Neurosensory disturbances after immediate loading of implants in the anterior mandible: an initial questionnaire approach followed by a psychophysical assessment

The aim of the study was to assess past and present neurosensory disturbances using a questionnaire and a psychophysical approach in patients treated with immediate loaded implants in the edentulous anterior mandible. A group of 65 patients (age range 30–84 years, mean 58 years, 30 women) was enrolled. All were treated by means of three immediately loaded implants (Branemark Novum System®). A self-designed questionnaire was used for data collection. The response rate was 89%. Of the 58 responders, 33% (n=19) reported neurosensory disturbances after implant surgery. Nine of these patients (mean age 56 years, seven women) participated in an objective evaluation and were subjected to a psychological and several psychophysical tests. At the moment of the evaluation none of the nine patients still had clinical complaints. Psychological testing revealed no statistical differences between the patients, who had previously experienced subjective complaints, and the control group. Two-point discrimination and thermal sensation tests revealed no sensory lesions. The light touch sensation test at the lower lip indicated a more frequent reduction of tactility for the test group (p≤0.03). Neurosensory disturbances can occur in the anterior region of the mandible after implant surgery.
Introduction
Nerve injury and specifically trigeminal nerve injury is known as a potential risk of many surgical procedures in the oral cavity in general [17, 23, 30].
Usually after oral implant rehabilitation, the patient expects and experiences significant improvements, not only regarding jaw function, but also in relation to dental, facial, and even overall body image [22]. One can perfectly understand that the patient does not accept neural side effects, which might compromise his well-being.
Following the definitions of the Subcommittee on Taxonomy of the International Association for the Study of Pain 1986, the types of sensorial disturbances are principally anesthesia, paresthesia, or dysesthesia. These changes can be persistent according to the degree of damage of the nerve [31].
Sensory disturbances in the maxillofacial region could be associated with different surgical procedures, like placement of endosseous implants [3, 36, 37]. With regard to immediately loaded implants, the presence of postoperative sensory disturbances was not documented.
Branemark et al. [5] developed a new approach for immediate loading using fixed prosthesis of prefabricated standard components in the edentulous mandible. This technique involves a flattening of the jaw crest, followed by the placement of the implants by means of prefabricated and thus not individualized surgical guides. Thus the distances (but not the depth of drilling) between the implants were always the same, considerably reducing the variation of the surgical procedure. Sometimes the distal implants were positioned near the mental foramen [5].
Damage to the mental nerve may result in loss of tactility and thus biting on the tongue or lip, drooling, painful sensations, and also interference with several jaw functions such as mastication, speech, hygiene maintenance, and social or psychosexual well-being [15].
Different methods were used to evaluate such sensory disturbances after the placement of dental implants. Ellies [11] in 1992 and Ellies and Hawker [12] in 1993 published two retrospectives studies based on the analysis of questionnaires.
Wismeijer et al. [36] applied in a prospective study a self-administrated questionnaire and the somatic questionnaire, the Hopkins Symptoms Checklist (HSCL). The latter is a questionnaire routinely used in psychology to estimate a patient’s psychoneurological and/or psychosomatic discomfort. The somatic score of the HSCL was oriented on physical complaints and shows the level of a patient’s perception of his/her physical state. The higher the somatic score (i.e., 25% in the study of Wismeijer et al. [36]), the more the patient tends to exaggerate physical complaints, but the risk of a sensory disturbance of the lower lip is a possible complication after implant surgery [38].
Bartling et al. [3] analyzed the neurosensory disturbances in a population of patients after oral implant placement, using a combination of psychophysical methods like soft brush, two-point discrimination, pain perception, and temperature sensitivity. A small number (8/94) of patients experience altered sensation after the placement of mandibular endosseous implants, but no permanent alteration was found.
Walton [37] published a prospective study of 75 subjects using one objective (the light touch sensation) test, associated with a subjective analysis (questionnaire); both methods were used before and after placement of two implants in the anterior mandible. In this study, 24% of subjects reported neurosensory disturbances in the short term after implant surgery in the anterior mandible, but the problem appears to be a transient one with only about 1% experiencing sensation changes 1 year after implant surgery [37].
After reviewing the related literature, it is clear that the proportion of patients with sensory disturbances varies among publications.
It is therefore essential to assess if neurosensory changes take place after the immediate loading of oral osseointegrated implants in the edentulous mandible in patients who received a fixed prosthetic construction on the day of implant insertion or the next day.
Distinguishing between dysesthesia, an unpleasant abnormal tactile sensation, and paresthesia, an abnormal (not painful) and often decreased sensation, is the first step in defining the character of the neurosensory disturbance reported by a patient. Further differentiation between paresthesia and hypoesthesia, which is a reduction in the level of sensation, and finally anesthesia, the complete absence of any sensation, is important from all viewpoints. In the presence of dysesthesia, the differentiation must be established between spontaneous and a stimulus-induced unpleasant sensation. The clinical approach and sometimes legal implications of these different conditions are evident [6].
Besides clinical evaluation, it must be understood that evaluation of nerve injuries such as demyelinization due to compression (neuropraxia), distal Wallerian degeneration with intact cell tubes (axonotmesis), or proximal and distal Wallerian degeneration with disparate Schwann cell tubes (neurotmesis) is an impossible mission for the clinician. Nevertheless, if no spontaneous return of tactile sensibility is noted within 3–6 months, the permanent loss of continuity of some or all the elements of the nerve trunk should be expected [11, 27]. Some observations indicate that the return to normal tactile sensibility may even occur after 2 to 3 years [12]. The differences between laboratory and clinical results are obvious.
In some studies, questionnaires were used to evaluate the presence of sensory disturbances. Such methodology is clinically helpful and is a good basis for more detailed and objective evaluations.
