study; basically we want to evaluate psychophysiological responses that may occur during angiography. However, while trying to address this question by designing and conducting a comprehensive clinical study, we have realized that methods used in the literature are not satisfactory. Therefore, we thought of recording relevant physiological signals at three phases: before, during, and after the angiography. Recoding of those signals during the angiography operation was a challenge by itself.

3) Features to be extracted and analyzed from the ECG signal is relatively typical; however analysis methods that we suggested and used for BVP and GSR signals are interesting and proved to be useful.

In summary, we have found out that features obtained especially from BVP and GSR signals undergo some statistically significant changes during different phases of the angiography test. Therefore, these signals and obtained features can be used in clinical studies or artificial intelligence applications where quantitative diagnosis and/or grading of psychophysiological responses are necessary.

is the size of the sequences and S = L,

Figure 2
2Power spectral density analysis of HRV signal of a patient in the non-diazepam group "during the angiography" phase.

Figure 3
3Local maximum and minimum points in the BVP signal and dicrotic notches.

Figure 4
4Power spectral density analysis of a patient's BVP signal in the non-diazepam group "during the angiography" phase. (The duration of the signal and sampling frequency was respectively 467,407 second and 1000Hz).

Figure 5
5Over-all LF/HF ratios (Mean ± SEM) across different angiography phases.

Figure 6
6Over-all median B k values (Mean ± SEM) across different angiography phases.

Figure 7
7Over-all DN1 values (Mean ± SEM) across different angiography phases.

Figure 8
8Over-all DN2 values (Mean ± SEM) across different angiography phases.

Figure 9
9Over-all DN1/DN2 values (Mean ± SEM) across different angiography phases.

Figure 10
10Over-all mean GSR values (Mean ± SEM) across different angiography phases.

Figure 11
11Over-all median seg_mean values (Mean ± SEM) across different angiography phases.

Table 1
1Subject demographicsDiazepam Group
Non-Diazepam Group

Number of Subjects
16
18

Age (Mean ± Standard Deviation)
60 ± 12
59 ± 11

Gender (Male/Female)
9/7
13/5

Education Level
Primary School: 15
High School: 1
University: 0

Primary School: 15
High School: 2
University: 1

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Table 2
2Results of the statistical analysis of the LF/HF ratio for phase comparisonAcross Phase Comparison

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Diazepam
Before-During
-0.5989
[-3.4960, 2.2982]
0.8563

Before-After
-0.0807
[-2.9778, 2.8164]

During After
0.5183
[-2.3789, 3.4154]

Non_Diazepam
Before-During
-0.8818
[-2.4851, 0.7214]
0.4003

Before-After
-0.3832
[-1.9865, 1.2201]

During After
0.4986
[-1.1047, 2.1019]

Table 3
3Results of the statistical analysis of the LF/HF ratio for group comparison change especially in elderly. Among different reasons explanations for this phenomenon T. Kitajima, T. Kanbayashi Y. Saito et al.Across Group Comparison
(Diazepam -Non_diazepam)

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Before
1.3845
[0.1586, 2.6103]
0.0281

During
1.1016
[-1.4706, 3.6738]
0.3895

After
1.0820
[-0.4370, 2.6010]
0.1565

Table 4
4Results of the Statistical analysis of the B k for phase comparisonAcross Phase Comparison

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Diazepam
Before-During
0.2077
[-0.8128, 1.2281]
0.7433

Before-After
-0.1037
[-1.1241, 0.9168]

During After
-0.3114
[-1.3318, 0.7091]

Non_Diazepam
Before-During
0.7110
[-0.1991, 1.6211]
0.1166

Before-After
0.0809
[-0.8292, 0.9910]

During After
-0.6301
[-1.5402, 0.2800]

Table 5
5Results of the Statistical analysis of the B k for group comparisonAcross Group Comparison
(Diazepam -Non_diazepam)

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Before
0.0240
[-0.8046, 0.8525]
0.9534

During
0.5273
[-0.1863, 1.2409]
0.1421

After
0.2086
[-0.6171, 1.0343]
0.6104

Table 6
6Results of the Statistical analysis of the DN1/DN2 for phase comparisonAcross Phase Comparison

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Diazepam
Before-During
-3.4570
[-8.2900, 1.3760]
0.0201

Before-After
2.1710
[-2.6620, 7.0040]

During After
5.6280
[0.7950, 10.4610]

Non_Diazepam
Before-During
-2.1975
[-9.3755, 4.9805]
0.0224

Before-After
5.8302
[-1.3478, 13.0082]

During After
8.0277
[0.8497, 15.2057]

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Table 7
7Results of the Statistical analysis of the DN1/DN2 for group comparisonAcross Group Comparison
(Diazepam -Non_diazepam)

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Before
-4.0692
[-8.5906, 0.4522]
0.0761

During
-2.8097
[-9.3415, 3.7221]
0.3874

After
-0.4100
[-4.5061, 3.6861]
0.8397

Table 8
8Results of the statistical analysis of the GSR signals for phase comparison Across Phase Comparison The Estimated Difference in Means Confidence Interval For the True Mean P-ValueDiazepam
Before-During
-0.0608
[-0.3026, 0.1810]
0.1222

Before-After
0.1384
[-0.1035, 0.3802]

During After
0.1992
[-0.0426, 0.4410]

Non_Diazepam
Before-During
-0.0270
[-0.3843, 0.3303]
0.0998

Before-After
0.2601
[-0.0973, 0.6174]

During After
0.2871
[-0.0703, 0.6444]

Table 9
9Results of the statistical analysis of the GSR signals for group comparisonAcross Group Comparison
(Diazepam -Non_diazepam)

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Before
-0.3281
[-0.5944, -0.0619]
0.0173

During
-0.2943
[-0.5960, 0.0074]
0.0556

After
-0.2064
[-0.3977, -0.0152]
0.0353

Table 10
10Results of the statistical analysis of the seg_mean for phase comparison Okkesim et al. BioMedical Engineering OnLine 2011, 10:71 http://www.biomedical-engineering-online.com/content/10/1/71Across Phase Comparison

The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Diazepam
Before-During
-0.0407
[-0.2581, 0.1767]
0.1686

Before-After
0.1211
[-0.0963, 0.3385]

During-After
0.1618
[-0.0557, 0.3792]

Non_Diazepam
Before-During
-0.0310
[-0.3940, 0.3321]
0.0999

Before-After
0.2621
[-0.1010, 0.6252]

During-After
0.2931
[-0.0700, 0.6561]

Table 11
11Results of the statistical analysis of the seg_mean for group comparisonAcross Group Comparison (Diazepam -Non_diazepam)The Estimated
Difference in Means

Confidence Interval
For the True Mean

P-Value

Before
-0.3375
[-0.6066, -0.0684]
0.0156

During
-0.3278
[-0.6181, -0.0374]
0.0282

After
-0.1964
[-0.3875, -0.0054]
0.0443

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Authors' contributions SO carried out data acquisition, data pre-processing and classification processes. MHA designed the study and prepared manuscript. MGK provided patients, performed coronary angiography and participated in the design of the study. SK participated in the design of the study and coordination. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
. Turkey. 3 Faculty of Engineering and Architecture. 380394Institute of Biomedical Engineering, Fatih University, Buyukcekmece, Istanbul, 34500, Turkey. 2 Department of Cardiology, Erciyes University ; Abdullah Gül University ; Department of Electrical and Electronics Engineering, Zirve University, Kizilhisar CampusInstitute of Biomedical Engineering, Fatih University, Buyukcekmece, Istanbul, 34500, Turkey. 2 Department of Cardiology, Erciyes University, Talas, Kayseri, 38039, Turkey. 3 Faculty of Engineering and Architecture, Abdullah Gül University, Kocasinan, Kayseri, 38090, Turkey. 4 Department of Electrical and Electronics Engineering, Zirve University, Kizilhisar Campus, Gaziantep, 27260, Turkey.

