Anne Buot
email: anne.buot@gmail.com

Damiano Azzalini

Maximilien Chaumon

Catherine Tallon-Baudry

Does stroke volume influence heartbeat evoked responses?

Keywords: Interoception, Heartbeat-evoked response, stroke volume, baroreceptors

We know surprisingly little on how heartbeat-evoked responses (HERs) vary with cardiac parameters. Here, we measured both stroke volume, or volume of blood ejected at each heartbeat, with impedance cardiography, and HER amplitude with magnetoencephalography, in 21 male and female participants at rest with eyes open. We observed that HER co-fluctuates with stroke volume on a beat-to-beat basis, but only when no correction for cardiac artifact was performed. This highlights the importance of an ICA correction tailored to the cardiac artifact. We also observed that easy-to-measure cardiac parameters (interbeat intervals, ECG amplitude) are sensitive to stroke volume fluctuations and can be used as proxies when stroke volume measurements are not available. Finally, interindividual differences in stroke volume were reflected in MEG data, but whether this effect is locked to heartbeats is unclear. Altogether, our results question assumptions on the link between stroke volume and HERs.

Highlights (3-5 bullet points, 85char max)

• Beat-to-beat fluctuations in stroke volume mostly affect the cardiac/pulse artifact • A specific ICA correction strongly attenuates the influence of stroke volume on HER • Interbeat intervals and ECG amplitude are sensitive to stroke volume fluctuations • Inter-individual differences in stroke volume affect MEG data

Introduction

Interoception refers to how the brain perceives and represents visceral signalswhether consciously or unconsciously (Azzalini et al., 2019). In the field of cardiac interoception, the heartbeat-evoked response (HER) is used to quantify how the brain transiently responds to an incoming interoceptive signal, the heartbeat. HERs correspond to electrophysiological (EEG or MEG) data averaged locked to one peak of the electrocardiogram, for instance the R peak that corresponds to ventricular contraction.

Simultaneously revealed in 1986 by the group of Schandry (1986) and Jones (1986), HERs have attracted a lot of interest in the recent years as they offer a window on heart-to-brain communication and its potential role in perception, emotion, cognition and consciousness (for reviews: Azzalini et al., 2019;Coll et al., 2021;Park and Blanke, 2019;Park and Tallon-Baudry, 2014). However, despite this growing interest, little is known about the origin of HERs and their dependence on cardiac parameters.

HERs are responses to an internal, cyclical neuro-muscular event resulting in blood ejection. The volume of blood ejected from the left ventricle at each cardiac cycle is called the stroke volume. Blood ejection transiently stretches the walls of the aorta and carotid arteries, inducing neural activity in baroreceptors at each cardiac cycle (Charkoudian et al., 2005;Vaschillo et al., 2012). It is often assumed that HERs are caused by the transient aortic and carotid baroreceptor responses to blood ejection, with some delays due to transduction and neural conduction times (Garfinkel and Critchley, 2016;Gray et al., 2007). Under this hypothesis, HER amplitude should co-vary with stroke volume on a beat-to-beat basis.

However, some cardiac mechanoreceptors discharge at each heartbeat but appear insensitive to stroke volume (Bishop et al., 1983). It thus seems equally plausible that HERs are independent from stroke volume. The primary aim of this paper is to test how beat-tobeat fluctuations in stroke volume affect HERs. To that aim, we measured beat-to-beat fluctuations in stroke volume using impedance cardiography (Figure 1) while also recording the electrocardiogram (ECG) and magnetoencephalographic (MEG) data in 21 young healthy participants at rest with eyes open.

A specificity of neural responses to heartbeats measured non-invasively is that they are contaminated by the cardiac artifact, because MEG and EEG sensors detect the electrical activity of the heart itself (Jousmäki and Hari, 1996;Dirlich et al., 1997). We thus have to consider a third hypothesis: HER amplitude measured non-invasively is related to stroke volume, but this effect is mediated by a modulation of cardiac electrical activity detected by scalp sensors, rather than by mechanoreceptor discharges in the heart or blood vessels that are relayed, with some delay, to the brain. In practice, Independent component analysis (ICA, Jung et al., 2001;Makeig et al., 1996) is efficient to attenuate, but probably not to remove completely, the cardiac artifact in MEG or EEG data. We systematically analysed the contribution of beat-to-beat fluctuations in stroke volume to HERs in ICA-uncorrected as well as ICA-corrected MEG data.

Additionally, two previous EEG studies suggested that interindividual differences in stroke volume (Schandry and Montoya, 1996) or a closely related measure (Gray et al., 2007) were reflected in interindividual differences in HERs. We thus also analysed whether interindividual differences in stroke volume contribute to interindividual differences in HERs measured with MEG.

Finally, most recent studies relating HERs with perceptual, emotional or cognitive variables compared HERs between two experimental conditions, observed HERs differences and verified that neither the electrocardiogram (ECG) nor parameters derived from the ECG such as inter-beat interval (IBI) and inter-beat interval variability differed between conditions. The difference in HERs along perceptual, emotional or cognitive variables was thus attributed to a difference in the neural processing of heartbeat related to the task at hand, rather than to a difference in cardiac input (e.g., Babo-Rebelo et al., 2019, 2016;Canales-Johnson et al., 2015;Müller et al., 2015;Park and Tallon-Baudry, 2014;Pollatos and Schandry, 2004;Schulz et al., 2015;Sel et al., 2017;Petzschner et al., 2019). However, stroke volume was not measured in those experiments -and is unlikely to be routinely measured, because it requires dedicated equipment and the injection of a high frequency current. We thus investigated the link between stroke volume and cardiac parameters commonly acquired, such as ECG amplitude and interbeat intervals, to determine whether they are sensitive to fluctuations in stroke volume and can be used as proxies to test whether stroke volume varies between experimental conditions.

Materials and Methods

Participants

24 right-handed volunteer were included in the study after giving written informed consent and were paid for their participation. The study was approved by the ethics committee CPP Ile de France III. Inclusion criteria were: age between 18 and 30 years old, right-handedness, no psychiatric or neurological disorder, no cardiac disorder, no ongoing medical treatment, normal or corrected vision, as well as absence of claustrophobia or the absence of metallic objects in the body to allow MEG recordings. Three participants were excluded from the study for extra systole (n=1), noisy impedance cardiography data (n=1) and outlying stroke volume values (n=1). The 21 remaining participants (10 males; age: mean±std = 23±2.14 years, range [19 28]) were included in the analysis.

Procedure

Five minutes recordings were acquired during resting-state with eyes open.

Participants were seated in front of a grey screen with a black fixation point. They were asked to remain silent, to relax, to avoid large eye or body movement, as well as repetitive physical or mental activity such as finger taping or counting.

Recordings

Continuous MEG data were acquired using a whole-head MEG system with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX, Elekta AB, Stockholm, Sweden; sampling rate of 1000 Hz, online low-pass filtered at 330 Hz). The ground electrode was located on the left costal margin.

ECG data were acquired on a separate amplifier (BIOPAC Systems, Inc., Goleta, United States; sampling rate of 1000 Hz, online band-pass filters 0.05 to 35 Hz) using 4 electrodes placed around the base of the neck (2 electrodes over the left and right clavicles, 2 electrodes above the left and right supraspinatus muscle) and referenced to a left abdominal location. Three different leads (I, II and III) were computed offline (Figure 1A). Impedance cardiography data were acquired using the Biopac EBI100C module using four spot electrodes (Figure 1A;Sherwood(Chair) et al., 1990). Two sources electrodes placed on the left side of the abdomen and neck, separated by a distance L (range [27 34cm]), were used to inject a low intensity (400 μA rms) high frequency (12.5 kHz) current.

to measure the voltage across the tissue. The distance L, which is important when computing SV based on impedance cardiography data, was precisely measured in each subject. For the validity of SV values obtained using impedance cardiography, see Raaijmakers et al., 1999. Cardiac sounds were recorded by placing an a-magnetic homemade microphone (online band-pass filter 0.05-300 Hz) on the chest of the subject.

Gaze location was monitored using an eye-tracker (EyeLink 1000, SR Research Ltd., Mississauga, Canada). A two-step procedure was used to label the R peaks of the ECG: We first created a subject-specific template QRS complex, and then used this template to detect all R peaks. To create the template, a band-pass filtered (1-100 Hz) version of the ECG was submitted to threshold detection, such that enough (>20) R peaks would be detected. The average QRS complex was computed as the average ECG signal locked to these peak detections. The full list of R-peaks was then detected by correlating the ECG with the template QRS complex and identifying the local maximum within the episodes of correlation >0.6. R-peak detection was visually verified in all participants. Q-peak was then identified as the minimum within 50ms before the R-peak, S-peak as the minimum within 100ms post R-peak, and T-wave as the maximum in the interval [S S+0.4s]. IBI consisted of the average time distance between two R-peaks and the heart rate variability corresponded to the standard deviation of the IBIs.

SV computation

For each participant, we calculated the beat-to-beat SV from the impedance cardiography data using the following Kubicek's formula (Kubicek et al., 1970):

!" = $ℎ& × ( ) *& ) × ("+, × - .* ./ 0 123
• Rho is blood resistivity, considered here to be 135 ohms.cm (Berntson et al., 2007).

Note that blood resistivity is taken here to be a constant, although it might vary from one participant to the other, and depend on current injection frequency (Berntson et al., 2007).

• L is the distance in cm between the two recordings electrodes

• Z0 is the average impedance, in ohm, during the left ventricular ejection

• LVET is the left ventricular ejection time in second.

• (dZ/dt)max is the maximum value of the first derivative of the impedance measure in ohm/sec.

To estimate LVET (duration of the left ventricular ejection) at each beat, we identified for each heartbeat, the beginning (B-point) and end (X-point) of the ejection. The procedure followed to determine these key points is illustrated in figure 1B andC.