Immediate loading of oral implants was proposed as an alternative protocol in the rehabilitation of partially or fully edentulous patients [8]. Surprisingly enough, no study has yet referred to the possible different neural sensibility when loading is imposed immediately after or together with the placement of the implants. This tactile sense aspect is relevant especially if we consider that the prosthetic rehabilitation (implying by example a full occlusal contact) is functional within one or a few hours after the implant placement. Thus, the patient’s awareness of the load imposed on the implants can be a key issue to avoid undue load transfer on the implant–bone interface.
The overall aim of the present research is to objectively evaluate the neurosensory disturbances and/or function occurring after placement of oral implants in the anterior region of the mandible.
Materials and methods
This study comprised a total of 65 patients (age range 30–84 years, mean age 58 years; 30 women). All patients were treated by means of immediately loaded implants in the anterior mandible with the Brånemark Novum® system approach. Surgery took place at the Department of Periodontology, Catholic University Leuven (55 patients, 3 surgeons), and at the Department of Oral and Maxillofacial Surgery, Erasmus Hospital, Free University of Brussels (10 patients, 2 surgeons).
Selection criteria included (1) placement of implants in the anterior mandible using Brånemark Novum System® (NobelBiocare AB, Gothenburg, Sweden); and (2) no history of neurological disorder.
An ad hoc multiple choice questionnaire (13 questions) was designed to record past and present neurosensory disturbances in these patients. The clinical history of the patient was used, and according to the selection criteria, a self-administered questionnaire was sent out to the 65 patients. The questionnaire was sent by mail to the patients with the request to complete it and return it to the clinic. The questionnaire was designed by experienced periodontologists, prosthodontists, and one psychologist (see Appendix for the questionnaire). The answers to the questions were analyzed and data were collected by this same team.
An objective evaluation took place after analysis of the questionnaire. During this evaluation psychological and psychophysical tests took place. The clinical evaluation of trigeminal nerve injuries suggested by Zuniga and Essick [39] in 1992 was used as basis in the test and control population.
A psychological test: the SCL-90-R® (Symptom Checklist-90-Revised) was applied. The SCL-90-R® measures the psychoneurotic-somatic nonwellness (Psychoneuroticism) of patients [10]. The SCL-90-R® is different from the HSCL because it considers a broader area of psychopathology. The scale of psychoneuroticism in patients is assessed. It reveals the global level of recent psychological and also physical dysfunction. In the present study, the intensity of somatic complaints was considered important. The SCL-90-R® scale reveals complaints, loaded with a general feeling of physical dysfunction, as a result from functional problems.
After completion of the questionnaire, each patient was interviewed using a standardized series of “key questions” on altered feeling in the chin, lips, tongue, and cheek. Each time a distinction was made between the right and left sides of the face. Typical questions were as follows:
How would you describe the altered feeling?Do you notice the altered feeling constantly or only when touching the area, or chewing, or talking?Is it painful? Where? Transient or constant?Does it start spontaneously or is it evoked by touching, chewing, or speaking?What exacerbates the pain?What relieves the pain?
After thorough questioning about the patient’s general and oral medical history, an extra- and intraoral clinical examination took place, including palpation and/or percussion, to detect eventual provoked pain at the site of injury. Finally, inspection of the oral cavity was performed to find eventual evidence of nerve injury, self-induced trauma, etc.
In the present study three psychophysical tests were selected: two-point perception at the lower lip, both left and right side (Fig. 1); thermal sensitivity at the lower lip and gingiva of the anterior lower jaw, both left and right side (Fig. 2); and light touch sensation at the lower lip and gingiva of lower jaw, both left and right side (Fig. 3).
Fig. 1Two-point discrimination instrumentFig. 2Thermal sensitivity instrumentFig. 3Light touch sensation (Semmes-Weinstein Aesthesiometer®) instrument
Psychophysical tests demand a thorough consciousness and active participation of the patients and a quiet environment [20]. A combination of both psychophysical tests with testing tools adapted to the intraoral and perioral sites were used. The light touch sensation on the other hand was tested using the original Semmes-Weinstein Aesthesiometer® (Stoeling Company, Wood Dale, USA) device. To determine the threshold level the staircase method was applied. In the light touch sensation and static two-point discrimination, eight maximum and eight minimum values were recorded. Finally, in the thermal sensitivity test, all subjects were tested with ten trials. For a more detailed methodology of the procedures for sensory testing, see Jacobs et al. [21].
The collected data were statistically analyzed using the Statistica for Windows 5.1® (Stat Soft, Tulsa, OK, USA). A Mann–Whitney U test was applied to the threshold levels in the test and the control groups.
Results
Fifty-eight of the 65 patients (89%) completed the questionnaire and returned it to the hospital. Systemic diseases like cardiac, respiratory, endocrine, and renal diseases, allergic reactions, and psychological (depression) problems were detected in 46% (n=30) of the patients.
The mean time between the placement of the implants and the reception of the questionnaire was 20 months (range 8–40 months).
The analysis of the questionnaire showed that 33% (n=19) of patients reported a kind of neurosensory disturbance after the placement of the implants (range 8–24 months). The age range of this subpopulation was between 30 and 71 years (mean age 56, 13 women). This data are not different from the remaining group.
The duration of this postsurgical neurosensory disturbance after the implant surgery was less than 3 months in 58% (n=11) of the patients. However, eight patients were still suffering from a disturbance. For them the problem persisted for a period between 8 and 21 months.
The most commonly affected sites in the 19 patients were the gingiva only (6 patients), the inferior lip only (4 patients), and the chin (4 patients). One of the patients did not remember or could not determine the affected zone.