D B Baker, R A Karasek, Stress. In Occupational Health -Recognizing and Preventing Work-Related Disease and Injury. Levy BS, Wegman DH. Philadelphia: Lippincott Williams and WilkinsBaker DB, Karasek RA: Stress. In Occupational Health -Recognizing and Preventing Work-Related Disease and Injury. Edited by: Levy BS, Wegman DH. Philadelphia: Lippincott Williams and Wilkins; 2000:419-435.

Stress and cognitive function. B S Mcewen, R M Sapolsky, Curr Opin in Neurobiol. 5McEwen BS, Sapolsky RM: Stress and cognitive function. Curr Opin in Neurobiol 1995, 5:205-216.

Emotional Intelligence New York: Bantam Books. D Goleman, Goleman D: Emotional Intelligence New York: Bantam Books; 1995.

Affective Medicine: Technology with Emotional Intelligence. R W Picard, Future of Health Technology. Edited by: Bushko RG. AmsterdamIOS PressPicard RW: Affective Medicine: Technology with Emotional Intelligence. In Future of Health Technology. Edited by: Bushko RG. Amsterdam: IOS Press; 2002:69-84.

Fear of patients related to coronary arteriography. Pounonen M Heikkila¨j, V Virtanen, P Laippala, Journal of Advanced Nursing. 281Heikkila¨J, Pounonen M, Virtanen V, Laippala P: Fear of patients related to coronary arteriography. Journal of Advanced Nursing 1998, 28(1):54-62.

Psychologic preparation to decrease anxiety associated with cardiac catheterisation. A M Mott, Journal of Vascular Nursing. 172Mott AM: Psychologic preparation to decrease anxiety associated with cardiac catheterisation. Journal of Vascular Nursing 1999, 17(2):41-49.

Psychologic preparation for cardiac catheterization. K O Anderson, F T Masur, Heart and Lung. 182Anderson KO, Masur FT: Psychologic preparation for cardiac catheterization. Heart and Lung 1989, 18(2):154-163.

. Okkesim, BioMedical Engineering OnLine. 10Okkesim et al. BioMedical Engineering OnLine 2011, 10:71 http://www.biomedical-engineering-online.

Fears and beliefs of patients regarding cardiac catheterization. P H Caldwell, H M Arthur, M Natarajan, S Anand, Social Science & Medicine. 65Caldwell PH, Arthur HM, Natarajan M, Anand S: Fears and beliefs of patients regarding cardiac catheterization. Social Science & Medicine 2007, 65:1038-1048.

Anxiety and health-related quality of life in patients awaiting elective coronary angiography. De-Jong-Watt , W J Arthur, H M , Heart and Lung. 334De-Jong-Watt WJ, Arthur HM: Anxiety and health-related quality of life in patients awaiting elective coronary angiography. Heart and Lung 2004, 33(4):237-248.

ACC/AHA guidelines for coronary angiography: executive summary and recommendations. P J Scanlon, D P Faxon, A M Audet, B Carabello, G J Dehmer, K A Eagle, Circulation. 99Scanlon PJ, Faxon DP, Audet AM, Carabello B, Dehmer GJ, Eagle KA, et al: ACC/AHA guidelines for coronary angiography: executive summary and recommendations. Circulation 1999, 99:2345-2357.

The burden of cardiovascular diseases mortality in Europe. S Sans, H Kesteloot, D Kromhout, European Heart Journal. 18Sans S, Kesteloot H, Kromhout D: The burden of cardiovascular diseases mortality in Europe. European Heart Journal 1997, 18:1231-1248.

Psychological responses to cardiac diagnosis: Changes in illness representations immediately following coronary angiography. D A Devcich, C J Ellis, G Gamble, K J Petrie, Journal of Psychosomatic Research. 65Devcich DA, Ellis CJ, Gamble G, Petrie KJ: Psychological responses to cardiac diagnosis: Changes in illness representations immediately following coronary angiography. Journal of Psychosomatic Research 2008, 65:553-556.

The effects of back massage before diagnostic cardiac catheterization. Alternative Therapies In Health And Medicine. M E Mcnamara, D E Burnham, C Smith, D L Caroll, 9McNamara ME, Burnham DE, Smith C, Caroll DL: The effects of back massage before diagnostic cardiac catheterization. Alternative Therapies In Health And Medicine 2003, 9(1):83-95.

Toward machine emotional intelligence: analysis of affective physiological state. R W Picard, E Vyzas, J Healey, IEEE Transactions Pattern Analysis and Machine Intelligence. 23Picard RW, Vyzas E, Healey J: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Transactions Pattern Analysis and Machine Intelligence 2001, 23(10):1175-1191.

Effect of music on anxiety, stress, and depression levels in patients undergoing coronary angiography. F Moradipanah, E Mohammadi, A Z Mohammadil, Eastern Mediterranean Health Journal. 153Moradipanah F, Mohammadi E, Mohammadil AZ: Effect of music on anxiety, stress, and depression levels in patients undergoing coronary angiography. Eastern Mediterranean Health Journal 2009, 15(3):639-647.

Effects of diazepam on human pain tolerance and pain sensitivity. C Chapman, B Feather, Psychomatic Medicine. 35American Psychosomatic SocietyChapman C, Feather B: Effects of diazepam on human pain tolerance and pain sensitivity. Psychomatic Medicine, American Psychosomatic Society 1973, 35:330-340.

The emergency function of the adrenal medulla in pain and the major emotions. W B Cannon, American Journal of Physiology. 33Cannon WB: The emergency function of the adrenal medulla in pain and the major emotions. American Journal of Physiology 1914, 33:356-372.

Task force of the European society of cardiology and the North American society of pacing and electrophysiology: Heart rate variability -standards of measurement, physiological interpretation, and clinical use. Circulation. 935Task force of the European society of cardiology and the North American society of pacing and electrophysiology: Heart rate variability -standards of measurement, physiological interpretation, and clinical use. Circulation 1996, 93(5):1043-1065.

G G Berntson, J T BiggerJr, D L Eckberg, P Grossman, P G Kaufmann, M Malik, H N Nagaraja, S W Porges, J P Saul, P H Stone, M W Van Der Molen, Heart rate variability: Origins, methods, and interpretive caveats. 34Berntson GG, Bigger JT Jr, Eckberg DL, Grossman P, Kaufmann PG, Malik M, Nagaraja HN, Porges SW, Saul JP, Stone PH, Van Der Molen MW: Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiol 1997, 34:623-648.

Signal processing quantification of changes in the blood volume pulse (BVP) waveform due to exercise. C Li, J Zhai, A Barreto, Proc of the 25th Annual International Conference of the IEEE EMBS 17-21. of the 25th Annual International Conference of the IEEE EMBS 17-214Li C, Zhai J, Barreto A: Signal processing quantification of changes in the blood volume pulse (BVP) waveform due to exercise. Proc of the 25th Annual International Conference of the IEEE EMBS 17-21 Sept 2003 Cancun 2003, 4:3180-3183.

Digital signal processing methods for the evaluation of Blood Volume Pulse (BVP) waveform changes due to exercise. C Li, J Zhai, A Barreto, Biomedical Sciences Instrumentation. 39Li C, Zhai J, Barreto A: Digital signal processing methods for the evaluation of Blood Volume Pulse (BVP) waveform changes due to exercise. Biomedical Sciences Instrumentation 2003, 39:163-168.