B point identification: Impedance cardiography data were low pass filtered at 15 Hz and the first derivative dZ/dt was computed. dZ/dt was baseline corrected by removing the average signal in the [Q-point Q-point+0.05sec] interval (Sherwood et al., 1990), to limit respiratory influences on impedance measurements (Cybulski, 1988). The B point was then identified as the time point at which dZ/dt reaches 15% of its maximum in the [Q-point Q-

MEG/ECG data preprocessing

Continuous MEG data were denoised using temporal signal space separation (as implemented in MaxFilter) and band pass filtered between 0.5 and 40 Hz using fourth-order Butterworth filter. MEG data contaminated by artifacts were detected through visual inspection and excluded from further analysis. MEG data contaminated by blink or saccade with amplitude >3degrees as indicated by the EyeLink were also excluded (https://www.srresearch.com).

ICA procedure to correct for the cardiac artifact

Independent component analysis (ICA), as implemented in the Fieldtrip toolbox (Oostenveld et al., 2011), was used to attenuate the cardiac artifact, for both magnetometer and gradiometer signals. MEG and ECG data were first high-pass filtered at 0.5 Hz using fourth-order Butterworth filter and then epoched in [-200 to +200 ms] segments centered on the R-peaks. MEG data segments free from artifact, blink and large saccade were then decomposed in independent component using the ft_componentanalysis fieldtrip function (which is using the runica function from EEGlab, (Delorme and Makeig, 2004)). Because temporal signal space separation induces rank deficiency, we defined the maximum number of ICA components as the rank of the dataset. To detect the components corresponding to cardiac activity, we computed the mean pairwise phase consistency (ppc;Vinck et al., 2010) between each independent component and the lead II ECG signal, in the 0-25 Hz range.

Components with a ppc superior to the mean+3std across ppcs were selected as cardiac components. The component selection process was repeated iteratively until no ppc was exceeding the mean+3std threshold, or until the maximum number of 3 components was reached. The cardiac components (between 1 and 3 per subject) were then removed from MEG data using the ft_rejectcomponent fieldtrip function, resulting in ICA-corrected MEG data. Before removing the cardiac components, their topographies and time-courses were visually checked. Across our participants, the rank of the data (i.e maximum number of components allowed when decomposing the data) was mean±std = 71±10 (range [67 116]).

The number of cardiac components removed from the data was mean+std = 2.9±0.3 (range

[2 3]).
Cardiac components topographies and time-courses, as well as magnetometers data before and after ICA correction, for an individual participant and at the group level, are shown on Figure 2.

Cardiac component extraction

To isolate the time-course of the cardiac component, we used the same ICA decomposition as described above, selected all components that were not correlated to cardiac activity and removed them from the ICA-uncorrected data using the ft_rejectcomponent fieldtrip function. This resulted in one continuous signal at each sensor corresponding to the cardiac components identified based on their similarity to the ECG and extracted from initial data.

HERs

To perform the analysis on HERs, MEG data were epoched in segments of [-0.1 to 0.7sec] around the R peak. Epochs containing an artifact, a blink or a large saccade, epochs containing a cardiac cycle shorter than 0.7 s, or with corresponding SV values exceeding a [mean+/-3std] threshold, were discarded from further analysis. On average, 37% of HER was discarded (min: 5%, max: 74%), resulting in a mean number of valid HER per subject of 205 (min: 108; max: 314). On average, 3% (min:0% max: 19%) of the trials were discarded due to artifact contamination, 26% (min: 1% max: 59%) due to blink, 2% (min: 0% max: 11%) due to saccade, 5% (min: 0% max: 47%) due to cardiac cycle duration and 1% (min: 0% max: 2%) due to outlying SV values. Gradiometers data were then recombined using the fieldtrip function ft_combineplanar. The exact same selection procedure was applied to ECG data.

Correlation analysis

For each subject, each channel and each time point, the Pearson correlation coefficient between HER and SV values was calculated and Fisher-transformed. To estimate chance level correlation in each subject, channel and time point, we created shuffled data where we permuted the association between SV and HER (e.g., SV in trial i re-allocated to HER in trial j) and recalculated the Fisher-transformed correlation coefficient. This operation was performed 100 of times and the median value of the random correlation coefficient distribution was extracted. This procedure resulted in one empirical correlation and one chance level correlation, at each channel and time point in each participant. Empirical and chance level correlation could then be statistically compared at the group level with a cluster-based permutation procedure (Maris and Oostenveld, 2007) detailed below.

Median-split analysis

For each subject, the median SV value was extracted. For each channel, high (above median) and low-SV (below median) HERs were then computed by averaging MEG data across segments according to their categories. Here again, the significance of the difference was assessed at the group level using a cluster-based permutation t test.

Group-level statistics: Cluster-based permutation procedure

Group level statistics relied on a cluster-based permutation procedure (Maris and Oostenveld, 2007). This method does not require the definition of any a priori spatial or temporal regions and intrinsically corrects for multiple comparisons in time and space.

Briefly, for each test (correlation/median-split), a statistical value t that quantifies the effect is computed between the two distributions being compared (empirical versus random r for the correlation, high versus low-SV HERs for the median-split). Individual samples with a statistical value corresponding to a P value below a selected threshold (P<0.05, two tailed)

are clustered together based on temporal and spatial adjacency. The cluster is characterized by the sum of the t values of the individual samples. To establish the likelihood that a cluster was obtained by chance, we shuffled the labels (empirical versus random or high versus low-SV) 10,000 times and repeated the clustering procedure selecting the maximum positive and the minimum negative cluster-level statistic across the tests. The Monte Carlo P value corresponds to the proportion of elements in the distribution of maximal (or minimal)

cluster-level statistics that exceeds (or is inferior to) the originally observed cluster-level test statistics.

Effect size

Effect size was estimated using Cohen's d definition for paired samples d=t/√N, where N is the number of participants and t the paired t-test statistics obtained when comparing high versus low-SV IBIs, ECG and HERs.

Interindividual analysis

To compute the interindividual correlation, we averaged MEG signal in the 450 to 550ms window post R-peak using heart cycles free from artifact, blink or saccade above 3 degrees. MEG signal and SV were then averaged across cycles for each subject, and mean correlation coefficient and its topography across time, the same procedure was performed using MEG signal at each time-point of the cardiac cycle.

Surrogate analysis

We first shuffled the IBI series for each subject. We then created surrogate R-peaks timing using the shuffled IBI series, with the first surrogate R-peak being placed 350ms before the first real R-peak. The SV associated to each surrogate cycle was the one corresponding in time to the surrogate R-peak. As for the initial analysis, we removed cardiac cycle contaminated by artifacts, blinks or saccades above 3 degrees. The procedure was repeated 150 times. To check whether our procedure succeeded in unlocking data from the real R-peak, we plotted for each subject the averaged ECG locked to the surrogate Rpeaks. Each plot was visually inspected.

Results

Validating the stroke volume measure in the MEG at 12.5 kHz

Impedance cardiography requires the injection of a small high-frequency current to quantify impedance changes due to blood ejection at each cardiac cycle. We found that the most commonly employed current frequency (100 kHz) is not compatible with good quality MEG recordings, because current injection at 100 kHz generated large spectral peaks at subharmonics of stimulation frequency. We thus estimated beat-to-beat stroke volume with a 12.5 kHz current, where spectral perturbations were not visible to the naked eye. , 2000). We suspected an effect of the stimulation frequency, notably because impedance depends on injected current frequency. We thus recorded, in five participants, five minutes at rest with 100 kHz stimulation and with 12.5 kHz stimulation, and compared the values of stroke volume thus obtained. In all five participants, the measured stroke volume was smaller at 12.5 kHz (30.6±2.8 ml) than 100 kHz (48.2 ± 4.0 ml). We then verified the incidence of stimulation frequency on each parameter used to compute the stroke volume. Left ventricular ejection time was similar at 12.5 and 100 kHz (mean LVET ± SE at 12.5 kHz = 0.288±0.007 s; at 100 kHz = 0.283±0.008 s), but the mean body impedance (mean Z0±SE at 12.5 kHz = 59.45±4.05 Ohms; at 100 kHz = 39.74±2.35 Ohms) and the peak value of the first derivative of the impedance (max(dZ/dt) ± SE at 12.5 = 3.40±0.34 Ohm.s -1 ; at 100 kHz = 2.47±0.26 Ohm.s -1 ) were systematically larger at 12.5 than 100 kHz. The distance between the electrodes remaining constant in each subject across stimulation frequencies, this confirmed that the stimulation frequency influenced the basal level of the impedance, leading to smaller stroke volume magnitude without affecting ejection time.

Since all further analysis are based on beat-to-beat fluctuations in stroke volume, or comparison between participants with the same acquisition parameters, rather than on the absolute value of stroke volume, this systematic underestimation of stroke volume has little incidence.

Influence of beat-to-beat fluctuations of stroke volume on HERs depends on ICA correction

We then evaluated whether variations of stroke volume were associated with modulations of HER amplitude computed on ICA-uncorrected magnetometers data, on the cardiac component isolated with ICA, which can be considered as reflecting the cardiac artifact affecting MEG sensors, and on ICA-corrected data. We performed two types of analysis in each participant: a correlation between single-beat HER at each timepoint and SV (one value per cardiac cycle), and a median-split analysis in which we directly compared HERs computed in high-versus low-SV cardiac cycles. In both analyses, we identified clusters of channels and timepoints on which the correlation or the difference was significant at the group level using cluster-based permutation tests (Maris and Oostenveld, 2007) .

ICA-uncorrected magnetometer data revealed a significant negative correlation between HER and SV at late latencies, from 534 to 617ms after the R-peak in a large cluster of 41 bilateral occipital and occipito-temporal channels (figure 3A; sum(t)=-2886, Monte Carlo p=0.037). The median-split analysis showed very similar results, with a significant difference between high-and low-SV HERs ranging from 569 to 694ms post R-peak in a cluster of 39 channels, with a similar topography (figure 3B; sum(t)=-4113, Monte Carlo p=0.002). A second negative subthreshold cluster (not shown) was observed at earlier latencies (472-536 ms post R peak) at posterior locations, but the difference between the high-and low-SV HERs did not reach significance (sum(t)=-1991, Monte Carlo p=0.07).