Speaking and drinking (five patients) were the oral activities most commonly impaired by the altered sensations. An important part of the affected population group (12 patients) didn’t complain about problems with oral function or daily activity.
The most common reported sensation was numbness (nine patients), followed by cutting, beating, and itching reported by two patients.
Only one patient considered that the benefits of a fixed prosthesis did not outweigh the disadvantages that she had experienced as a result of disturbances in sensation of the lower jaw. She considered that she would not have done this surgery again, if she had been informed beforehand about the potential sensory changes in the orofacial region.
From the 19 patients, 9 volunteered to participate in the objective evaluation (test group). The test group was 30–71 years old (mean age 56 years, seven women).
A control group of volunteers (N=9) was also enrolled. Their age ranged from 49 to 71 years (mean age 63 years, all women). The mean observation time in this group since the surgery (two implants placed in the anterior mandible after a two-stage protocol) was 18 months (range 11–22 months).
As psychological test, the SCL-90-R® test was applied for symptom measurement of patient treatment outcomes and their degree of somatization of the symptoms. The psychophysical tests included three neurosensory evaluations: two-point discrimination, thermal sensitivity, and light touch sensation. The staircase method of limits was applied to determine the tactile threshold. An age- and gender-matched group of patients who had undergone similar surgery (two implants in the symphyseal region) in the same area, but without reporting sensory disturbances, served as control.
Results of the psychological and psychophysical tests
As mentioned above, each of the 19 patients of the self-declared affected population was invited for a clinical evaluation and 9 of the 19 patients accepted to participate. The time span between surgery and psychophysical evaluation was on average 29 months (range 19–49 months). During the clinical interview, before the actual objective testing took place, none of the nine patients had remaining complaints or clinical symptoms such as drooling or tongue bite wounds that could indicate a sensory disturbance.
However, at the reception of the questionnaire, which had to be mailed after the examination session, five of the nine patients with self-declared neurosensory disturbances still reported having them. In the remaining four patients the subjective neurosensory disturbances were completely resolved.
No major complaints were recorded after the evaluation of the SCL-90-R®. The scores of the SCL-90-R® of the test (N=9) and control group (N=10) revealed no statistic differences, neither on the global scale of neuroticism, nor on the dimension of somatic complaints. This means that the level of complaints of both groups was similar.
After clinical examination and considering the negative interview for unpleasant dysesthesia and pain, it was concluded that only potential signs of paresthesia were currently present in the affected patients. Two-point discrimination, thermal sensation, and light touch sensation at the gingival level show no significant difference between test and control groups (Table 1) [14, 16, 26, 28].
Table 1Overview of psychophysical tests scores between test group, control group, and reference valuesType test/RegionTest groupControl groupReference valuesReference numberT°S/LLLS0.8 (CR)0.9 (CR)0.8 (CR)18T°S/LLRS0.9 (CR)0.9 (CR)0.8 (CR)18T°S/LGLS0.8 (CR)0.8 (CR)0.8 (CR)18T°S/LGRS0.8 (CR)0.8 (CR)0.8 (CR)182PD/LLLS3.4 mm4.5 mm6.1±3.1 mm193.3±1.6 mm202±4 mm21, 222PD/LLRS3.4 mm4.8 mm6.1±3.1 mm193.3±1.6 mm202±4 mm21,22LTS/LGLS7 NFa6 NFa4 (2.83) NF 19LTS/LGRS7 NFa7 NFa4 (2.83) NF19T°S Thermal sensation, LLLS lower lip left side, LLRS lower lip right side, LGRS lower gingiva right side, LGLS lower gingiva left side, CR correct ratio, 2PD two-point discrimination, mm millimeters, LTS light touch sensation, and NF the number of the filamentaMean value of von Frey hair
The light touch sensation of the lower lip was significantly impaired in the test group (Mann–Whitney U test). There was a statistical difference for both sides (p≤0.03) between test and control groups.
Systemic diseases have a significant effect on the outcome of the light touch testing at the lower lip. The light touch sensation of the lower lip revealed a statistical difference (p≤0.04) for both sides.
Discussion
The anterior region of the mandible was always considered as a “safe zone” for the use of oral implants. This is particularly true concerning the high success rate of oral osseointegrated implants [25]. Nevertheless, there is an important difference between the reported implant success rate with specific surgical technique and the postsurgical changes to function, sensory mechanisms, and the physiological integration in the human body. Anatomical considerations in the anterior region of the mandible and the skills and experience of the surgeon are also important [24].
It is surprising to note that the present methodology allowed us to observe a very high percentage of subjective postoperative complaints. These results are in agreement with others, i.e, Ellies [11] with 37% and Ellies and Hawker [12] with 36%. This high degree of concordance for the percentages of incidence is interesting, considering the different surgical techniques used and cultural and sometimes ethnic differences between the three involved populations (Canada, Australia, and Belgium).
It was not possible, based on the subjective data, to establish any correlation between the systemic diseases presented in the study population and the reported sensory disturbances (i.e., impairment of perception in a patient with diabetes) [2, 4].
It is interesting to know that eight patients had the impression to be affected by some kind of persistent neurosensory disturbance, most probably paresthesia or hypoesthesia, more than 12 months after the surgery. Nevertheless, besides these results, a majority of these affected patients considered that the benefits outweigh this kind of transient or permanent disadvantages, and consider that they would accepted an implant surgery again even if they knew about the present complication.
Normal somatic sensation reflects a continuous day and night monitoring process. Little of this activity reaches consciousness under ordinary conditions. Disordered sensation is alarming and dominates the sufferer’s attention. As expected from the neuroanatomical knowledge recently collected, neurosensory disturbances regularly occur in the anterior region of the mandible after surgery. Anatomical factors like the presence of an anterior loop, handling of the mental nerve during surgery, or the perforation of the incisive nerve canal can all provoke such disturbances.