Publication Recommendations for Electrodermal Measurements. D C Fowles, M J Christie, R Edelberg, W W Grings, D T Lykken, P H Venables, Psychophysiol. 18Fowles DC, Christie MJ, Edelberg R, Grings WW, Lykken DT, Venables PH: Publication Recommendations for Electrodermal Measurements. Psychophysiol 1981, 18:232-239.

Analysis and characterization of photo-plethysmographic signal. J Bhattacharya, P Kanjilal, IEEE Transac on Biomed Eng. 48Bhattacharya J, Kanjilal P: Analysis and characterization of photo-plethysmographic signal. IEEE Transac on Biomed Eng 2001, 48:5-11.

Sleep stage and obstructive apneaic epoch classification using single-lead ECG. B Yılmaz, M H Asyalı, E Arıkan, S Yetkin, F Özgen, BioMedical Engineering OnLine. 939Yılmaz B, Asyalı MH, Arıkan E, Yetkin S, Özgen F: Sleep stage and obstructive apneaic epoch classification using single-lead ECG. BioMedical Engineering OnLine 2010, 9:39.

An algorithm for robust and efficient location of T-wave ends in electrocardiograms. Q A Zhang, A I Manriquez, M 'edigue Papelier, C Y Sorine, M , IEEE Transaction on Biomedical Engineering. 5312Zhang QA, Manriquez AI, M'edigue Papelier CY, Sorine M: An algorithm for robust and efficient location of T-wave ends in electrocardiograms. IEEE Transaction on Biomedical Engineering 2006, 53(12):2544-2552.

Discrimination Power of Long-Term Heart Rate Variability Measures. M H Asyalı, Proc of the 25th Annual International Conference of the IEEE EMBS. of the 25th Annual International Conference of the IEEE EMBS1Asyalı MH: Discrimination Power of Long-Term Heart Rate Variability Measures. Proc of the 25th Annual International Conference of the IEEE EMBS, 17-21 Sept; 2003 Cancun 2003, 1:200-203.

The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. P D Welch, IEEE Transactions on Audio and Electroacoustics. 15Welch PD: The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Transactions on Audio and Electroacoustics 1967, 15:70-73.

Frequency domain analysis of power system transients by using Welch and Yule Walker AR Methods. Energy Conversion and Management. A Alkan, A S Yilmaz, 48Alkan A, Yilmaz AS: Frequency domain analysis of power system transients by using Welch and Yule Walker AR Methods. Energy Conversion and Management 2007, 48:2129-2135.

Assessment of Global Atrial Fibrillation Organization to Optimize Timing of Atrial Defibrillation. T H Everett, J R Moorman, L C Kok, J G Akar, D E Haines, Circulation. 103Everett TH, Moorman JR, Kok LC, Akar JG, Haines DE: Assessment of Global Atrial Fibrillation Organization to Optimize Timing of Atrial Defibrillation. Circulation 2001, 103:2857-2861.

Effects of midazolam and diazepam as premedication on heart rate variability in surgical patients. T Ikeda, M Doi, K Morita, K Ikeda, Br J Anaesth. 73Ikeda T, Doi M, Morita K, Ikeda K: Effects of midazolam and diazepam as premedication on heart rate variability in surgical patients. Br J Anaesth 1994, 73:479-483.

Diazepam reduces both arterial blood pressure and muscle sympathetic nerve activity in human. T Kitajima, T Kanbayashi, Y Saito, Neuroscience Letters. 355Kitajima T, Kanbayashi T, Saito Y, et al: Diazepam reduces both arterial blood pressure and muscle sympathetic nerve activity in human. Neuroscience Letters 2004, 355:77-80.

Fear and anxiety in patients at different time-points in the coronary artery bypass process. M Koivula, M Tarkka, P Laippala, M Paunonen-Ilmonen, International Journal of Nursing Studies. 39Koivula M, Tarkka M, Laippala P, Paunonen-Ilmonen M: Fear and anxiety in patients at different time-points in the coronary artery bypass process. International Journal of Nursing Studies 2002, 39:811-822.

R W Picard, J Healey, Affective wearables. Personal Technologies. 1Picard RW, Healey J: Affective wearables. Personal Technologies 1997, 1:231-240.

Autonomic nervous system activity in emotion: A review. S D Kreibig, Biological Psychology. 84Kreibig SD: Autonomic nervous system activity in emotion: A review. Biological Psychology 2010, 84:394-421.

Signal Processing for Recognition of Human Frustration. R Fernandez, R W Picard, 10.1186/1475-925X-10-71IEEE Int Conf on Acoustics, Speech and Signal Processing. Seatle, W.A. USA6Fernandez R, Picard RW: Signal Processing for Recognition of Human Frustration. In IEEE Int Conf on Acoustics, Speech and Signal Processing. Volume 6. Seatle, W.A. USA; 1998:3773-3776. doi:10.1186/1475-925X-10-71

Analysis of coronary angiography related psychophysiological responses. Okkesim, BioMedical Engineering OnLine. 1071Cite this article asCite this article as: Okkesim et al.: Analysis of coronary angiography related psychophysiological responses. BioMedical Engineering OnLine 2011 10:71.

. Okkesim, BioMedical Engineering OnLine. 10Okkesim et al. BioMedical Engineering OnLine 2011, 10:71 http://www.biomedical-engineering-online.com/content/10/1/71


Correlation of adiposity indices with cardiovascular disease risk factors in healthy adults of Singapore: a cross- sectional study

Xinyan Bi
Ling Siew
Tey
Claudia Leong
Rina Quek
Yi Ting Loo
Christiani Jeyakumar Henry
Correlation of adiposity indices with cardiovascular disease risk factors in healthy adults of Singapore: a cross- sectional study
10.1186/s40608-016-0114-4R E S E A R C H A R T I C L E Open Access
Background: Obesity has long been highlighted for its association with increased incidence of cardiovascular disease (CVD). Nonetheless, the best adiposity indices to evaluate the CVD risk factors remain contentious and few studies have been performed in Asian populations. In the present study, we compared the association strength of percent body fat (PBF) to indirect anthropometric measures of general adiposity (body mass index (BMI) and body adiposity index (BAI)) and central adiposity (waist circumference (WC), and waist-to-hip ratio (WHR)) for the prediction of CVD risk factors in healthy men and women living in Singapore.Methods: A total of 125 individuals (63 men and 62 women) took part in this study. PBF was measured by using three different techniques, including bioelectrical impedance analysis (BIA), BOD POD, and dual-energy X-ray absorptiometry (DEXA). Anthropometric measurements (WC, hip circumference (HC), height, and weight), fasting blood glucose (FBG), fasting serum insulin (FSI), and lipid profiles were determined according to standard protocols. Correlations of anthropometric measurements and PBF with CVD risk factors were compared.Results: Irrespective of the measuring techniques, PBF showed strong positive correlations with FSI, HOMA-IR, TC/HDL, TG/HDL, and LDL/HDL in both genders. While PBF was highly correlated with FBG, SBP, and DBP in females, no significant relationships were observed in males. Amongst the five anthropometric measures of adiposity, BAI was the best predictor for CVD risk factors in female participants (r = 0.593 for HOMA-IR, r = 0.542 for TG/HDL, r = 0. 474 for SBP, and r = 0.448 for DBP). For males, the combination of WC (r = 0.629 for HOMA-IR, and r = 0.446 for TG/HDL) and WHR (r = 0.352 for SBP, and r = 0.366 for DBP) had the best correlation with CVD risk factors.Conclusion: Measurement of PBF does not outperform the simple anthropometric measurements of obesity, i.e. BAI, WC, and WHR, in the prediction of CVD risk factors in healthy Asian adults. While measures of central adiposity (WC and WHR) tend to show stronger associations with CVD risk factors in males, measures of general adiposity (BAI) seems to be the best predictor in females. The gender differences in the association between adiposity indices and CVD risk factors may relate to different body fat distribution in males and females living in Singapore. These results may find further clinical utility to identify patients with CVD risk factors in a more efficient way.