The cardiac component identified with ICA and locked to heartbeats showed a pattern of correlation with SV overlapping with the one observed in ICA-uncorrected magnetometer data. The cardiac component identified with ICA negatively correlated with SV from 489 to 700ms post R-peak in a cluster of 38 left-lateralized channels (figure 3C; sum(t)=-9889, Monte Carlo p=0.002). A significant difference between the high-and low HERs was also observed from 595 to 700ms post R-peak, in a cluster including 39 channels (figure 3D; sum(t)=-4419, Monte Carlo p=0.030).

Finally, HERs computed on ICA-corrected magnetometer data did not significantly correlate with stroke volume (figure 3E), and the difference between high-and low-SV HERs was no longer significant (figure 3F). This suggests that ICA successfully removed the cardiac artifact related to stroke volume. Still, this conclusion has to be tampered down by the existence of a subthreshold cluster (not shown) on the difference between high-and low-SV HERs found in ICA-corrected data (464-532ms post R-peak, sum(t)=-1820, Monte Carlo p=0.069).

The exact same analysis was performed on HERs computed from the combined gradiometers. As opposed to magnetometers, we observed no significant correlation with SV or significant difference between the low-and high-SV HERs on any type of data (ICAuncorrected data, cardiac component, ICA-corrected data).

In summary, HERs computed on combined gradiometers are not sensitive to stroke volume, but HERs computed on magnetometer data are sensitive to fluctuations in stroke volume. An ICA correction tailored to target the cardiac artifact strongly attenuates the contribution of stroke volume to HERs computed on magnetometers but might not entirely remove it.

Electrocardiogram and heart rate co-vary with stroke volume

Recording stroke volume requires a specific equipment and current injection might create artifacts on MEG/EEG data. We thus tested whether stroke volume fluctuations are reflected in fluctuations of classical and more readily accessible measures of cardiac activity, namely ECG itself and interbeat intervals (IBIs).

At the group level, we observed a significant positive correlation between IBI and stroke volume (figure 4B, mean correlation coefficient ± sd = 0.28±0.16, one-sample t test comparing the Fischer-transformed correlation coefficients against 0, two-sided t(20)=7.61, p<10 -6 ). The correlation was confirmed at the individual level with a significant positive correlation observed in 86% (18 out of 21) of the participants. One example of co-variation between SV and IBI in one subject (S12) is shown on figure 4A. The comparison between high and low-SV trials analysis revealed a similar pattern with significantly longer IBIs in cardiac cycles with a larger stroke volume (figure 4C, mean difference between the high-and low-SV cardiac cycles ±sd = 29.70±18.45ms; one-sample t test, t(20)=7.37, p<10 -6 ). Individual data revealed a significant difference of IBI between high-and low-SV cycles in 81% (17 out of 21) of the participants.

Interbeat intervals and ECG are more sensitive to stroke volume than MEG data

We have shown that HERs computed from magnetometers, as well as IBIs and ECG are sensitive to stroke volume. Because measuring stroke volume by impedance cardiography requires a specific set-up and induces additional noise on electrophysiological data, it would be interesting to be able to use ECG or IBIs as proxies to detect stroke volume differences between experimental conditions. To explore this possibility, we computed the effect size (Cohen's d, see formula in the methods) for the difference between high-and low-SV variables (Table 3).

The largest Cohen's d was observed for IBI with an effect size of 1.61. The second largest Cohen's D was observed for ECG-lead II with an effect size twice smaller (0.83). Effect sizes on HERs computed from magnetometers were systematically smaller, reaching at most 0.64 when selecting the channel showing the largest effect. In conclusion, IBIs are twice more sensitive to stroke volume fluctuations than MEG or ECG data.

Interindividual correlation between SV and HER amplitude

Given the previous report of an interindividual correlation between mean SV and mean HER's amplitude as measured with EEG in the 450 to 550ms window post Rpeak (Schandry and Montoya, 1996), we tested for an interindividual correlation between mean SV and HER's amplitude averaged in the same time window in MEG. Note that this analysis consists in correlating, across participants, the HER averaged across all heartbeats with the SV averaged across all heartbeats, i.e., very different from the beat-to-beat fluctuations reported so far in this article.

The HER amplitude averaged between 450 and 550ms in each participant on ICAcorrected magnetometers data significantly correlated with each participant's mean SV (Fig 6A right panel) on 12 channels (FDR-corrected p<0.05). Four of the 12 channels located over the right fronto-temporal regions showed a negative correlation (mean R=-0.61, all FDR corrected p<0.05), and 7 of the 12 channels, located over left fronto-temporal regions, showed a positive correlation (mean R=0.60, all FDR corrected p<0.05). An isolated channel over median occipital regions also showed a negative correlation (R=0.64, FDR-corrected p=0.04). Our results thus reveal a significant interindividual correlation between SV and HER amplitude in ICA-corrected magnetometer data, in the time-window in which Schandry and Montoya reported a correlation with HEP amplitude measured on EEG data.

However, and to our surprise, we observed that neither the topography (Fig 6A ) nor the time-course of the correlation between SV and HER varied much over time (Fig 6B). This pattern of results does not seem compatible with the hypothesis of a transient HER driven by aortic baroreceptors activity indexed by SV. We thus performed an additional analysis to probe whether at least part of the interindividual correlation between SV and HER was specifically locked to the heartbeat. To this end we performed the same analysis on surrogate datasets created by randomizing Rpeaks latency. We repeated this operation 150 times, and found that both the magnitude and the topography of the correlation between SV and surrogate HER data were similar to that of the correlation between SV and HER locked to the real heartbeats (Fig 6C). The results thus reveal the existence of a sustained, ongoing component in the correlation between MEG data and SV, is not locked to heartbeats. The results do not exclude the possibility of an additional transient interindividual correlation between HER and SV locked to heartbeats since the empirical correlation coefficient in the 450-550ms time window reported by Schandry and Montoya (1996) tends to be larger than surrogate correlation coefficients.

Discussion

HERs are often held to be related to the phasic arterial baroreceptor response to blood flow increase at each heartbeat, and should hence covary with stroke volume.

However, because some mechanoreceptors in the heart are insensitive to stroke volume, HERs could also be independent from stroke volume magnitude -those two hypotheses not being exclusive. The question of the link between HERs and stroke volume is further complicated by the presence of the cardiac artifact, attenuated by ICA correction. Here, we evaluated the link between beat-to-beat changes in stroke volume and HERs. We found that in healthy participants at rest, stroke volume contributed to HER amplitude in ICAuncorrected MEG data, over occipital and bilateral occipito-temporal areas with a late latency (500-700 ms post R-peak), as well as to the electrocardiogram in a wide, but earlier, latency range (150-550ms post R-peak). Using an ICA procedure designed to remove signal components covarying with the ECG, we found that SV influence disappeared from ICAcorrected MEG data. As further discussed below, this indicates that at least part of the HER (the one that remains after ICA correction) is unrelated to stroke volume and thus from transient arterial baroreceptor activity. In addition, we show that IBIs have a tight link with stroke volume, and are actually twice more sensitive to stroke volume fluctuations than ICAuncorrected MEG data, which might be useful since stroke volume requires a specific equipment to be recorded, and can induce noise in electromagnetic recordings. Finally, we further explored, in ICA-corrected MEG data, the between-participant correlation between HERs and SV as originally reported in EEG (Schandry and Montoya, 1996). We replicated the correlation between mean HER's amplitude in the 450 to 550ms time-window post R-peak and mean SV across participants, but found that this correlation is actually sustained, not transient, with a stable topography over the whole cardiac cycle.

What is the origin of beat-to-beat fluctuations in HER related to SV?

Before ICA correction, HERs did co-vary with stroke volume. This effect could reflect transient responses to blood ejection in arterial baroreceptors transmitted with rather long delays to the brain. In this scenario, the ICA would overcorrect HERs by removing genuine neuronal activity.

But the ECG, which is directly captured in MEG data and contributes to the cardiac artifact, also varies with SV. It follows that the influence of SV on ICA-uncorrected MEG data might come from the cardiac artifact. This is coherent with the observation that the ICA procedure is effective at removing the SV effect from MEG data, since the cardiac ICA components in MEG data are identified on the basis of their correlation with the ECG.

Another argument in favor of this interpretation is that SV did not affect gradiometers data, which are less sensitive to distant sources than magnetometers data. However, if SV affected MEG data through the cardiac artifact, we would expect SV to co-vary with MEG at the same latencies than with ECG, which was not the case in our study. It is thus difficult to determine whether the influence of SV on ICA-uncorrected MEG data is due to a genuine neural response dependent on SV, or to the cardiac artifact. Our results also show that ICA removes from the data both the cardiac artifact and the SV influence (whether corresponding to neural activity or mediated by the cardiac artifact).

Whichever interpretation is correct, our results indicate that the source of SV influence on ICA-uncorrected data is statistically dependent on ECG signal. The ICA correction, which was computed on small epochs of data ranging from -200 to +200ms around the R peak, affected the SV effect occurring late in the cardiac cycle, starting 500ms after the R-peak. In other words, removing MEG components identified early in the cardiac cycle also modified MEG signal late in the cardiac cycle, suggesting some statistical dependencies between MEG signals at different phases of the cardiac cycle.