As mentioned above, the Brånemark Novum® technique involves a flattening of the jawbone. This procedure can be considered rather invasive and can damage, in severely resorbed jaws, the incisive canal or the mental nerve emerging at the crestal level. The relationship between a reduction of the crestal jawbone and neurosensory disturbances could not be traced in the literature. However, bone chin grafting procedures present some similarities. von Arx et al. [35] recently reported 8.1% of neurosensory disturbances after 6 months in patients who underwent such procedures.
For some systemic conditions, it was reported that persons can be more prone to sensory disturbances. Peripheral neuropathy in people with scleroderma is thought to be rare, however, nerve conduction studies showed abnormalities in patients with a mean disease duration of 10 years or longer [29]. Polyneuropathies in mild or severe diabetic patients often cause subclinical damage of the trigeminal nerve. Moreover, their sensory complaints in the perioral area often remain unnoticed and the dysfunction undiagnosed [9]. Age by itself also has an influence, especially when repair is concerned. The younger the subject, the greater the degenerative response but the quicker and more complete the overall recovery [7].
Tactile threshold assessment reveals that detection of monofilaments up to 0.023 g (monofilament number 2) can be considered normal in the orofacial region [14, 37].
Neurophysiological recordings of the masseter reflex, the mental nerve blink reflex, or evoked potentials are all useful in evaluating trigeminal nerve damage. This was specifically shown for damage of the inferior alveolar nerve [19, 32, 33]. However, evaluation of nerve damage in the symphyseal region was not reported in the literature. The number of nerves innervating this region, the vicinity of interfering structures (e.g., tongue, saliva, and labial muscles), and anatomical variations make this a difficult but challenging task.
In humans, several reports show that during the stimulation of peripheral sensory limb nerve examined after surgical repair (time span between 5 and 20 years), the sensory function remained deficient and often included abnormal sensory disorders [1]. It must also be considered that the loss of tactile sensitivity after surgery is not always reflected in abnormal psychophysical test results [13]. Furthermore, it is also possible, as shown by the collected data in this study, that an abnormal test result, particularly the light touch sensation test, does not reflect clinical reality. In other words, the patient does not always detect the loss of sensitivity.
It should be stressed that the selection of a control group of patients treated by means of two implants in the symphyseal region was principally motivated by the intent to have an age- and gender-matched group. The selection of a control group among patients treated with the Novum® system but without neurosensory complaints would not have allowed this.
It must also be considered that sometimes thresholds of the psychophysical test are not easy to reach in the oral cavity; the devices were not originally designed for this region, particularly light touch sensation [33].
Not less important is the fact that 95% (n=17) of the affected population considers that the treatment benefits outweigh the transient disadvantages, and that 18/19 of the patients consider that they would follow an implant surgery again if they knew beforehand the changes in sensation after the surgery.
There are no data in literature to compare neurosensory disturbances after immediate loading with those after the two-stage protocol. It is clear that it is difficult for the patient to distinguish between postoperative inconveniences, early functional adaptation, and real neurosensory dysfunction. Only a differential objective diagnosis can do so.
A meticulous preoperative planning of the surgery, even in an improperly so-called safe region of the jaws, like the symphyseal region, might avoid many neural disturbances [18].
In this perspective, the use of cross-sectional images and the transfer of the planning to the operative field may be considered [34].
Conclusions
The use of a questionnaire to determine the presence or absence of a problem after a medical procedure is easy and inexpensive; but to clarify the type, magnitude, extension, and eventual persistence of the neurosensory disturbance, the use of objective methods (i.e., psychophysical methods) in the evaluation of a population affected by any sensory disturbance, when complaints are detected, is highly recommended.
The objective follow-up revealed that patients are often not impaired by, and even not aware of, neurosensory dysfunctions after implant surgery in the anterior mandible. Objective tests indicate however that tactile threshold levels may be elevated after such surgery. None of the patients suffering from this impaired tactile function seems to have functional deficits resulting from it.
Based on these data, proper preoperative planning using cross-sectional imaging can be advised even for surgical procedures in the symphyseal region.

neurosensory disturbances
psychophysical tests
oral implants
mental nerve
symphysis


Algorithm for the classification of multi-modulating signals on the electrocardiogram

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of aj(t), uj(t), oj(aj) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of aj(t), uj(t) change the source into the periodic signal and τj = oj(aj) confines its harmonics into a few instantaneous components at τj being a common instant on two scales between t and τj. As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among aj(t), aj+1(t) and uj+1(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.
Introduction
The electrocardiogram (ECG) contains well-known PQRST(U) pattern. Its morphology has been extensively studied since the technique of recording was introduced in the beginning of the twentieth century. The use of computers for ECG recording enabled studies of dynamic properties of the ECG. In particular, two important characteristics have been revealed: amplitude and frequency modulation.
The modulation of the cardiac cycle is extensively studied based on the signal that is generated from intervals between consecutives R-peaks, known as heart rate variability (HRV). Numerous approaches have been proposed and the signal have been decomposed in various ways—analyzing their spectral properties by frequency and time-frequency methods such as Fourier transform [2], autoregressive spectral estimation [11], wavelet transform [9, 17], or their scaling properties [6].
The amplitude characteristics and amplitude modulation received less attention, especially because the notion of the ECG amplitude is ambiguous: the heart as a source of the signal is moving and the signal is collected from the surface of the body that is moving as well. In addition, it is usually recorded as a 2-D projection. However, it has long been recognized that one of the sources of the amplitude modulation—the respiratory related movement of the thorax can be usefully used to extract the information about the respiration.