Background

Obesity (excess adiposity tissue) is a substantial public health crisis globally with the prevalence increasing rapidly in Asia [1]. Compared with normal weight people, those who are overweight or obese are at greater risk for many diseases, such as cardiovascular disease (CVD), osteoarthritis, diabetes mellitus (DM), and cancers [2,3]. Despite the clear evidence linking obesity to various poor health outcomes, obesity itself is a complex and heterogeneous condition [4]. Previous study has shown that equally obese subjects with the same amount of total body fat may have markedly different risk factor profiles [5]. The accumulation of intra-abdominal (visceral) adiposity was associated with increased risk of metabolic abnormalities such as insulin resistance and dyslipidemia [5,6]. However, obese subjects with a normal metabolic risk profile (known as metabolically healthy obese individuals) were generally characterized by low levels of visceral adipose tissue and by subcutaneous obesity [7]. Therefore, the evaluation of total body fat alone is insufficient to distinguish between individuals at high and low risk of CVD. Recent technological advances have made possible the accurate measurement of regional fat compartments using magnetic resonance imaging (MRI) and computed tomography (CT) [8]. These methods may be better predictors of obesity-related health risks, their applications in large epidemiological studies or clinical practice is, nevertheless, not feasible due to the complexity and high costs of the instrumentations.

In contrast, simple-to-use anthropometric indices, e.g. body mass index (BMI; weight in kilograms divided by square of height in meters), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR; ratio of WC to HC), and body adiposity index (BAI; HC divided by height in meters 1.5 , and subtracting 18 from the result), have been widely used as surrogates to correlate with total and subcutaneous body fat volumes for assessing adiposityrelated risks [9][10][11][12][13][14]. Despite years of research, the best measure of adiposity and cut-off values to predict the CVD risk factors have remained contentious. Previous studies have reported that BMI was significantly related with various health outcomes [9]. It has been suggested that BMI was equally effective as WC in identifying individuals at increased risk of CVD [10,11]. Conversely, some studies reported that WC is a better indicator of CVD risk than BMI and WHR in ethnically diverse groups [12][13][14], whereas a high WHR has been identified as an increased risk of dyslipidemia, hypertension, CVD and DM compared with BMI [4]. One explanation of these discrepant results could be attributed to the methodological drawbacks of field methods used for anthropometry analysis and the differences in participant characteristics, e.g. age, gender, ethnicity, diet, countries where participants reside [15][16][17]. Compared to Caucasians, Asians have more visceral adiposity, which is metabolically more adverse, for the same BMI. To date, there have been few studies to examine the relationship between anthropometric adiposity indices to CVD risk factors in Asian populations. Therefore the first objective of this study was to correlate measured anthropometric variables with CVD risk factors, e.g. dyslipidemia, insulin resistance, and blood pressure, in an adult population in Singapore, a South-East Asian country. In addition to the simple anthropometric measures, the commonly used body fat measuring techniques, i.e. dual-energy X-ray absorptiometry (DEXA), BOD POD, and bioelectrical impedance analysis (BIA) were also utilized in this study to estimate the relationship between percent body fat (PBF) and CVD risk factors. Therefore, the secondary objective of this study was to compare the value of simple anthropometric measures of adiposity to advanced fat measuring techniques for the prediction of CVD risk factors.

Methods

Study design

This study was limited to cross-sectional analyses of data from participants attending a visit between June 2014 and June 2015. The participants included 125 healthy adults aged 21 to 68 years: 63 males (50.4 %) and 62 females (49.6 %). They were recruited from the general public in Singapore through advertisements and posters that were placed around the National University of Singapore campus, public area and on the Clinical Nutrition Research Centre (CNRC) website. To be eligible, participants were required to be Singaporeans or individuals who have resided in Singapore for a minimum of five years, healthy males and females. Participants were excluded if they were pregnant or diagnosed with any major diseases. Prior to the test day, participants were asked to restrict alcohol and caffeine-containing drinks as well as to refrain themselves from intense physical activity.

Clinical measures

Participants arrived at the laboratory in the morning after a 10 h overnight fast. Two finger prick capillary blood samples were obtained for determining blood glucose concentration (FBG, mmol/L) using the HemoCue® 201+ RT Glucose analyser (HemoCue Ltd, Dronfield, UK). In addition, a total of 10 mL of venous blood was collected into Vacutainers (Becton Dickinson Diagnostics). Blood samples were separated by centrifugation at 1500 rpm for 10 min at 4°C within 2 h of being drawn and aliquots were stored at −80°C until analysis. Fasting serum insulin (FSI, μU/mL) was measured using the immunochemistry analyzer COBAS e411 (Roche, HITACHI, USA). Insulin resistance index HOMA-IR was calculated using FBG and FSI (HOMA-IR = FBG × FSI/22.5).

Fasting lipid parameters including total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL), and triglycerides (TG) were measured using chemistry analyzer COBAS c311 (Roche, HITA-CHI, USA). The ratios of TC/HDL, TG/HDL, and LDL/ HDL were calculated from the standard lipid profile. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured with an Omron blood pressure monitor (model HEM-907). The measurements were done in duplicate and readings were averaged.

Standing height was measured to the nearest millimetre with a stadiometer. Body weight and composition were measured to the nearest 0.1 kg by using an 8-electrode BIA device (Tanita BC-418, Tokyo, Japan). Participants were weighed in light clothing without footwear. PBF was measured by using Bod Pod™® Body Composition Tracking System (Cosmed, Rome, Italy; software version 5.2.0) and DEXA (QDR 4500A, fan-beam densitometer, software version 8.21; Hologic, Waltham, USA). WC and HC were measured with an anthropometric measuring tape while the participants were dressed in light clothing. Measurements were taken according to the ISAK International Standards for Anthropometric Assessment guidelines [18]. WC was measured at the minimum circumference between the iliac crest and the rib cage. HC was measured at the maximum protuberance of the buttocks. All of these anthropometric measurements were done in duplicate and readings were averaged.

The body mass index (BMI) was calculated using weight (kg) divided by the height squared (m 2 ) whereas the body adipose index (BAI) was calculated using HC and height (BAI = (HC in centimetres)/(height in meters) 1.5 -18).

Statistical analysis

Baseline characteristics of the participants were presented as arithmetic means ± SDs. Baseline characteristics was compared between the two gender groups by using independent-samples t tests. Pearson's correlations were used to investigate the associations between adiposity indices and various CVD risk factors. Linear regression models, adjusted for age, were used to examine the adjusted associations. All statistical analyses were performed using Stata 11.1 (StataCorp, College Station, Tex, USA). Two sided p < 0.05 was considered statistically significant in all cases.

Results

The clinical characteristics of the study population are shown in Table 1. Almost equal men (50.4 %) and women (49.6 %) with similar average age took part in the study. Amongst the anthropometric measurements, males had significantly higher height, weight, BMI, WC, and WHR than females, while females had significantly higher PBF and BAI than males. With the exception of HDL (1.5 ± 0.3 for males and 1.7 ± 0.4 for females, p = 0.001), systolic blood pressure (117.7 ± 10.4 for males and 105.3 ± 17.8 for females, p < 0.001) and diastolic blood pressure (67.7 ± 8.6

for males and 64.4 ± 9.5 for females, p = 0.046), all of the cardiovascular traits did not differ significantly between males and females.