Another candidate to account for the SV effect on ICA-uncorrected MEG data is the pulse artifact. This artifact is related to the cardiac cycle but its exact origin remains unclear. It has been related to different factors such as small movement of the head triggered by blood vessels pulsation, as well as the movement of blood particles at the time of blood ejection (Debener et al., 2010), which have different magnetic properties depending on their oxygenation level (Tank et al., 1992). From what has been described in EEG studies, the pulse artifact seems to share similar characteristics with the SV effect since it varies within participants across cardiac cycles, and also with the duration of the cardiac cycle (Debener et al., 2010). Whether MEG data are affected by the pulse artifact is not clear. Whereas one study suggested that MEG data were not contaminated by blood flow (Jousmäki and Hari, 1996), another study suggested that a late component might be related to the flow of blood hitting the aortic arch (Rodin et al., 2005). However, in both MEG and EEG data, the pulse artifact seems to occur around 200ms after the R-peak (Allen et al., 1998;Rodin et al., 2005), at the time of the ejection of blood from the left ventricle, which does not correspond to the latencies of the SV effect.

Arterial baroreceptors cannot be the only source of HER fluctuations

Whatever the origin of the SV influence on HERs in ICA-uncorrected data, our results

show that after ICA correction, HERs do not depend on stroke volume. It follows that at least part of the beat-to-beat fluctuation in HER is independent from beat-to-beat fluctuations in the volume of blood ejected at each heartbeat. Hence, arterial baroreceptors should not be considered as the sole sources of HERs.

The attribution of HERs to arterial baroreceptor transient activity dates back to the correlation initially described by Schandry et al in EEG (1996), where participants with a small mean SV had smaller HERs in the 450 to 550ms post R-peak time-window. While we replicate this interindivual correlation in this time window, we show that the correlation between SV and HER and its topography are relatively constant during the entire cardiac cycle, suggesting the existence of a basal correlation between MEG data and SV not related to the heartbeat. Since the basal level of correlation was constant across time, it cannot be triggered by a discrete cardiac event such as the ejection of blood from the left ventricle. Still this observation has to be considered with caution for multiple reasons. First, we did not carefully test the correlation at each time-point with a proper statistical analysis. In fact interindividual analyses of stroke volume are not recommended when using impedance cardiography (Sherwood et al., 1990). Indeed, between participants variations in thoracic density, erythrocyte orientation and electrode placement may influence stroke volume in an unpredictable manner (Miller and Horvath, 1978). Second, we cannot rule out the existence of an additional transient response to the heartbeat since the correlation in the 450 to 550ms was slightly higher in magnitude than during the rest of the cycle. However, the topography of the correlation that is not uniform across channels suggests that it does not come from differences in measurements between participants such as different level of noise affecting both impedance cardiography and MEG data.

Altogether, the fact that within-subject correlation with SV disappeared after ICA correction, as well as the fact that the inter-subject correlation with SV was constant across the entire cardiac cycle is not in favor of the hypothesis that HER amplitude only depends on the transient activity of arterial baroreceptors, itself shaped by the amount of blood ejected from the left ventricle.

Since our results question the hypothesis that HERs are driven by transient responses in arterial baroreceptors after blood ejection, other pathways might be considered. First, a great variety of baroreceptors exists, with both myelinated and non-myelinated pathways (Shepherd, 1985) and various number of synapses (Malliani et al., 1986). Additionally, baroreceptors are not only present in the arteries but also in the heart atria and ventricles (Armour, 1973). Activation of these baroreceptors could occur not only at the end of the systole when the blood is ejected from the left ventricle but at any time during the heart cycle, while the heart is filling. Finally, some mechanoreceptor activity is independent from blood pressure (Bishop et al., 1983). Beyond cardiac and arterial mechanoreceptors, additional pathways could also be recruited such as the skin somatosensory pathway (Khalsa et al., 2009) or more hypothetically the recently suggested vasculo-neuronal coupling (Kim et al., 2016). Finally, HERs during resting-state might reflect more cognitive parameters resulting from cortico-cortical interactions, such as content of spontaneous thoughts (Babo-Rebelo et al., 2016a, 2016b), than a direct ascending influence of cardiac parameters.

Implication and recommendation for future HER studies

The modulation of HERs amplitude according to stroke volume also means that a difference in HERs amplitude between two conditions, even if it occurs late in the cardiac cycle when the heart is electrically relatively silent, can still be related to a difference in cardiac activity and not solely to a difference in the way cardiac information are integrated at the level of the central nervous system. To eliminate or at least lower the influence of cardiac activity on HER amplitude, correction methods such as ICA are often used. Here, we showed that the stroke volume effect was present on the ICA cardiac components removed from the data and not anymore on the ICA corrected data. This highlights the importance of applying a correction procedure on brain data when studying HERs and shows the efficiency of the ICA in removing stroke volume effects. However it must be kept in mind that the ICA is a statistical procedure, with results that might slightly vary from one computation to the other. Additionally, the ICA procedure used in our study is not the standard textbook procedure since we segmented the data in 400ms time-window centered on the R-peak.

Applying the same filters on ECG and MEG data might also be important to select the cardiac components, even more in case of noisy datasets. Thus we cannot rule out that in some instances, depending on ICA parameters and efficiency, an effect of stroke volume might remain in ICA-corrected data. Finally, other artifact correction methods have been used to remove the cardiac field artifact from EEG or MEG data, such as template matching algorithms, spectral analyses or a combination of multiple procedures. It is thus important to remind the reader that the results presented here hold only for the specific ICA procedure that we used in this study and that stroke volume effects on HER might persist when using other correction methods.

Fortunately, stroke volume-related modulations were also observed on ECG and IBI, with significant correlations as well as differences in high-versus low-SV trials. The highest effect size was observed for the correlation between SV and IBI, followed by the correlation between SV and ECG lead II. This means that in the absence of stroke volume monitoring, a statistical comparison between ECG waveforms (with a preference for the lead II of the Einthoven triangle) and IBI durations between two experimental conditions can be used as a proxy to evaluate stroke volume differences between the two conditions. In the absence of differences in IBI or in ECG, it seems very unlikely that differences in brain response would be linked to a difference in cardiac activity and especially in stroke volume. It must be noted that the stroke volume effect on the ECG is observed both early and late in the cardiac cycle, and that SV-related difference in ECG amplitude is fairly small and undetectable by eye.

Thus, to rule out an effect of cardiac parameters between two conditions using the ECG, we recommend performing statistical test on the entire ECG time-series to rule out a differential effect of experimental conditions on heart activity. Finally, the existence of a betweenparticipant correlation between HER and SV, even after cardiac artifact attenuation with ICA, indicates that any comparison of HERs between groups of participants should carefully control for cardiac parameters.

As many HER studies are using EEG and not MEG to monitor brain activity, it would be important to also assess the stroke volume effect on HER computed from EEG data. It is difficult to speculate on this since the precise origin of the stroke volume effect on MEG data remains unknown. However, since the estimation of cardiac parameters such as IBI duration and ECG waveform do not vary between EEG and MEG recordings, our recommendations on carefully controlling for differences in cardiac parameters between conditions still hold in the case of EEG studies.

Conclusion

Whereas numerous recent studies point at a role of cognition in the modulation of HERs (Azzalini et al., 2019;Park and Blanke, 2019), the link between cardiac parameters and HERs has remained understudied. Our results call for a reconsideration of the hypothesis that HERs are driven by arterial baroreceptors activity and stroke volume fluctuations, at least when HERs are measured with MEG, since after the specific ICA correction applied here, HERs no longer co-vary with stroke volume. Still, the small within-subject correlation being observed between HER's amplitude and SV in ICA-uncorrected data highlights the need for a careful artifact rejection procedure, as well as rigorous assessment of cardiac parameters when comparing HERs between conditions.

Figure 1 .

 1 Figure 1. Experimental set up and stroke volume computation

  point+0.4sec] window (Figure 1C upper panel). X point identification: The end of the ejection was defined as the time point at which dZ/dt reaches a minimum in the [B-point B-point+0.4sec] time window (Figure 1C lower panel). If multiple local minima were observed in the time window of interest, we chose the one closest in time to the second heart sound that mainly reflects the closing of the aortic valve (Sherwood et al., 1990). The identification of the B and X-point were performed using a homemade Matlab script (source code available online, Fieldtrip toolbox required, Oostenveld et al., 2011), which included a visual verification step and detected outlying values for the ejection time, the delay between the R peak and the B point, and the delay between S2 (second heart sound) and the X point. Across the 21 participants, the mean±std LVET was 0.264±0.034 sec (range [0.185 0.311sec]). An illustration of LVET determination is represented in Fig 1B.

Figure 2 :

 2 Figure 2: ICA correction for the cardiac artifact

Figure 3 .

 3 Figure 3. Correlation and median-split analysis of the MEG data

Figure 4 .

 4 Figure 4. Cardiac parameters: Stroke volume and IBI

Figure 5 .

 5 Figure 5. Correlation and median-split analysis of ECGs

Figure 6 .

 6 Figure 6. Interindividual correlation between meanHERs and meanSV

Table 1 . Mean and standard deviation of SV values for each participant

 1

	Using the

Table 2 . Summary of the correlation (top rows) and median-split (bottom rows) analysis on the three ECG leads

 2 In conclusion, larger stroke volumes are significantly associated with longer cardiac cycles, and stroke volumes fluctuations are reflected in the ECG itself, during both systole and diastole.

		Lead I		Lead II			Lead III
			Cluster		Cluster			Cluster
		Sign of the	statistics	Sign of the	statistics		Sign of the	statistics
			Latency			Latency
	Latency (ms)	correlation/	(sum(t),	correlation/	(sum(t),		correlation/	(sum(t),
			(ms)			(ms)
		difference	MonteCarlo	difference	MonteCarlo		difference	MonteCarlo
			p)		p)			p)
			sum(t)=-399					sum(t)=367
	139-264	-			sum(t)=-	156-306	+
	Correlation		p=0.01					p=0.036
			276-543	+	1126
	with SV		sum(t)=294					sum(t)=821
	441-534	+			p<10 -3	316-527	+
			p=0.026					p=0.02
			sum(t)=-160					sum(t)=563
	141-207	-				126-297	+
	SV High vs.		p=0.044		sum(t)=599			p=0.005
			364-522	+
	Low		sum(t)=285		p=0.001			sum(t)=561
	444-527	+				347-516	+
			p=0.007					p=0.005

Table 3 . Influence of stroke volume on the different variables tested: effect size (Cohen's d) of the difference between high and low SV trials.