The influence of respiration on recording cardiac potentials was reported as early as in 1967; Flaherty et al. [5] and Moody et al. [13] were probably the first to discuss the possibility to derive the respiratory signal from the ECG. Since then several algorithms were proposed to reconstruct respiration by demodulating the ECG signal (e.g. [1, 4, 8, 10]).
The ECG-derived respiration signal either based on changes in heart rate or based on direct effect of respiration on the ECG waveform has been particularly used in studies of obstructive sleep apnoea [3, 16].
The problem to reconstruct waveform from the discrete Fourier analysis is that the biological signal has complicated properties; being non-stationary wave, including random noise and having interactive components. However, though people rarely notice, the essential problem is that there is no way to gather impartial and statistical samples. That is why samples from an unique sampling frequency are biased for spectral analysis even if fine sampling.
The purpose of this study is to introduce an algorithm to classify the multi-modulating waveforms on ECG signals. The algorithm is composed by the three particular scale transforms and the statistical Fourier transform (SFT) with impartial and statistical samples [12]. As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals simultaneously by particular transformations.
Algorithm
The principle of this algorithm is the selection of three particular scale transforms. Two scales convert modulating waveforms into the periodic signal and the other converts the periodic components into a few terms. The role of the SFT is to hold sample information for any transforms.
Scale transforms aj(t), uj(t) and oj(t) on modulating signal
Since a source sample at any instant tk has the mutual phase relationship of instantaneous components on the SFT, any scale transform including permutation expresses the same sample as own instantaneous components by each scale (Appendix 1).
Three scale transforms aj(t), uj(t), oj(t) are adopted to the periodicity and to the reduction of Fourier components.
aj = aj(t) is a scale to match time intervals in the standard’s intervals and is called adaptive scale and uj = uj(τj) is a scale to match the magnitude spans in the standard’s magnitudes. Those scales convert the source x′(t) into the standard xj(t) as xj(t) = x′(aj)/uj(aj). aj(t) and uj(aj), then, are defined as a join of each akj(t) and ukj(akj) (k = 1,..., kj·) when the standard repeats kj times in a window Tj.
oj = oj(t), called inherent scale, is a timescale to convert the standard xj(t) into the Fourier series of rj components passing through 2rj poles of xj(t). The scale transform τj(t) is defined as the composite transform as follows (Appendix 2):
The transformed signal (x′(τj) − εj(τj))/uj(τj) occupies rj instantaneous components of the skipped ikjth term in a window Tjincluding kj modulations. While, instantaneous components of the εj(τj)/uj(τj) scatters into broad components from the 1st to the nth terms. As a result, (x′(τj) − εj(τj))/uj(τj) fits the scales τj, uj and εj(τj)/uj(τj) unfits scales τj, uj, and then, the source x′(t) is decomposed into two class modulations of x′(τj) − εj(τj) and εj(τj) (Appendix 3).
That is, x′(τj) − εj(τj) is extracted by vj(τj) = Σuj(τj)hij(τj) from the ikjth (i = 1,.., rj) components and εj(τj) is extracted by the other εj(τj) = Σuj(τj)hij(τj) except the ikjth components when n instantaneous components of x′(τj)/uj(τj) are defined as hij(τj). The source x′(t), therefore, is decomposed into x′(t) − εj(t) and εj(t) by inverse scale transform t = t(τj) under an adequate uj(τj).
The magnitude scale ukj(τj) is defined as an expression of the magnitude pattern in each window Tkj when the signals vj(τj)(=x′(τj) − εj(τj)), εj(τj) and uj(τj) are expressed by {vkj(τj)}k, {εkj(τj)}k and {ukj(τj)}k (k = 1,.., kj), and each changes its waveform independently. Especially, vj(τj) is expressed by a constant value uj(τj) = ukj in each window Tkj when its magnitude modulation takes a similar figure with the standard (Appendix 4).
Iterative scale classification of modulating signals
The timescale transform of the residual εj(t) is defined as τj+1(t) = oj+1(aj+1(t)) when several waves of {εkj(t)/ukj+1(t)}k in εj(t)/uj+1(t) reappears within a window Tj+1(Tj+1 ≤ Tj). εj(t) is analyzed as the j + 1th modulation with the iterative algorithm as εj(t) − εj+1(t) and εj+1(t) under aj+1(t), oj+1(aj+1(t)), uj+1(t) and xj+1(t) (Appendix 2).
When x′(t) is m classes of Fourier series with terms of r1, r2,..., rm (n = r1 + r2 +
 + rm), it expresses Eq. 2.
In Eq. 2, t = t(τj) is the inverse scale transform of the jth scale τj to scale t, vj(t) is a Fourier series of the jth class modulation, and εm(t) is the mth residual signal. Equation 2 shows that x′(t) is reconstructed completely by periodic Fourier series vj(τj) on scale τj (j = 1,2,..., m).
Quantity of information on the modulating signal
The relation between the quantity of information (3) and the modulation (4) is derived from formula (2).
Since the probability of components on the jth class modulation is to be pj = rj/n, the probability to encounter x′(t) with m modulations is p(r1,r2,..., rm) = p1r1, p2r2,..., pmrm which relates to the geometrical mean
. Equation is as follows (Appendix 5).
It clearly shows that the quantity of information on multi-modulating signal is m itself.
When hij(τj) is the ith instantaneous Fourier component of standard xj(τj) in scale τj and hij(t) is its modulating component in scale t, the morphological relation is given by the following Formula (4):
In Eq. 4, ∂τj/∂t is the Jacobian. Therefore, formula (4) shows that the modulation of every component at scale t is expressed by scale’s modulations on its magnitude uj(τj) and its time τj(t) = oj(a(t)). Especially, the adaptive scale aj(t) describes composite transform aj(t) = c1(c2(
cn(t))) and that of exchanged orders aj(t) = cn(
c2 (c1 (t))), so the sth scale cs(t) will express its specific modulation from the standard such as modulations of PQ, QRS, QT and RR.