Results obtained from Pearson's correlation were comparable to those obtained from linear regression models adjusting for age, e.g. statistically significant results remained largely unchanged while non-significant results remained the same (data not shown). Table 2 shows the associations between various anthropometric adiposity indices and CVD risk factors in male participants. All five adiposity indices (i.e. BMI, WC, HC, WHR, and BAI) were significantly correlated with FSI, HOMA-IR, TG, and TG/ HDL. Comparison of these adiposity indices in the strength of their correlations with CVD variables revealed that WC had the best correlation with HOMA-IR (r = 0.629), TC/ HDL (r = 0.315), and TG/HDL (r = 0.446). On the other hand, WHR was the only adiposity index showing significantly association with SBP (r = 0.352) and DBP (r = 0.366) in males. The relationships of anthropometric adiposity indices with CVD risk factors were found to be gender dependent. As observed in Table 3, BMI, WC, HC, WHR, and BAI were significantly correlated with all CVD risk factors, except for LDL and TC in female participants. Amongst them, the Pearson's correlation coefficients of BAI with HOMA-IR (r = 0.593), TC/HDL (r = 0.522), TG/ HDL (r = 0.542), LDL/HDL (r = 0.497), SBP (r = 0.474), and DBP (r = 0.448) were the strongest.

As a direct measurement of body adiposity, PBF was assessed in the strength of its associations with CVD risk factors as presented in Table 4 (males and females separately) and Additional file 1: Table S1 (all participants). Table 4 shows that PBF, measured by three different techniques, shared a comparable pattern of correlation for both genders. We found that PBF was significantly correlated with all CVD risk factors except for TC in female participants. No significant correlations were observed between PBF and FBG, LDL, TC, SBP, and DBP in males.

We compared the correlation strength of PBF derived from BOD POD and anthropometric adiposity indices with the CVD risk factors in the current study population ( Fig. 1 for males and Fig. 2 for females). Figure 1 shows that WC outperformed PBF in the strength of its correlation with HOMA-IR and TG/HDL and WHR had significant correlations with SBP and DBP, but not PBF in male participants. Figure 2 shows that BAI had a comparable correlation with CVD risk factors as PBF in female participants. The comparison of five anthropometric adiposity indices in their strength of correlations with CVD risk factors are shown in Additional file 1: Figures S1 and S2.

Discussion

Although the advanced instrumentations, such as DEXA and MRI, have been employed to measure PBF accurately, they are not readily available in most epidemiological studies due to the high cost and complexity. In contrast, anthropometric obesity measurements that reflect body fat are of importance in clinical settings because they require less expense and expertise. Although several studies have successfully included the anthropometric indices of body adiposity in the assessment of CVD risk factors [19][20][21][22], the best measure to predict these risk factors remained contentious, especially in Asians. Previous studies suggested that central obesity measures were more strongly associated with CVD risk factors compared with general obesity measures [21]. This is probably because central obesity is associated with systemic inflammation which directly contributes to CVD risk [23]. Conversely, some other studies, like ours, reported that the association between general obesity and CVD was similar to the association between central obesity measures and CVD [24,25]. We found that both general obesity (BMI) and central obesity (WC) correlated  significantly more with seven of the CVD risk factors (FBG, FSI, HOMA-IR, TG, TC/HDL, TG/HDL, LDL/ HDL) than HC when all participants were considered together. Moreover, WHR correlated the best with HDL, SBP, and DBP (Additional file 1: Table S1). BAI was recently proposed to offer a simple-to-use tool to estimate body adiposity [26]. The assessment of BAI's association with CVD risk factors has been done in various populations with different ethnicities showing that BAI was inferior to other anthropometric measures such as BMI and WC as a predictor of CVD risk factors [20,[27][28][29][30]. This present study shows that BAI and PBF shared a comparable correlation pattern, suggesting that BAI, as a measure of overall adiposity, seemed to correlate with PBF better than other anthropometric indices. However, there was no case in which BAI and PBF outperformed these indices with regards to their correlation with several CVD risk factors including HDL, TC/HDL, LDL/HDL, SBP, and DBP in the gender-pooled analyses. This is consistent with prior investigation of body composition and health risk showing that PBF did not perform better than BMI or WC in predicting metabolic risks [31]. Although BAI is related to PBF, it may not be a good indicator of CVD risks as observed in the current population. A circumspect approach may be to use WC and WHR in routine clinical assessment since they are  easy to measure and less prone to measurement and calculation errors [32].

An intriguing result of this study was the pronounced gender difference in the correlations of body adiposity measures with CVD risk factors. For male participants, central obesity (WC and WHR) was more strongly associated with CVD risk factors, whereas BAI appeared to be a better predictor of CVD risk factors in female participants (Tables 2 and 3). This may be attributed to the different distribution of adipose tissue in males and females. Compared to men who store more fat in the visceral depot, women store them in the gluteal-femoral region [33,34]. Therefore, women had less visceral fat despite having a higher total body fat [35]. While some epidemiological studies have concluded that visceral adipose tissue was a stronger correlate of CVD [36,37], others have argued that subcutaneous adipose tissue may have protective effects [38,39]. It has thus been hypothesized that the different fat distribution in males and females may contribute to the different correlation of anthropometric obesity indices with CVD risk factors.

Conclusions

In conclusion, our study reports that several simple anthropometric measurements of obesity, i.e. BAI, WC, and WHR, outperform the measurement of PBF in the prediction of CVD risk factors in healthy males and females living in Singapore. Our data suggests that the correlations between obesity indices and CVD risks are gender-dependent. While measures of central adiposity show stronger associations with CVD risk factors in males, measures of general adiposity show stronger associations in females.

Our study has several limitations that warrant further investigation. This is a cross sectional study and thus causal inferences cannot be drawn. Moreover, the sample size of this study is small. Future studies should be conducted with larger sample sizes. Additionally, although HOMA-IR has become a widely used clinical and epidemiological tool, it is not a direct measurement of insulin resistance. Therefore, the use of HOMA-IR to assess insulin resistance may have potential problems and needs further validation. Despite these limitations, the results of our study provide evidence of the linkage between simple anthropometric measurements and the purported associations between adiposity and health markers.

Additional file

Additional file 1: Supporting information. Table S1. Correlation coefficients of anthropometric measurements and PBF with cardiovascular risk factors for all participants. Figure S1. Correlation coefficients between different anthropometric measurements and CVD risk factors in female participants. Figure S2. Correlation coefficients between different anthropometric measurements and CVD risk factors in male participants. (DOCX 25 kb) Abbreviations BAI, body adiposity index; BIA, bioelectrical impedance analysis; BMI, body mass index; CVD, cardiovascular disease; CT, computed tomography; DBP, diastolic blood pressure; DEXA, dual-energy X-ray absorptiometry; DM, diabetes mellitus; FBG, fasting blood glucose; FSI, fasting serum insulin; HC, hip circumference; HDL, high density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; LDL, low density lipoprotein; MRI, magnetic resonance imaging; PBF, percent body fat; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; WC, waist circumference; WHR, waist-to-hip ratio

Fig. 1
1Comparison of the correlation strength of PBF derived from BOD POD and WC or WHR with the CVD risk factors, e.g. HOMA-IR, TG/HDL, SBP, and DBP, in male participants (n = 63)

Table 1
1Clinical characteristics of the study population Total (n = 125) Male (n = 63) Female (n = 62) p value BAI body adiposity index, BIA bioelectrical impedance analysis, BMI body mass index, DBP diastolic blood pressure, DEXA dual-energy X-ray absorptiometry, FBG fasting blood glucose, FSI fasting serum insulin, HC hip circumference, HDL high density lipoprotein, HOMA-IR homeostasis model assessment of insulin resistance, LDL low density lipoprotein, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, WC waist circumference, WHR waist-to-hip ratioAge (y)
31.4 ± 12.2
30.6 ± 11.6
32.2 ± 12.8
0.452