 3 MEG data show very similar effect sizes in ICA-uncorrected data and cardiac component identified with ICA, while no significant effect persists in ICA-corrected data. The effect observed on ICA-uncorrected data is thus fully captured by the cardiac components identified with ICA, and disappears from ICA-corrected data. For ECG, effect sizes pertain to the different clusters of significant difference between high and low SV trials reported in

Table 2 .

 2 ECG lead II is the most sensitive ECG lead to detect fluctuations in SV, and IBI is by far the most sensitive indirect measure of stroke volume.

	MEG (high vs low SV)		ECG (high vs low SV)
	ICA-uncorrected	Cardiac components	Lead I	Lead II	Lead III	IBI (high vs. low SV)

Acknowledgements

This work was supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 670325, Advanced grant BRAVIUS) and a senior fellowship of the Canadian Institute For Advanced Research (CIFAR) program in Brain, Mind and Consciousness to CTB; by a doctoral fellowship from the Ecole des Neurosciences de Paris Ile de France to DA; and by ANR-17-EURE-0017. The authors thank Clémence Alméras and Christophe Gitton for assistance in data acquisition.

Data and code accessibility

Data and code available at: https://osf.io/xr6zk/?view_only=002003ae99b947b4933322e2b80c6126


Mahmoud Al Ahmad

Magali Brunet

Sandrine Payan

Dominique Michau

Mario Maglione

Member, IEEE Robert Plana

Member, IEEE Mahmoud Al Ahmad

Wide-tunable low-field interdigitated barium strontium titanate capacitors

Keywords: Barium strontium titanate (BST), capacitors, thin film, tunable circuit, varactors

come

I. INTRODUCTION

I NTEGRATED barium strontium titanate (BST) voltage- controlled capacitors are core components in tunable radio frequency (RF) and microwave devices such as voltage controlled oscillators (VCOs) [START_REF] Jamil | Tunable ferroelectric capacitor-based voltage-controlled oscillator[END_REF]- [START_REF] Al-Ahmad | Wide BSTbased tuning of voltage controlled oscillator[END_REF], tunable filters [START_REF] Tombak | Voltage-controlled RF filters employing thin-film barium-strontium-titanate tunable capacitors[END_REF], [START_REF] Papapolymerou | A miniature low-loss slow-wave tunable ferroelectric bandpass filter from 11-14 GHz[END_REF], phase shifters [START_REF] Erker | Monolithic Ka band phase shifter using voltage variable BaSrTiO3 parallel plate capacitor[END_REF], [START_REF] Kim | A wide-band reflection-type phase shifter at S-band using BST coated substrate[END_REF], and tunable matching networks [START_REF] Chen | Analog tunable matching network using integrated thin-film BST capacitors[END_REF], [START_REF] Scheele | Continuously tunable impedance matching network using ferroelectric varactors[END_REF]. A varactor with high quality factor and large tuning range is a mandatory prerequisite to meet the requirements of high tunable components specifications [START_REF] Rahman | Electronically tunable LTCC based multi-layer filter for mobile handset application[END_REF]. The quality of the BST thin film depends on its processing conditions such as temperature, thickness and substrate [START_REF] Jakoby | Nonlinear dielectrics for tunable microwave components[END_REF]. The interdigital capacitor (IDC) fingers provide coupling between the input and output ports across the gaps. The major challenge in the fabrication of such structures is maintaining narrow gaps for high tunability, while simultaneously forming thick electrodes to minimize RF loss [START_REF] Yoon | Tunable ferroelectric capacitor with low-loss electrodes fabricated using reverse side exposure[END_REF]. The metallization and patterning by photolithography are key issues in the fabrication of such structures which are formed by the deposition of metal on a single side of a BST film [START_REF] Nath | Microwave properties of bst thin film interdigital capacitors on low cost alumina substrates[END_REF]. However, due to the difficulty of forming high aspect ratio photoresist molds with the required pattern resolution, it is difficult to fabricate tall and narrow gap electrodes .

In this work, 3.5 m gap BST interdigital capacitor exhibiting high tunability at low bias field is presented. The tunability of 3:1 is obtained at bias voltage of 5 V. Furthermore, loss tangent of the BST thin film is found to be approximately 0.0204 at 30 GHz.

II. INTERDIGITATED CAPACITOR DESIGN AND FABRICATION

Interdigitated capacitor structures were achieved by lift-off process [START_REF] Ouaddari | Microwave characterization of ferroelectric thin-film materials[END_REF]. The structure has 16 fingers of width 3.5 m, length of 100 m and spacing of 3.5 m. The Ba Sr TiO thin films were deposited by radio frequency sputtering using home made targets of 2 in diameter. The power density was 3 W cm , the distance between the target and the substrate was 55 mm and the 15 15 mm substrate temperature was 650 C. The total pressure and oxygen partial pressure were tuned as to reach the right film stoechiometry as checked by Rutherford Backscattering. The optimal parameters to achieve this good BST stoechiometry were a base pressure of 6 10 Pa, a total Argon Oxygen pressure of 5 Pa and an Oxygen partial pressure of 1%. Prior to the film deposition a pre-sputtering of 1 h was undertaken as to fix the target surface. Prior to the electrodes deposition, the sample's surface was cleaned by a soft O plasma treatment and baked in oven (200 C, 30 min). Then, top electrodes pattern was designed by lithography insolation of a photoresist resin through a shadow mask. 400 nm-thick Au electrodes were deposited by evaporation. To ensure the adherence of the noble metal film, a Ti layer (50 nm) was deposited prior to Au deposition. After the resin removing and an anneal step in a furnace (250 C/ 20 min). The final device is shown in Fig. 1(a).

III. MEASUREMENTS AND ANALYSIS

The device has been measured using a network analyzer. The device consists of two independent tunable capacitors with common ground. The two interdigited capacitors have been activated by applying a dc bias on the signal lines. The measured transmission coefficients as a function of frequency for different bias voltages are shown in Fig. 2(a). It is evident that the two capacitors are isolated from each other by more than 45 dB which is good enough to decouple them. Hence, any information about the device performance, material characterizations and modeling are constructed from its reflection coefficients.

Fig. 2(b) shows the measured reflection coefficients of the device. The tunability is more pronounced at higher frequencies due to the value of the tuned capacitance which exhibits a low value. The corresponding equivalent circuit has been constructed based on the physical geometry of the structure and is shown in Fig. 1(b). The values of the circuit elements were first calculated by using the general relation between the distributed and the lumped elements [START_REF] Mongia | RF and Microwave Coupled Line Circuits[END_REF]. Then these computed values were adjusted by matching the equivalent circuit performance with the measured. The set of elements, , and , represent the shunt-stub corresponding inductance, inherent capacitance and losses, respectively. Due to the wide slot of the CPW line, i.e., 60 m, the BST material is not activated within the slots, thus these set of elements are not tuned. The variation in the dielectric constant of the BST thin-film due to the applied electric field results in a tunable capacitance which is generally modeled by a lumped capacitance of value . The thin-film quality modeled in terms of parallel resistance of , while the conductor losses are represented by . represents the inductance of the CPW access and capacitor fingers. Fig. 2(b) also shows the good fitting between the measurements and the equivalent circuit performance for zero bias. The tuned capacitance exhibits a tunability of 63% with the application of applied field of 1.4 V m. Up to our knowledge this is the lowest tuning field ever reported.

The film quality factor is computed from the inverse of the dielectric loss factor . This dielectric loss is calculated using (1) and the equivalent circuit parameters, namely the shunt resistance which models the losses of the dielectric and the which represent the capacitance tuned value, the loss tangent, , is then calculated [START_REF] Jin | Microwave characterization of thin film BST material using a simple measurement technique[END_REF] as follows:

(1)

where is the angular frequency. At 30 GHz, the is found to be 0.0204, i.e., film quality of 50. Fig. 3 shows the permittivity and losses voltage-dependence in the dielectric material. The BST material exhibits unavoidable intrinsic losses at high frequency, thus limiting their based devices performances. The limitation of device quality factor due to its ohmic losses can be avoided by thicker metallizations.

Finally, we compare the present data with several recent literature examples as shown in Table I [17]- [START_REF] Bellotti | Tunable dielectric properties of BST thin films for RF/MW passive components[END_REF]. These data illustrate that this work reports the lowest required tuning field preserving a wide tunability.

IV. CONCLUSION

Interdigitated BST capacitors with both wide tunability and low biasing field have been processed. The capacitance exhibits its maximum value of 0.2 pF at zero bias. A tunability ratio of 3:1 with the application of low field 1.4 V m is observed. This is the lowest applied field with such high tunability that ever reported comparable to results reported in the literature by other groups.

Fig. 1 .

 1 Fig. 1. Fabricated device and its equivalent circuit: (a) test structure with W , L , L and X are of 850, 100, 350, and 50 m respectively. CPWs are with GSG of 60-100-60 m. Shunt stub of length 340 m and width of 150 m; (b) lumped element representations: Rs, Ls, Rp, L, C are 0.5 , 0.0712 nH, 3.7 , 0.0125 pF, respectively. Rbst and Cbst are of 1300 and 0.2 pF, respectively, at zero bias.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Microwave measurements of the device shown on Fig. 1(a): (a) the jS j transmission coefficient and (b) the jS j reflection coefficient and the circuit model.