Materials and methods
The analysis of scale classification of the three modulating signals on V4 lead was carried out by using the statistical Fourier analysis with maximum terms of 256 with A/D converted 16-bit samples of the sampling frequency 20 kHz over successive 50 s. To gather independent samples with 256 terms, samples needed to be accumulated by the high speed 20 kHz of A/D converter with a large dynamic range of 16 bit to avoid the deform of a tiny respiratory signal (Fig. 1).
Fig. 1Synchronous frequency samplings on scale t and τ
The ECG standard xj(t) was selected from the source x′(t) without breathing. On the other hand, x′(t) with natural breathing and the respiratory flow  are recorded simultaneously by using Number 3 Fleisch pneumotachograph. Both the ECG and the respiratory scale transforms were the same as in Fig. 2a, b of Appendix 2. The ECG x′(t) − εj(t) and its baseline wander εj(t) were analyzed when uj(τj) = 1 and the respiratory standard xj+1(t) was selected from stationary sequences of εj(t) in 16 respiratory cycle. (εj(τj+1) − εj+1(τj+1))/uj+1(τj+1) and εj+1(τj+1)/uj+1(τj+1) were analyzed when uj+1(τj) of the formula (5) in Appendix 4 and lung ventilation Vj+1(t) and its residual εj+1(t) were determined.
Fig. 2ECG and respiratory standard waves and specific points on scale t. a Cardiac standard, b respiratory standard, and c detecting dots on a part of the source
Results
Signals corresponding to every process on the scale classification of m = 2 under constant magnitude scale of uj = 1 are illustrated in Fig. 3 with five cardiac periods. That is, intervals among specific points in source x′(t) in Fig. 3a are transformed into the same intervals of the standard signal with the adaptive scale aj(t) to x′(aj(t)) of Fig. 3b and with the inherent scale τj(t) to x′(oj(aj(t))) of Fig. 3c. The transformed signal x′(τj) is decomposed into statistical Fourier components on a fundamental period of 53 cardiac intervals. The transformed signal xj′(τj) in Fig. 3d is a reconstructed signal from the 53rd, 106th, 159th and 212th components, and its residual εj(τj) in Fig. 3e from all other components. The extracted ECG xj′(t) in Fig. 3f and the base line wander εj(t) in Fig. 3g are the inverse scale-transformed signals from scale τj to scale t. When tiny-leak signals on the εj(t) synchronized with intervals of QT are replaced with linear trends, the figure of pattern εj(t) takes that of the lung ventilation in Fig. 3i. The smoothing signal, Vj(t) in Fig. 3h is a filtered signal of εj(t) with low-pass filter (LPF) under 30 terms (0.6 Hz). Vj(t) is illustrated with enlarged four times. The signal V(t) in Fig. 3i is the lung ventilation curve which is an integral signal of the direct measurement of the respiratory flow Fig. 3Classified process of modulating signal of the algorithm
Any differences of P ∼ U waveforms between the source x′(t) and reconstructed ECG x′j(t) bear getting information, and also the differences between ventilation V(t) and baseline wander Vj(t) are small when frequencies lower than breathing are neglected. x′j(t) provides time-interval information on Minnesota codes better than that of x′(t) clearly in visual.
Correlation coefficients ρ between the signal Vj(t) and V(t) are calculated by each ventilation with scatter plots in Fig. 4a, and are classified by P ∼ U waves of 53 cardiac signals in Table 1a and by respiratory phases of 16 ventilations in Table 1b. Total ρ takes even values (ρ ≈ 0.82) and has less interference between P ∼ U waves in cardiac cycles. However, it takes low values (ρ ≤ 0.5) at the transient regions of I, III between the inspiration and the expiration corresponding to loops in Fig. 4a. The inspiration and the expiration except transient regions are statistically dominant at higher value (ρ ≈ 0.95). It is clear that the scale classification extracts the electrical activity of the heart with constant magnitude scale of uj(τj) = 1 in hole window of Tj. The extracted baseline wander is constructed with lung ventilation and other biological information.
Fig. 4X–Y loop among modulating Vj(t), Vj+1(t) and V(t)Table 1Correlation coefficients ρi among Vj(t), Vj+1(t) and V(t) on cardiac and respiratory stagesCardiac stagesPQQRSST-TBase lineTotal(a) Correlation coefficients ρi between Vj(t) and V(t) on ECG stages ρi0.83280.82430.81490.81510.8244 Number of samples13,56612,69033,01843,553102,827Respiratory stagesI II III VI V Total(b) Correlation coefficients ρi between Vj(t) and V(t) on respiratory stages ρi0.39900.97880.57720.99700.95460.7273 Number of samples25,74725,21025,92814,57411,558103,017(c) Correlation coefficients ρi between Vj+1(t) and V(t) on respiratory stages ρi0.99230.65890.99430.80320.73050.91648 Number of samples25,28622,81621,16315,15518,077102,497I inspirations before midpoints, II inspirations after midpoints, III expirations before midpoints, VI expirations after midpoints, V expirations after inflection points
The magnitude scale transform is applied to the scale classification of the base line wander Vj(t) with a respiratory standard in Fig. 2b. Scale classification of m = 4 is illustrated in Fig. 5, the source x′(t) in Fig. 5a, the extracted ECG xj(t) in Fig. 5b, the extracted respiratory signal Vj+1(t) in Fig. 5c, the residual εj+1(t) in Fig. 5d, the leak ECG ε(t) over 0.6 Hz of εj(t) in Fig. 5e and the ECG of xj(t) + ε(t) in Fig. 5f. The signal V(t) in Fig. 5g is 16 measured respiratory ventilations. The magnitude scale Δuj+1(t) is illustrated in Fig. 5h.Vj+1(t) and εj+1(t) are displayed in the reverse because the extracted respiratory waveform is a visually reversed V(t). εj+1(t) is similar to base-line trends of V(t) which relate to the thoracic gas volume (TGV).