Height (cm)
166.5 ± 9.0
172.4 ± 6.8
160.6 ± 6.9
<0.001

Weight (kg)
63.3 ± 13.9
70.3 ± 11.9
56.2 ± 12.2
<0.001

BMI (kg/m 2 )
22.7 ± 3.9
23.6 ± 3.8
21.7 ± 3.9
0.006

WC (cm)
73.5 ± 10.4
78.0 ± 9.4
68.9 ± 9.4
<0.001

HC (cm)
90.8 ± 7.5
91.7 ± 7.1
89.9 ± 7.8
0.196

WHR
0.81 ± 0.07
0.85 ± 0.06
0.76 ± 0.06
<0.001

PBF a
24.1 ± 8.4
18.8 ± 5.8
29.5 ± 7.2
<0.001

PBF b
25.2 ± 9.4
20.1 ± 7.8
30.4 ± 7.9
<0.001

PBF c
29.2 ± 8.3
23.5 ± 5.6
35.2 ± 6.1
<0.001

BAI
24.4 ± 4.1
22.6 ± 3.5
26.2 ± 3.8
<0.001

SBP (mm Hg)
111.5 ± 15.8
117.7 ± 10.4
105.3 ± 17.8
<0.001

DBP (mm Hg)
66.1 ± 9.2
67.7 ± 8.6
64.4 ± 9.5
0.046

FBG (mmol/L)
4.6 ± 0.5
4.6 ± 0.5
4.5 ± 0.5
0.313

FSI (mU/L)
8.3 ± 5.3
7.9 ± 5.6
8.8 ± 4.9
0.349

HOMA-IR
1.7 ± 1.2
1.6 ± 1.3
1.8 ± 1.2
0.474

TG (mmol/L)
0.86 ± 0.44
0.89 ± 0.40
0.83 ± 0.49
0.472

TC (mmol/L)
5.0 ± 1.4
5.0 ± 1.8
5.1 ± 0.9
0.666

HDL (mmol/L)
1.6 ± 0.4
1.5 ± 0.3
1.7 ± 0.4
0.001

LDL (mmol/L)
3.1 ± 1.2
3.1 ± 1.6
3.0 ± 0.8
0.777

TC/HDL
3.3 ± 1.1
3.5 ± 1.2
3.2 ± 1.0
0.056

TG/HDL
0.62 ± 0.51
0.67 ± 0.45
0.57 ± 0.57
0.277

LDL/HDL
2.1 ± 0.9
2.2 ± 1.0
1.9 ± 0.8
0.051

Values are expressed as mean ± SD. p values indicate results of independent
samples t tests between gender groups
PBF was measured by a BIA, b BOD POD, and c DEXA, respectively
Abbreviations:

Table 2
2Pearson's correlation coefficients of anthropometric measurements with cardiovascular risk factors for male participants (n = 63) BAI body adiposity index, BMI body mass index, DBP diastolic blood pressure, FBG fasting blood glucose, FSI fasting serum insulin, HC hip circumference, HDL high density lipoprotein, HOMA-IR homeostasis model assessment of insulin resistance, LDL low density lipoprotein, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, WC waist circumference, WHR waist-to-hip ratioBMI (kg/m 2 ) WC (cm) HC (cm) WHR
BAI

FBG (mmol/L)
0.097
0.145
0.108
0.124
0.048

FSI (mU/L)
0.529**
0.628**
0.544**
0.471**
0.403**

HOMA-IR
0.534**
0.629**
0.548**
0.464**
0.408**

TG (mmol/L)
0.411**
0.412**
0.407**
0.264*
0.424**

HDL (mmol/L)
−0.267*
−0.374** −0.297* −0.330* −0.174

LDL (mmol/L)
0.022
0.049
0.071
0.007
0.048

TC (mmol/L)
−0.005
−0.001
0.029
−0.034
0.038

TC/HDL
0.238
0.315*
0.308*
0.210
0.231

TG/HDL
0.428**
0.446**
0.410**
0.322*
0.445**

LDL/HDL
0.209
0.290*
0.292*
0.186
0.200

SBP (mm Hg)
0.138
0.228
0.060
0.352*
0.066

DBP (mm Hg)
0.076
0.200
−0.001
0.366** −0.007

*Correlation is significant at p < 0.05
**Correlation is significant at p < 0.005
Abbreviations:

Table 3
3BAI body adiposity index, BMI body mass index, DBP diastolic blood pressure, FBG fasting blood glucose, FSI fasting serum insulin, HC hip circumference, HDL high density lipoprotein, HOMA-IR homeostasis model assessment of insulin resistance, LDL low density lipoprotein, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, WC waist circumference, WHR waist-to-hip ratioPearson's correlation coefficients of anthropometric
measurements with cardiovascular risk factors for female
participants (n = 62)

BMI (kg/m 2 ) WC (cm) HC (cm) WHR
BAI

FBG (mmol/L)
0.444**
0.454**
0.341*
0.420**
0.452**

FSI (mU/L)
0.546**
0.549**
0.466**
0.438**
0.563**

HOMA-IR
0.559**
0.561**
0.465**
0.460**
0.593**

TG (mmol/L)
0.384**
0.417**
0.289*
0.406**
0.541**

HDL (mmol/L)
−0.419**
−0.398** −0.228
−0.442** −0.509**

LDL (mmol/L)
0.177
0.176
0.214
0.074
0.202

TC (mmol/L)
0.071
0.085
0.161
−0.028
0.075

TC/HDL
0.419**
0.425**
0.304*
0.405**
0.522**

TG/HDL
0.401**
0.422**
0.272*
0.432**
0.542**

LDL/HDL
0.399**
0.402**
0.294*
0.376**
0.497**

SBP (mm Hg)
0.403**
0.385**
0.283*
0.369**
0.474**

DBP (mm Hg)
0.452**
0.433**
0.363**
0.346*
0.448**

*Correlation is significant at p < 0.05
**Correlation is significant at p < 0.005
Abbreviations:

Table 4
4Pearson's correlation coefficients of PBF with cardiovascular risk factors for male (n = 63) and female (n = 62) participants PBF was measured by a BIA, b BOD POD, and c DEXA, respectively Abbreviations: BIA bioelectrical impedance analysis, DBP diastolic blood pressure, DEXA dual-energy X-ray absorptiometry, FBG fasting blood glucose, FSI fasting serum insulin, HDL high density lipoprotein, HOMA-IR homeostasis model assessment of insulin resistance, LDL low density lipoprotein, PBF percent body fat, SBP systolic blood pressure, TC total cholesterol, TG triglyceridesPBF a
PBF b
PBF c

Male
Female
Male
Female
Male
Female

FBG (mmol/L)
0.087
0.482**
0.015
0.506**
0.041
0.462**

FSI (mU/L)
0.595**
0.589**
0.547**
0.536**
0.524**
0.542**

HOMA-IR
0.581**
0.601**
0.526**
0.583**
0.504**
0.574**

TG (mmol/L)
0.435**
0.426**
0.327*
0.482**
0.300*
0.442**

HDL (mmol/L)
−0.462**
−0.435**
−0.400**
−0.359**
−0.356*
−0.352*

LDL (mmol/L)
0.118
0.255*
0.257*
0.370**
0.195
0.311*

TC (mmol/L)
0.053
0.151
0.191
0.297*
0.138
0.248

TC/HDL
0.432**
0.488**
0.500**
0.519**
0.437**
0.474**

TG/HDL
0.490**
0.440**
0.401**
0.438**
0.361**
0.401**

LDL/HDL
0.405**
0.466**
0.484**
0.501**
0.423**
0.451**

SBP (mm Hg)
0.130
0.452**
0.085
0.462**
0.081
0.408**

DBP (mm Hg)
0.146
0.491**
0.139
0.517**
0.100
0.471**

*Correlation is significant at p < 0.05
**Correlation is significant at p < 0.005