M. A. Ahmad, M. Brunet, and R. Plana are with LAAS CNRS, Université de Toulouse, Toulouse F-31077, France (e-mail: al-ahmad.mahmoud@ieee.org; m_al_ahmad@hotmail.com).S. Payan, D. Michau, and M. Maglione are with ICMCB CNRS, Université de Bordeaux, Pessac F-33608, France (e-mail: maglione@icmcb-bordeaux.cnrs. fr).


Géraldine Parnaud

Brigitte Pignatelli

Ginette Peiffer

Sylviane Taché

Denis E Corpet

GP, ST Gp

Endogenous N-nitroso compounds, and their precursors, present in bacon, do not initiate or promote aberrant crypt foci in the colon of rats

Keywords: cancer, colorectal, prevention, promotion, initiation, toxicology, toxicity, processed meat, cured meat, ham, bacon, rat, NOC, N-nitroso-compounds, nitrosamine, ACF, preneoplastic lesions, biomarkers NOC, N-nitroso compounds, ACF, aberrant crypt foci, TEA, thermal energy analyser, TAC, TEAresponsive compounds

Processed meat intake is associated with increased risk of colorectal cancer. This association may be explained by the endogenous formation of Nnitroso compounds (NOC). The hypothesis that meat intake can increase fecal NOC levels and colon carcinogenesis was tested in 175 F344 rats. Initiation was assessed by the number of aberrant crypt foci (ACF) in the colon of rats, 45 days after the start of a high-fat bacon-based diet. Promotion was assessed by the multiplicity of ACF (crypt/ACF), in rats given experimental diets for 100 d, starting 7 d after an azoxymethane injection. Three promotion studies were done, each in 5 groups of 10 rats, whose diets contained 7, 14 or 28% fat. Tested meats were bacon, pork, chicken and beef. Fecal and dietary NOC were assayed by thermal energy analysis. Results show that feces from rats fed bacon-based diets contained 10 to 20 times more NOC than feces from control rats fed a casein-based diet (all p<0.0001 in four studies). In bacon-fed rats, amount of NOC input (diet) and output (feces) were similar. Rats fed a diet based on beef, pork or chicken meat had less fecal NOC than controls (most p<0.01). No ACF were detected in the colon of bacon-fed uninitiated rats. After azoxymethane injection, unprocessed but cooked meat-based diets did not change the number of ACF, nor the ACF multiplicity, compared with control rats. In contrast, the baconbased diet consistently reduced the number of large ACF per rat, and the ACF multiplicity in the three promotion studies by 12, 17 and 20 % (all p<0.01). Results suggest that NOC brought by dietary bacon would not enhance colon carcinogenesis in rats.

Introduction

Epidemiological studies suggest that dietary factors are important determinants of colon cancer risk [START_REF] Doll | The causes of cancer[END_REF]. Many studies have implicated red meat and processed meat intake as risk factors, while white meat or fish intake would reduce the risk of developing colon cancer (review in ref. [START_REF] Parnaud | Colorectal cancer: controversial role of meat consumption[END_REF]. One of the causative agent from processed meat may be Nnitroso compounds (NOC) either brought by the meat, or generated endogenously. But epidemiologic evidence of the carcinogenic potential of dietary NOC, and of precursor nitrates and nitrites, remains inconclusive with regard to the risk of stomach and esophageal cancers [START_REF] Eichholzer | Dietary nitrates, nitrites, and N-nitroso compounds and cancer risk: a review of the epidemiologic evidence[END_REF][START_REF] Lijinsky | W: N-nitroso compounds in the diet[END_REF]. Volatile nitrosamines are detected in cured meat processed with nitrite, like bacon. In some fried bacon, concentrations of NOC could vary from 430 to 6800 µg(N-NO)/kg [START_REF] Massey | Volatile, non-volatile and total N-nitroso compounds in bacon[END_REF]. NOC are alkylating agents, and they are a class of potent animal carcinogen that occur in the environment. But NOC are also found in the colon. They are formed endogenously, because the amines and the amides produced primarily by bacterial decarboxylation of amino acids can be N-nitrosed in the presence of a nitrosating agent such as nitrite in food [START_REF] Pignatelli | Formation et distribution des composes N-nitroses CNO dans l'alimentation[END_REF]. A relationship between NOC formation catalysed by bacteria, chronic urinary tract infections, and an increased risk for squamous cell carcinoma of the bladder is supported by studies using analytical methods and storage conditions that exclude artifactual NOC formation [START_REF] Ohshima | N-nitrosamine formation in urinary-tract infections[END_REF]. Furthermore, the mutations that are common in human colorectal cancers are consistent with the alkylating effect of NOC [START_REF] Bos | Prevalence of ras gene mutations in human colorectal cancers[END_REF]. The effect of red meat and white meat consumption on fecal NOC levels have already been study in humans [START_REF] Bingham | Does increased endogenous formation of N-nitroso compounds in the human colon explain the association between red meat and colon cancer?[END_REF][START_REF] Silvester | Effect of meat and resistant starch on fecal excretion of apparent N-nitroso compounds and ammonia from the human large bowel[END_REF]. The authors showed a 3-fold increase in fecal NOC levels when volunteers are given 600 g of red meat per day. In contrast white meat and fish intakes do not increase fecal NOC levels in volunteers [START_REF] Bingham | Does increased endogenous formation of N-nitroso compounds in the human colon explain the association between red meat and colon cancer?[END_REF].

The present study was designed to investigate the effects of a diet containing processed meat, red meat or white meat on fecal NOC levels in rats. Since the fat content of diet may modify the effect of diet on colon carcinogenesis [START_REF] Pence | Feeding of a well-cooked beef diet containing a high heterocyclic amine content enhances colon and stomach carcinogenesis in 1,2dimethylhydrazine-treated rats[END_REF], the effect of meat was tested in both a low-fat and a high-fat context. We have examined three hypotheses: i) that red meat consumption increases NOC production in the rat as it does in man; ii) that processed meat intake increases fecal NOC excretion; and, iii) that these high intestinal NOC levels may initiate or promote the growth of aberrant crypt foci (ACF). The rat/azoxymethane model of experimental colon carcinogenesis was used to assess the validity of these hypotheses.

Materials and methods

Animals.

A total of 175 female Fischer 344 rats (F344) obtained from Iffa-Credo (Lyon, France) at 4 weeks of age, were used. The animals were housed two rats per stainless steel wire drop-bottom cage, at 22°C on a 12-hour light:12hour dark cycle. Before randomization, the rats were allowed free access to standard rodent chow (UAR, Villemoisson, France) and to water. After randomization, the rats were fed dry powdered diets based on a modified AIN-76 formula, provided by UAR.

Experimental procedures

We did three sequential rodents studies. #1: A 45d initiation study in three groups of rats; #2: A 100d promotion study in 10 groups of rats, fed high-fat diets. #3: A 100d promotion study in 5 groups of rats fed low-fat diets. Both ACF and NOC data of studies #1 and #3 are reported here. The ACF data of study #2 have already been reported in a previous article [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF], and the results of NOC analysis are presented here.

Study #1:Initiation : Twenty-five rats were allocated to three groups after five days of acclimatisation (Fig. 1). Five positive control rats in group 1 were given a single i.p. injection of azoxymethane (Sigma, St Quentin, France) at a dose of 5 mg /kg in NaCl 9g/l. These rats and ten control rats were fed a high fat AIN76-based diet containing 28% fat and 40% protein (Table 1.a). Ten experimental rats were fed, ad libitum, a diet containing 60% bacon during 30 days (Table 1.a). The bacon-based diet and the control diet contained identical fat and protein levels. Then, all rats received the high-fat control diet for 15 more days (Fig. 1).

Study #2: High-fat diet Promotion : As previously reported [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF], one hundred F344 rats were given given a single i.p. injection of azoxymethane (20 mg /kg, Sigma, St Quentin, France), then randomized to 10 different AIN76based high-fat diets, given ad libitum for 100 days. Five diets were adjusted to 14% fat and 23% protein, and 5 other diets to 28% fat and 40% protein. Fat and protein were supplied by 1) lard and casein, 2) olive oil and casein, 3) beef, 4) chicken with skin, and 5) bacon. The three types of meat were cooked in an oven for 15 minutes at 180-185°C. Each ovenproof glass dish contained 500 g of meat, 1 cm thick. During cooking, the bottom of meat portions was dipping in melted fat from meat. After cooling, the portions were minced, frozen for 24 hours at -20°C, then freezedried. Dry powdered cooked meats were included to make 30% or 60% of the diet.

Study 3: Low-fat diet Promotion: The effect of the high-fat meat-based diets on the promotion of ACF was so unexpected [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF] that we decided to duplicate the meat study in a low-fat diet context. Fifty rats were given a single i.p. injection of azoxymethane (20 mg /kg, Sigma, St Quentin, France) after five days of acclimatization to the animals colony. One week later, the rats were randomly allocated to 5 groups (n = 10 in each group) and given the experimental low-fat diets for 100 days (Fig. 1). Control rats were fed an AIN76 based low-fat control diet containing 2% corn oil, 5% lard, and 25 % casein (Table1.b). Four experimental groups were given diets with the same fat and protein contents, containing 30% meat. Low-fat beef (hamburger, Carrefour), chicken (fillet, Père Dodu), pork (Carrefour), and lean bacon (Carrefour) were obtained from a local supermarket (Carrefour Purpan, Toulouse, France). Meat was cooked as previously described. After analysis of fat and protein contents in each type of meat, meat diets were supplemented with casein to reach the 25 or 40% protein target. The percentage of fat was adjusted with lard for bacon and pork diets, and with chicken fat for chicken diet (Table 1.b).

In the three studies, the animals were observed daily and weighed weekly. The consumption of experimental diets and water were recorded on two occasions, for three days. The 24h fecal excretion (weight and number of pellets) was monitored for 3 days, on three occasions. Fecal moisture was also recorded on feces collected for 24h. All rats were killed by carbon dioxide asphyxiation. When the animals were sacrificed, feces in colon were removed for NOC assay, and immediately put in ice until preparation of fecal sample.