Fig. 5Scale classification of the four modulating signals in an electro-cardiac signal
The scatter plots of 15 lung ventilations between Vj+1(t) and V(t) take the similar pattern as illustrated in Fig. 4b. Correlation coefficients ρ in Table 1c takes higher value than the others though transient regions change I, III into II, IV.
The frequency distributions of the amplitude components on |V(f)|, |Vj+1(f)|, |εj+1(f)| and that of phase shifts Δφ(f) between V(f) and Vj+1(f) are illustrated in Fig. 6. On frequency ranges from 0.2 to 0.5 Hz, amplitude distribution in |V(f)| and |Vj+1(f)| takes similar pattern and those of phase shifts are invariant near 0°. Amplitude distribution in |εj+1(f)| has not only the lower frequency components under 0.06 Hz corresponding to the trends of TGV but also the leak components of the lung ventilation from 0.25 to 0.5 Hz. The components of |εj+1(f)|, moreover, has the harmonics among 0.04, 0.1, 0.18 and 0.28 Hz though those harmonics disappear in |V(f)| and |Vj+1(f)| at least.
Fig. 6Frequency distributions of the measured V(f) and the extracted Vj+1(f), εj+1(f)
As a result, the time and magnitude scale is effective to classify modulating signals from the higher frequency Vj+1(t) to the lower frequency εj+1(t) because a residual εj+1(t) has less interference of the higher Vj+1(t) and successive sequential εj+1(t) will supply the signal for detecting other lower modulation.
Discussion and conclusion
A discrepancy between time domain and frequency domain analysis happens commonly by using biased samplings for discrete signal processing in various way such as Hilbert transform [2] and wavelet analysis [9] relating with an unique sampling frequency. Moreover, the mathematical concepts of one-to-one correspondence, n unknown amplitudes and phases are derived from 2n samples, hinders to orientate the statistics in the signal processing.
The SFT, however, has no contradiction between Fourier analysis and the statistics which resolute components with the same signal-to-nose ratio (SNR) for any signal decreasing amplitude components with order 1/f such as ECG signal, which is unsatisfied in the autoregressive spectral estimation [11]. The SFT component, moreover, is given independently by a time trace of instantaneous components at each instant as shown in Appendix 1. That is, its amplitude and phase are not calculated by the inner products between the source and trigonometric bases but by the extracted traces of instantaneous sin-wave themselves [12]. The concepts of signal arrangements at series {tk} is another thing on the SFT because the replacement between ti and tj needs the replacements of all the synchronous frequency samplings between ti and tjto hold the same instantaneous components. The summation of replacements, then, is about
. In other words, the SFT supplies a signal ensemble arranging samples of the series {tk}. In this study, discrete samples from A/D converter of 20 kHz satisfy the independency as shown in Appendix 6. As a result, the random noise including a signal is not analyzed as a white noise but as inherent components characterizing arrangements. Therefore, the reconstructed waveform of a baseline wander holds a prototype even if it includes many random noises.
The necessary condition of the spectral analysis known as Nyquist’s condition [14, 15] becomes precise on the SFT as shown in Appendix 3, which confirms its condition statistically. The potentiality to scatter the aliased components impartially discriminates the waveforms under the unsatisfied Nyquist’s condition. In this algorithm, while the inherent scale transform converges to the standard xj(τj) with four skipping components, others εj(τj), either continuous or random, disperse components into 256 terms.
Since the scale-transformed samples on the SFT are gathered from a whole window at any instant, those samples reflect its scale transform better than any local one. The random and leak signal on the extracted ECG xj′(t) are reduced into the tiny level under a stable behavior of the random noise. The 256 terms is enough to analyze scale-transformed signal in a window under 53 cardiac cycles because transformed components with the ranges, 8 ≤ kj ≤ 53, takes almost the same figures. The high-resolution analysis, therefore, is accomplished by the relative high SNR from those instantaneous components.
The problem on the scale transforms is to be adequate scales, not to be expressing the true biological modulation. aj+1(t) is, however, selected a traditional scale for non-stationary sequences in each respiratory cycle which carry information stored in Fourier phases as already indicated in [6] and aj(t) is selected from cardiac cycles without breathing in addition to the non-stationary sequences. As a result, oj(aj) and oj+1(aj+1) make those mutual relationship of phases more simple relations. Taking own standard characterized with number of poles is also reasonable to determine the waveform reconstruction and to discriminate the own HRV. Moreover, the lost waveform is scattered into the other components without losing the morphologic information such as the leak ECG waveform.
It is well known the volume conductivity of the thorax takes inequality and changes the electro cardiac potentials on the thorax’s surface with respiratory movements [1, 5].
In the scale classification of m = 4, it indicates that a constant magnitude scale ukj = 1 produces unchangeable heart-activity xj′(t) so that the signal εj(t) appears in respiratory modulations from their changes of the conductivity. It suggests that the standard without breathing follows the formula (2) of the signal classification and the source follows the formula (4) which expresses the modulating standard with Jacobian, ∂τj/∂t. A composite scale cs(t), therefore, is expected to express the inherent modulation from the standard such as PQ, QT and RR. That is, the spectral analysis of cs(t) on RR provides the distribution of the HRV without a tachogram which is expressed in other ways by others [2, 9, 11]. The extracted ECG has the potentiality to detect R–R modulations of the heart itself.