AcknowledgementsThe authors greatly thank Ms. Susanna Lim and Ms. Cathy Mok for help with venipuncture.FundingNot applicable.Availability of data and materials Data supporting our findings can be sent by corresponding author upon request. This study was conducted at the CNRC, Singapore Institute for Clinical Sciences (SICS), Singapore, according to the guidelines laid down in the Declaration of Helsinki. All procedures involving human subjects were approved by the National Healthcare Group Domain Specific Review Board (NHG DSRB, Reference Number: 2013/00783), Singapore. All participants gave written informed consent prior to participation.Authors' contributionsThe authors' responsibilities were as follows: CJH designed the study; XB, SLT, CL, RQ, and YTL conducted experiments and collected data; XB and SLTCompeting interestsThe authors declare that they have no competing interests.Consent for publicationNot applicable.Ethics approval and consent to participate
Epidemic obesity and type 2 diabetes in Asia. K H Yoon, J H Lee, J W Kim, J H Cho, Y H Choi, S H Ko, P Zimmet, H Y Son, Lancet. 3689548Yoon KH, Lee JH, Kim JW, Cho JH, Choi YH, Ko SH, Zimmet P, Son HY. Epidemic obesity and type 2 diabetes in Asia. Lancet. 2006;368(9548):1681-8.

Mechanisms linking obesity with cardiovascular disease. L F Van Gaal, Nature. 4447121Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006;444(7121):875-80.

Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. A G Renehan, M Tyson, M Egger, R F Heller, M Zwahlen, Lancet. 3719612Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet. 2008;371(9612):569-78.

Body fat distribution and risk of cardiovascular disease: An update. J P Despres, Circulation. 12610Despres JP. Body fat distribution and risk of cardiovascular disease: An update. Circulation. 2012;126(10):1301-13.

Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. J P Despres, S Moorjani, P J Lupien, A Tremblay, A Nadeau, C Bouchard, Arteriosclerosis. 104Despres JP, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990;10(4):497-511.

Pathophysiology of human visceral obesity: An update. A Tchernof, J P Despres, Physiol Rev. 931Tchernof A, Despres JP. Pathophysiology of human visceral obesity: An update. Physiol Rev. 2013;93(1):359-404.

Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. V Messier, A D Karelis, D Prud'homme, V Primeau, M Brochu, R Rabasa-Lhoret, Obesity (Silver Spring). 185Messier V, Karelis AD, Prud'homme D, Primeau V, Brochu M, Rabasa-Lhoret R. Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. Obesity (Silver Spring). 2010;18(5):911-7.

Metabolically active components of fat-free mass and resting energy expenditure in humans: Recent lessons from imaging technologies. M J Muller, A Bosy-Westphal, D Kutzner, M Heller, Obes Rev. 32Muller MJ, Bosy-Westphal A, Kutzner D, Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: Recent lessons from imaging technologies. Obes Rev. 2002;3(2):113-22.

Mortality, health outcomes, and body mass index in the overweight range: A science advisory from the. C E Lewis, K M Mctigue, L E Burke, P Poirier, R H Eckel, B V Howard, D B Allison, S Kumanyika, F X Pi-Sunyer, American Heart Association. Circulation. 11925Lewis CE, McTigue KM, Burke LE, Poirier P, Eckel RH, Howard BV, Allison DB, Kumanyika S, Pi-Sunyer FX. Mortality, health outcomes, and body mass index in the overweight range: A science advisory from the American Heart Association. Circulation. 2009;119(25):3263-71.

Body mass index can similarly predict the presence of multiple cardiovascular risk factors in middle-aged Japanese subjects as waist circumference. H Satoh, R Kishi, H Tsutsui, Intern Med. 4911Satoh H, Kishi R, Tsutsui H. Body mass index can similarly predict the presence of multiple cardiovascular risk factors in middle-aged Japanese subjects as waist circumference. Intern Med. 2010;49(11):977-82.

Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. H J Schneider, H Glaesmer, J Klotsche, S Bohler, H Lehnert, A M Zeiher, W Marz, D Pittrow, G K Stalla, H U Wittchen, J Clin Endocrinol Metab. 922DETECT study groupSchneider HJ, Glaesmer H, Klotsche J, Bohler S, Lehnert H, Zeiher AM, Marz W, Pittrow D, Stalla GK, Wittchen HU, DETECT study group. Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab. 2007;92(2):589-94.

Raceethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. S Zhu, S B Heymsfield, H Toyoshima, Z Wang, A Pietrobelli, S Heshka, Am J Clin Nutr. 812Zhu S, Heymsfield SB, Toyoshima H, Wang Z, Pietrobelli A, Heshka S. Race- ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. Am J Clin Nutr. 2005;81(2):409-15.

Obesity in the elderly and its relationship with cardiovascular risk factors in Taiwan. K C Huang, M S Lee, S D Lee, Y H Chang, Y C Lin, S H Tu, W H Pan, Obes Res. 131Huang KC, Lee MS, Lee SD, Chang YH, Lin YC, Tu SH, Pan WH. Obesity in the elderly and its relationship with cardiovascular risk factors in Taiwan. Obes Res. 2005;13(1):170-8.

Waist circumference and not body mass index explains obesity-related health risk. I Janssen, P T Katzmarzyk, R Ross, Am J Clin Nutr. 793Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379-84.

Visceral adiposity and risk of type 2 diabetes: A prospective study among Japanese Americans. E J Boyko, W Y Fujimoto, D L Leonetti, L Newell-Morris, Diabetes Care. 234Boyko EJ, Fujimoto WY, Leonetti DL, Newell-Morris L. Visceral adiposity and risk of type 2 diabetes: A prospective study among Japanese Americans. Diabetes Care. 2000;23(4):465-71.

Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. J M Chan, E B Rimm, C A Colditz, M J Stampfer, W C Willett, Diabetes Care. 179Chan JM, Rimm EB, Colditz CA, Stampfer MJ, Willett WC. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care. 1994;17(9):961-9.

Absolute fat mass, percent body fat, and body-fat distribution: Which is the real determinant of blood pressure and serum glucose?. D Spiegelman, R G Israel, C Bouchard, W C Willett, Am J Clin Nutr. 556Spiegelman D, Israel RG, Bouchard C, Willett WC. Absolute fat mass, percent body fat, and body-fat distribution: Which is the real determinant of blood pressure and serum glucose? Am J Clin Nutr. 1992;55(6):1033-44.

International standards for anthropometric assessment. The International Society for the Advancement of Kinanthropometry. ISAK, International standards for anthropometric assessment. Published by The International Society for the Advancement of Kinanthropometry.

Body adiposity index versus body mass index and other anthropometric traits as correlates of cardiometabolic risk factors. C T Lichtash, J Cui, X Guo, Ydi Chen, W A Hsueh, J I Rotter, M O Goodarzi, PLoS ONE. 8665954Lichtash CT, Cui J, Guo X, Chen YDI, Hsueh WA, Rotter JI, Goodarzi MO. Body adiposity index versus body mass index and other anthropometric traits as correlates of cardiometabolic risk factors. PLoS ONE. 2013;8(6):e65954.

Relationship between the body adiposity index and cardiometabolic risk factors in obese postmenopausal women. B Elisha, R Rabasa-Lhoret, V Messier, J Abdulnour, A D Karelis, Eur J Nutr. 521Elisha B, Rabasa-Lhoret R, Messier V, Abdulnour J, Karelis AD. Relationship between the body adiposity index and cardiometabolic risk factors in obese postmenopausal women. Eur J Nutr. 2013;52(1):145-51.

Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study. Lgh Goh, S S Dhaliwal, T A Welborn, A H Lee, P R Della, BMJ Open. 424138Goh LGH, Dhaliwal SS, Welborn TA, Lee AH, Della PR. Anthropometric measurements of general and central obesity and the prediction of cardiovascular disease risk in women: a cross-sectional study. BMJ Open. 2014;4(2):e004138.

Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. Bcc Lam, Gch Koh, C Chen, M Tack, K Wong, S J Fallows, PLoS ONE. 104122985Lam BCC, Koh GCH, Chen C, Tack M, Wong K, Fallows SJ. Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLoS ONE. 2015;10(4):e0122985.

Adipose tissue, inflammation, and cardiovascular disease. A H Berg, P E Scherer, Circ Res. 969Berg AH, Scherer PE. Adipose tissue, inflammation, and cardiovascular disease. Circ Res. 2005;96(9):939-49.

Comparison of the associations of body mass index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: A study using data from 4 UK cohorts. A E Taylor, S Ebrahim, Y Ben-Shlomo, R M Martin, P H Whincup, J W Yarnell, S G Wannamethee, D A Lawlor, Am J Clin Nutr. 913Taylor AE, Ebrahim S, Ben-Shlomo Y, Martin RM, Whincup PH, Yarnell JW, Wannamethee SG, Lawlor DA. Comparison of the associations of body mass index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: A study using data from 4 UK cohorts. Am J Clin Nutr. 2010;91(3):547-56.

Body mass index and waist circumference predict both 10-year nonfatal and fatal cardiovascular disease risk: Study conducted in 20,000 Dutch men and women aged 20-65 years. I Van Dis, D Kromhout, J M Geleijnse, J M Boer, W M Verschuren, Eur J Cardiovasc Prev Rehabil. 166van Dis I, Kromhout D, Geleijnse JM, Boer JM, Verschuren WM. Body mass index and waist circumference predict both 10-year nonfatal and fatal cardiovascular disease risk: Study conducted in 20,000 Dutch men and women aged 20-65 years. Eur J Cardiovasc Prev Rehabil. 2009;16(6):729-34.

A better index of body adiposity. R N Bergman, D Stefanovski, T A Buchanan, A E Sumner, J C Reynolds, N G Sebring, A H Xiang, R M Watanabe, Obesity (Silver Spring). 195Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, Xiang AH, Watanabe RM. A better index of body adiposity. Obesity (Silver Spring). 2011;19(5):1083-9.

Body adiposity index, body fat content and incidence of type 2 diabetes. M B Schulze, B Thorand, A Fritsche, H U Häring, F Schick, A Zierer, W Rathmann, J Kröger, A Peters, H Boeing, N Stefan, Diabetologia. 556Schulze MB, Thorand B, Fritsche A, Häring HU, Schick F, Zierer A, Rathmann W, Kröger J, Peters A, Boeing H, Stefan N. Body adiposity index, body fat content and incidence of type 2 diabetes. Diabetologia. 2012;55(6):1660-7.

Body adiposity index indicates only total adiposity, not risk. Obesity (Silver Spring). J G De Lima, L H Nobrega, A B De Souza, 201140de Lima JG, Nobrega LH, de Souza AB. Body adiposity index indicates only total adiposity, not risk. Obesity (Silver Spring). 2012;20(6):1140.

Newly proposed body adiposity index (BAI) by Bergman et al. is not strongly related to cardiovascular health risk. M B Snijder, M Nicolaou, I G Van Valkengoed, L M Brewster, K Stronks, Obesity (Silver Spring). 206Snijder MB, Nicolaou M, van Valkengoed IG, Brewster LM, Stronks K. Newly proposed body adiposity index (BAI) by Bergman et al. is not strongly related to cardiovascular health risk. Obesity (Silver Spring). 2012;20(6):1138-9.

Is the body adiposity index (hip circumference/height1.5) more strongly related to skinfold thickness and risk factor levels than is BMI? The Bogalusa Heart Study. D S Freedman, H M Blanck, W H Dietz, P Dasmahapatra, S R Srinivasan, G S Berenson, Br J Nutr. 1092Freedman DS, Blanck HM, Dietz WH, DasMahapatra P, Srinivasan SR, Berenson GS. Is the body adiposity index (hip circumference/height1.5) more strongly related to skinfold thickness and risk factor levels than is BMI? The Bogalusa Heart Study. Br J Nutr. 2013;109(2):338-45.

Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. A Bosy-Westphal, C Geisler, S Onur, O Korth, O Selberg, J Schrezenmeir, M J Müller, Int J Obes (Lond). 303Bosy-Westphal A, Geisler C, Onur S, Korth O, Selberg O, Schrezenmeir J, Müller MJ. Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes (Lond). 2006;30(3):475-83.

A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of cardiovascular risk factors. The Canadian Heart Health Surveys. C J Dobbelsteyn, M R Joffres, D R Maclean, G Flowerdew, Int J Obes Relat Metab Disord. 255Dobbelsteyn CJ, Joffres MR, MacLean DR, Flowerdew G. A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of cardiovascular risk factors. The Canadian Heart Health Surveys. Int J Obes Relat Metab Disord. 2001;25(5):652-61.

Gender differences in fat metabolism. E Blaak, Curr Opin Clin Nutr Metab Care. 46Blaak E. Gender differences in fat metabolism. Curr Opin Clin Nutr Metab Care. 2001;4(6):499-502.

Sex differences in human adipose tissues-the biology of pear shape. K Karastergiou, S R Smith, A S Greenberg, S K Fried, Biol Sex Differ. 313Karastergiou K, Smith SR, Greenberg AS, Fried SK. Sex differences in human adipose tissues-the biology of pear shape. Biol Sex Differ. 2012;3:13.

Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. S Lemieux, D Prud'homme, C Bouchard, A Tremblay, J P Despres, Am J Clin Nutr. 584Lemieux S, Prud'homme D, Bouchard C, Tremblay A, Despres JP. Sex differences in the relation of visceral adipose tissue accumulation to total body fatness. Am J Clin Nutr. 1993;58(4):463-7.

Abdominal visceral and subcutaneous adipose tissue compartments: Association with metabolic risk factors in the Framingham Heart Study. Circulation. C S Fox, J M Massaro, U Hoffmann, K M Pou, P Maurovich-Horvat, C Y Liu, R S Vasan, J M Murabito, J B Meigs, L A Cupples, D'agostino Rb Sr, C J O'donnell, 116Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D'Agostino RB Sr, O'Donnell CJ.. Abdominal visceral and subcutaneous adipose tissue compartments: Association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116(1):39-48.

Impact of abdominal visceral and subcutaneous adipose tissue on cardiometabolic risk factors: The Jackson Heart Study. J Liu, C S Fox, D A Hickson, W D May, K G Hairston, J J Carr, H A Taylor, J Clin Endocrinol Metab. 9512Liu J, Fox CS, Hickson DA, May WD, Hairston KG, Carr JJ, Taylor HA. Impact of abdominal visceral and subcutaneous adipose tissue on cardiometabolic risk factors: The Jackson Heart Study. J Clin Endocrinol Metab. 2010;95(12):5419-26.

Beneficial effects of subcutaneous fat transplantation on metabolism. T T Tran, Y Yamamoto, S Gesta, C R Kahn, Cell Metab. 75Tran TT, Yamamoto Y, Gesta S, Kahn CR. Beneficial effects of subcutaneous fat transplantation on metabolism. Cell Metab. 2008;7(5):410-20.

Abdominal subcutaneous adipose tissue: A protective fat depot? Diabetes Care. S A Porter, J M Massaro, U Hoffmann, R S Vasan, C J O'donnel, C S Fox, 32Porter SA, Massaro JM, Hoffmann U, Vasan RS, O'Donnel CJ, Fox CS. Abdominal subcutaneous adipose tissue: A protective fat depot? Diabetes Care. 2009;32(6):1068-75.