Assay of ACF

At the termination of the studies, the colon were excised, flushed with Kreb's Ringer solution (Sigma), then opened longitudinally and fixed flat between coded filter papers in 10% buffered formalin (Sigma). ACF were scored using the procedure of Bird [START_REF] Bird | Observation and quantification of aberrant crypts in murine colon treated with a colon carcinogen: preliminary findings[END_REF]. The colons were stained with methylene blue (0.1%) for 6 min, then the mucosal side was observed at 32 x magnification. ACF were distinguished by their slit-like opening, increased staining, size and pericryptal zone. The number of ACF per colon and the number of aberrant crypts in each focus were recorded. All colons were scored blindly by a single observer. Initiation was assessed by the number of ACF. Promotion was assessed by the number of large ACF, and by the multiplicity of aberrant crypt foci (number of crypts per ACF).

Analysis of Fecal Nitrate and Nitrite

Fresh fecal samples were weighed and mixed with 4 volumes of distilled water. After shaking the mixture for 20 min and centrifugation at 20000 rpm, the supernatant was removed. One part was treated with sulfamic acid to destroy nitrite, and stored at -20°C until analysis of fecal NOC. The other part was used to measure nitrate and nitrite. Fecal nitrate were converted to nitrite a Nitralyser TM (World Precision Instruments, Inc). Nitrite concentration was determined colorimetrically : 200µl of reduced supernatant were added to 200µl of Griess reactive (0.1%  naphthylethylenediamine in 60% acid acetic/ 1% sulfanilic acid in 30% acid acetic (V/V)). The absorbance was read at  =550nm.

Fecal NOC Assay

Fecal total NOC were determined using thermal energy analysis (TEA) [START_REF] Pignatelli | Improved group determination of total NOC in human gastric juice by chemical denitrosation and thermal energy analysis[END_REF]. In brief, this method involves direct chemical denitrosation of the sample followed by chemiluminescence detection of the released nitric oxide (NO). In procedure A, the fecal sample was injected directly into refluxing ethyl acetate (50 ml) containing acetic acid 0.1% V/V hydrochloric acid and the thermo-and acetic acid labile TEA-responsive compounds (TAC) were determined. In procedure B, the fecal sample were injected directly into refluxing ethyl acetate (50 ml) containing hydrogen bromide (15% in glacial acetic acid) and both TAC and NOC were determined simultaneous. The difference between the two procedures (B-A) represents the concentration of NOC in fecal sample. The concentrations of NOC were calculated from the ratio of the peak area of the sample to that of a standard sodium nitrite solution. Results are expressed as µmol NOC/kg feces.

Statistical Methods

Values are means ± SD, and P values are two sided. Data were analysed by one-way factorial analysis of variance (ANOVA: factor: type of meat). When ANOVA showed a significant difference between groups (f test < 0.05), multiple comparisons were done by the Dunnett's test comparing each group with the control group.

Results

General Observations

In the initiation study #1, the feeding of bacon did not modify the mean body weight (Table . 2), and the food intake (data not shown). Mean daily food intake was 8.2 g/d for each high-fat diets. Study #2 results have already been reported in details (ref. 12, table 3). In the promotion study #3, the feeding of meat increased significantly the rats mean body weight (p=0.001, Table . 2). In contrast, the mean daily food intake was the same in all groups (10 g/d).

The water intake was nearly the same in all groups except in bacon-fed rats (Table 2). As already reported [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF], the water intake was 1.5 to 2-fold higher in rats fed baconbased diets than in controls (p<0.0001). This is likely due to the salt in the bacon-based diets (3.5% and 2.6% NaCl in the diets containing bacon, study #1 and #3 respectively).

In the three studies, the feeding of bacon markedly increased the daily fecal weight and the fecal moisture (Table .  2 and ref. 12). Fecal excretion was 1.5 time higher, and fecal moisture more than doubled by the feeding of a bacon-based diet. The fecal weight and the fecal moisture were not different between the others groups (Table 2).

Effect of Different Types of Meat on Fecal NOC

Study #1 Initiation : The fecal NOC concentration was 21-fold higher in feces of bacon-fed rats than in feces of controls (p<0.001; Table . 3).

Study #2: High-fat diet Promotion: Rats fed 30% bacon had 20 times more NOC than control fed 14% lard, p<0.0001 (Table . 3). Rats given 60% bacon had 12 times more NOC than controls fed 28% lard, p<0.0001 (Table . 3). Rats fed with beef or chicken meat had less fecal NOC than controls (Table . 3), and this difference reached significance when we analysed the data set without the group of bacon-fed rats. Diets containing 30% and 60% bacon brought 4.3 and 12.2 nmol NOC daily to the rats, that excreted 8.6 and 12.3 nmol NOC/d respectively.

Study #3: Low-fat diet Promotion: Rats fed a baconbased diet had 10.5 times more fecal NOC than control rats (p<0.0001) (Table . 3). Bacon-based diet brought 13.3 nmol NOC daily to the rats, that excreted 13.1 nmol NOC/d. Rats fed with beef or pork meat had less fecal NOC than control (Table . 3).

Fecal nitrite and nitrate levels were 3 times higher in rat fed the bacon based diet than in rat fed control diet: 321.3 ± 91.1 and 100.0 ± 32.3 nmol/g feces respectively (p<0.01). There were no significant difference between the others groups (beef: 146.1 ± 62.3; chicken: 116.3 ± 52.5; pork: 159.3 ± 72.9 nmol/g feces).

Effect of Different Type of Meat on ACF

Study #1 Initiation: No ACF were detected in salineinjected rats fed with a bacon-based diet (group 3) or a control diet (group 2). In contrast, many ACF were detected in each rats of group 1 treated with AOM (mean ACF number, 19; range: 7-154 ACF per colon).

Study #2: High-fat diet Promotion: As already reported, the ACF multiplicity was reduced by 12% in rats fed the 30% bacon diet ( 3.21 ± 0.47 crypts/ACF in controls, and 2.84 ± 0.45 in bacon-fed rats), and by 20% in rats fed the 60% bacon diet (3.27 ± 0.38 and 2.62 ± 0.6; p<0.001) [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF]. In contrast, the ACF multiplicity was the same in the other groups fed casein and fat, or beef meat or chicken meat diets (p=0.7) [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF]. Bacon intake also decreased the number of large ACF (containing 7 or more crypts/focus). These large ACF (containing 7 crypts or more per ACF) are better predictors of cancers than total ACF. The number of large ACF was three times lower in rats fed a 60% bacon-diet than in control rats (2.1 ± 2.5 and 6.4 ± 4.9 large ACF/colon respectively, p=0.03), but the difference did not reach significance in rats given a 30% bacon-diet (5.0 ± 4.8 and 5.4 ± 4.1 large ACF/colon).

Study #3: Low-fat diet Promotion: The multiplicity of ACF which may be considered a marker for tumor promotion was diet dependent (p=0.0009; Table. 4). The multiplicity was nearly the same in all the groups fed various meats, except in the group of rats fed a low-fat bacon-based diet. Compared with the low-fat control diet, beef, chicken and pork diets did not change the growth of ACF. In contrast, compared with the low-fat control diet, the ACF multiplicity was reduced by 17% (p<0.01), and the number of large ACF was reduced by 44%, in rats fed the low-fat bacon-based diet (p=0.003; Table 4). The total number of ACF per rat was not changed by the diet (Table. 4).

Discussion

The feeding of a diet containing 30 or 60% bacon to rats increased the fecal NOC level. Compared with control rats fed with casein and lard, the bacon-based diets led to a 10 to 20-fold increase in fecal NOC in three independent rat studies. Assuming that the median MW of fecal NOC is 150, the fecal concentration was 1.5 to 2.5 µg/g feces.

The present results can be contrasted with those of earlier studies: Previously, fecal NOC were found to be increased in humans increasing red meat consumption: a high red meat diet increases the fecal NOC level 3-fold in volunteers, to an estimated concentration of 2 µg/g feces [START_REF] Bingham | Does increased endogenous formation of N-nitroso compounds in the human colon explain the association between red meat and colon cancer?[END_REF]. In contrast, here, a diet containing 60% beef meat did not increase the fecal NOC in rats. However, bacon-fed rats had a fecal NOC concentration similar to volunteers eating a highmeat diet. Two differences between humans and rats may explain the results: (i) The AIN76 rat-diet does not contain vegetables, in contrast with human diet. Most vegetables contain nitrate, which may combine with meat factors to make NOC. (ii) Humans and rats do not have the same gut flora: different bacteria may produce different metabolites. Indeed, epidemiological data suggest that intestinal bacterial activity may be related to colon cancer [START_REF] Moore | Intestinal floras of populations that have a high risk of colon cancer[END_REF]. The gut flora and its potential for metabolic transformations could play an important role in the genesis of carcinogens. Many bacteria are able to catalyze the formation of nitrosamines [START_REF] Rowland | The potential role of colonic bacteria in colorectal cancer[END_REF]. Bacterial nitrate reductase reduces nitrate to nitrite, which generates nitrosating agents that, in turn, can react with nitrogenous compounds (amines, amides), in the presence of the microflora, to produce NOC. Nitrate reductase could be less active in rats than in humans, which may explain that beef-fed rats had lower fecal NOC levels than control rats. Moreover, in humans, the origin of NOC in feces is likely to be endogenous [START_REF] Bingham | Does increased endogenous formation of N-nitroso compounds in the human colon explain the association between red meat and colon cancer?[END_REF]. In contrast, here, the rats excreted as much NOC as they consumed. Thus, in rats, fecal NOC seems to come, principally, from ingested NOC. However, preformed NOC in food seem to be efficiently metabolized in the liver by detoxification enzymes [START_REF] Bartsch | Relevance of nitrosamines to human cancer[END_REF], and a small part of an ingested NOC dose should reach the colon. The nature of total NOC present or formed after nitrosation in colorectal content is unknown, so we cannot come to a conclusion about fecal NOC origin. In addition, Pignatelli et al [START_REF] Pignatelli | Improved methods for analysis of Nnitroso compounds and applications in human biomonitoring[END_REF], have shown that known NOC constitute only a minor fraction of total NOC that are formed or occur in diets. It is thus possible that a part of fecal NOC may be formed endogenously from meat amines or amides, and from bacon nitrate and nitrite.

Previously, Pence et al. showed that pyrolysed beef can significantly enhance colon carcinogenesis in rats when it is fed during, but not after, the dimethylhydrazine initiation [START_REF] Pence | Feeding of a well-cooked beef diet containing a high heterocyclic amine content enhances colon and stomach carcinogenesis in 1,2dimethylhydrazine-treated rats[END_REF]. In this study, cooked meat diet was started a week after the carcinogen injections. The lack of tumor promotion in rats fed a cooked beef meat diet was thus consistent with Pence's study. This lack of effect is also consistent with all other published rats studies: cooked red meat does not promote colon carcinogenesis in rodents (see refs. 2 and 12 for review).

Previously, specific NOC have been shown to produce colon tumors [START_REF] Hasegawa | Studies of initiation and promotion of carcinogenesis by N-nitroso compounds[END_REF]. In this study, a high level of fecal NOC was not associated with ACF initiation or ACF promotion in rats. However, Hasegawa could show the initiation of few colon tumors in rats given a total dose of 100 µmol of pure genotoxic NOC [START_REF] Hasegawa | Studies of initiation and promotion of carcinogenesis by N-nitroso compounds[END_REF]. He also showed a weak promotion of colon tumors in rats given a total dose of 1 mmol of non-genotoxic NOC [START_REF] Hasegawa | Studies of initiation and promotion of carcinogenesis by N-nitroso compounds[END_REF]. Here, each rat had received a total dose of 0.4 µmol of NOC (initiation study), or of 1.3 µmol of NOC (promotion studies). Hence, the NOC doses from bacon were too small to initiate or to promote ACF in the colon of rats. More, in rats fed bacon, NOC could have been diluted by the high water content of feces (Table 2). In addition, Peto et al. [START_REF] Peto | Nitrosamine carcinogenesis in 5120 rodents: chronic administration of sixteen different concentrations of NDEA, NDMA, NPYR and NPIP in the water of 4440 inbred rats, with parallel studies on NDEA alone of the effect of age of starting (3,6 or 20 weeks) and of species (rats, mices or hamsters)[END_REF] have also demonstrated that preformed NOC was not consistently associated with large bowel cancer. Oral doses of nitrosamines in animal studies mainly result in liver and esophageal tumors. Moreover, we don't know the nature of NOC in the bacon-based diet. According to the literature, the main N-nitroso compound found in fried bacon is nitrosothiazolidine, a non-carcinogenic nitrosamine (4). So, it is possible that the specific NOC in bacon and in feces of bacon-fed rats were not genotoxic or not tumor-promoting for the rat colonic mucosa.

Rats given the high-bacon diets consistently had smaller ACF than controls, as previously reported [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF]. This protective effect was found in two independent studies, using bacon from different suppliers. Bacon-based diets containing 7, 14 and 28% fat decreased significantly the ACF growth in rats colon, when compared with matched controls. A baconbased diet thus appears to protect against carcinogenesis, perhaps because bacon contains 5% NaCl and increased the rats' water intake. We have hypothesized that the high water intake could weakly increase the fecal moisture and allowed a dilution of toxic compounds, like bile acids, in the gut [START_REF] Parnaud | Effect of meat (beef, chicken, and bacon) on rat colon carcinogenesis[END_REF].

Beef, pork or chicken meat intake decreased fecal NOC excretion, and did not change ACF growth in rats. In contrast, a bacon-based diet increased 10-to 20-fold the fecal NOC level. But the intestinal concentration of NOC due to bacon-based diets did not initiate or promote colon carcinogenesis, assessed by the ACF assay in rats. Moreover, in three independent studies, a bacon-based diet decreased the colon ACF growth in rats. Taken together, the data do not support the hypothesis that NOC could explain the association of meat intake with colon cancer risk. NOC level in bacon-fed rats was high, thus the SD was large and masked the other differences.

Fig. 1 Figure 1 .

 11 Fig.1 Experimental protocol

Table 1 .

 1 Composition of experimental diets used in initiation (#)1 and in promotion (#3) studies

	Studies	Initiation #1			Promotion #3
	Diets	high fat			low fat
	Group	1 and 2	3	Control Beef	Chicken	Pork	Bacon
	Ingredient a
	beef b	-	-	-	30	-	-	-
	chicken c	-	-	-	-	28	-	-
	pork d	-	-	-	-	-	30	-
	bacon	-	60 e	-	-	-	-	30 f
	casein	40	13.9	25	-	-	2.73	1.58
	lard	-	-	5	-	-	3.42	0.29
	chicken fat	-	-	-	-	3.3	-	-
	corn oil	28	-	-	-	-	-	-
	sucrose	6	6	43	43	43	43	43
	corn starch	10.2	10.2	15	15	15	15	15
	cellulose	6.7	6.7	5	5	5	5	5
	choline	0.26	0.26	0.2	0.2	0.2	0.2	0.2
	bitartrate
	methionine	0.4	0.4	0.3	0.3	0.3	0.3	0.3
	corn oil	2.5	2.5	2.0	2.0	2.0	2.0	2.0
	mineral mix	4.68	4.68	3.5	3.5	3.5	3.5	3.5
	vitamin mix	1.33	1.33	1.0	1.0	1.0	1.0	1.0
	Notes : a: values are g/100g diet
	b: Freeze-dried cooked beef contained 11.1% fat and 83.8% protein
	c: Freeze-dried cooked chicken contained 6.1% fat and 89.0% protein
	d: Freeze-dried cooked pork contained 15.7% fat and 78.1% protein
	e: Freeze-dried cooked bacon contained 47.1% fat and 44.4% protein
	f: Freeze-dried cooked bacon contained 5.3% fat and 74.3% protein

Table . 2 Effect of feeding meat diets on body weight, water intake and fecal values in F344 rats

 .

					Body Weight Water intake Fecal Weight Water in Feces
	Group	Treatment and diet	n	(g)			(ml)		(g/d)		(%)
	study #1: initiation
	1	AOM + control high-fat 5	166 ± 6	a		15.7 ± 1.2		1.0 ± 0.5		16.2 ± 8.4
	2	control high-fat diet	10	167 ± 8			15.0 ± 1.5		1.1 ± 0.2		10.7 ± 1.3
	3	bacon high-fat	10	169 ± 5			21.2 ± 1.2	b	1.8 ± 0.6	b	40.0 ± 3.7	b
	study #3: promotion	c
	control AOM + control low-fat	10	187 ± 13		11.7 ± 0.5		0.67 ± 0.14	11.2 ± 0.9
	beef	AOM + beef low-fat	10	204 ± 8	b	12.7 ± 0.7		0.82 ± 0.05	14.4 ± 1.4
	chicken AOM + chicken low-fat 10	204 ± 9		b	12.9 ± 0.9		0.73 ± 0.05	12.8 ± 2.0
	pork	AOM + pork low-fat	10	204 ± 12	b	12.4 ± 1.3		0.76 ± 0.05	14.1 ± 5.7
	bacon	AOM + bacon low-fat	10	200 ± 12	b	21.2 ± 1.6	b	0.90 ± 0.10	b	24.3 ± 5.2	b
	a Mean ± SD b Significantly different from the control by Dunnet test (p<0.01) c ACF data from study#2 have been published (12)

Table . 3 Effect of meat-based diets on fecal NOC concentrations in rats § NOC concentration (nmol/g feces) a

 . No statistical comparison can be made between columns because studies were done at one year intervals. Significantly different from control by Dunnet test (p<0,01) *: Significantly different from control by Dunnet test (*, p<0,05; **: p<0,01 without the bacon group.

	Study b	3	2	2	1
	Dietary Fat	7%	14%	28%	28%
	Group
	lard and caseine	1.38 ± 0.64 0.47 ± 0.18	1.10 ± 0.49
	oil and caseine		0.29 ± 0.10* 0.72 ± 0.27*	1.05 ± 0.11
	beef	0.28 ± 0.13 c 0.20 ± 0.08** 0.27 ± 0.20**
	chicken	0.86 ± 0.78 0.43 ± 0.22	0.41 ± 0.18**
	pork	0.50 ± 0.49 c
	bacon	14.42 ± 5.43 c 9.24 ± 2.09 c 13.67 ± 3.08 c 22.04 ± 2.83 c
	§ : diet composition is given in Table 1

a : mean ± SD b : c :

Table 4 . Effect of diets containing 30% meat on number and multiplicity of ACF in the colon of F344 female rats, 100 days after a single azoxymethane injection (Study #3)

 4 Significantly different from the control by Dunnet test(p<0.01) c Significantly different from the control by Student t-test (p=0.003) and significantly different from pork group by Student t-test (p=0,014)

	Group	n	No.of ACF	No. of aberrant	No. of ACF with
			per colon	crypt/focus	5 or more crypts/focus
	control	10	137 ± 26	a	2.9 ± 0.2		19.7 ± 6.8
	beef	10	122 ± 60		2.9 ± 0.3		15.6 ± 9.8
	chicken	10	151 ± 28		2.7 ± 0.2		18.6 ± 8.1
	pork	10	151 ± 25		2.7 ± 0.3		18.1 ± 6.8
	bacon	10	134 ± 21		2.4 ± 0.2	b	11.1 ± 4.4 c

a Mean ± SD b

Acknowledgements

This work was supported in part by the Direction Générale de l'Enseignement et de la Recherche, the Ministère de l'Agriculture, France and by a grant from the Ligue Nationale Contre le Cancer, comity of Gers.