The algorithm extracts the lung ventilation curves {Vj+1(t)}k from V4 lead as changing the thoracic gas volume [1, 4]. The difference of amplitude distribution between V(t) and Vj+1(t) from spectral analysis takes the same pattern around 0.3 Hz [8]. The phase difference between V(t) and Vj+1(t), however, occurs in the transient regions between the inspiration and the expiration which deduce from inadequate respiratory standards, false respiratory peaks and the gas compression in the thorax [7]. It is effective to determine specific components, such as harmonics of εj+1(t) around (0.04, 0.1, 0.18 and 0.28 Hz) in cardiovascular responses [2, 17].
Information of the partial ventilation is also expected from other 12 leads [8]. The respiratory Jacobian ∂τj+1/∂t, like that of ECG, has the potentiality to detect respiratory modulation itself as a part of the HRV and uj+1(t) is expected to detect the amplitude modulation such as sighs distribution. The residual εj+1(t) supplies information of waveforms not only of the thoracic gas volume but also of the blood flow relating to cardiovascular responses [2, 11, 17].
It can be concluded that this algorithm reveals the simultaneous measurements of ECG and respiration. The comparison with the frequency distribution and the waveform evaluates new modulations clearly, such as ukj+1 for detecting changes of the tidal volume (TV) and for finding TGV and cardiovascular responses. This contribution of the algorithm indicates diagnostic potentials on the sleep apnoea from ECG recordings [16].

electrocardiogram
scale transform
fourier transform
heart rate variability
respiratory waveform


Thoracic arachnoiditis, arachnoid cyst and syrinx formation secondary to myelography with Myodil, 30 years previously

Spinal arachnoiditis can rarely occur following irritation from foreign body substances, including certain oil based contrast agents used for myelography. We describe a patient with thoracic arachnoiditis, arachnoid cyst and syringomyelia, 30 years following a myelogram with Myodil. A 62-year-old female presented with chronic thoraco-lumbar back pain, a spastic paraparesis and sphincter disturbance. She had undergone a myelogram with Myodil, 30 years previously for investigation of back pain. A MRI scan revealed evidence of arachnoiditis, thoracic syringomyelia (T6–T8) and an anteriorly placed, extramedullary, arachnoid cyst at T10–T12, compressing the cord. At surgery, T7–T10 thoracic laminectomies were carried out and syringo- and cysto-subarachnoid shunts were inserted. At 12 months follow-up, the sphincter disturbance, lower limb weakness and mobility problems had almost resolved. Although, the use of oil based contrast agents such as Myodil has been discontinued, the present case illustrates some of the rare sequelae of its use, manifesting decades later. Aggressive surgical intervention produced symptomatic benefit.
Introduction
Causes of spinal arachnoiditis include infection, trauma, tumours, surgery and irritation from foreign body substances [3, 4, 9]. The latter include older radiographic contrast agents such as isophendylate (Myodil and pantopaque), which was widely used for myelography in Europe and North America until the 1980s [3, 4, 6]. We describe a 62-year-old lady who presented with thoracic arachnoiditis, arachnoid cyst and syringomyelia, 30 years following myelography-using Myodil.
Case report
A 62-year-old female presented with a 2 year history of chronic thoraco-lumbar back pain, and a progressive and bilateral lower limb weakness. This was associated with spasms affecting her legs and abnormal sensation on the soles of the feet. There was a 3 month history of urinary and faecal urgency. She was asymptomatic in the upper limbs. She had undergone a Myodil myelogram; 30 years previously as part of investigations for lumbosacral back pain. The myelogram was normal and her symptoms resolved one year later with conservative measures.
On examination, there was spastic paraparesis, with an up going plantar response on the right. There was bilateral weakness of hip flexion (MRC 4, motor power) and she had difficulty walking independently. Light touch, pinprick and temperature sensations were preserved, but proprioception was reduced at the toes bilaterally. Cranial nerves and upper limbs were neurologically intact.
A MRI scan of the thoraco-lumbar region revealed an intramedullary lesion extending from T6–T8 and an anteriorly placed extramedullary, intradural lesion at T10–T12, compressing the thoracic cord (Fig. 1). The abnormal areas were non-enhancing, low signal on T1 weighted images and high signal on T2 weighted images, in keeping with a thoracic syrinx and an arachnoid cyst, respectively (Fig. 1). Axial MRI scans at the lumbar levels revealed clumping of the nerve roots, suggestive of arachnoidal adhesions, although Myodil droplets were not seen (Fig. 2). Plain X-rays of the thoraco-lumbar spine, full blood count and serum inflammatory markers were normal.
Fig. 1Sagittal T1 weighted (a) and T2 weighted (b, c) spin echo MRI scans of the thoracic region, demonstrate an intramedullary lesion from T6–T8 and an anterior extramedullary, intradural lesion at T10–T12. The abnormal areas were low signal on T1 weighted images and high signal on T2 weighted images, in keeping with a syrinx and an arachnoid cyst, respectivelyFig. 2Axial T2 weighted spin echo MRI scans at the L2 (a) and L4 (b) levels reveal adhesions and clumping of the lumbar roots
As she was symptomatically deteriorating, surgical exploration was undertaken in the form of a T7–T10 thoracic laminectomies. On opening the dura, dense arachnoidal adhesions were apparent. These were partially released to enter the anteriorly placed arachnoid cyst at T10 (Fig. 3a) and the syrinx cavity at T8 level (Fig. 3b). Fluid similar to cerebro-spinal fluid (CSF) was released under pressure from both the arachnoid cyst and the syrinx. Syringo- and cysto-subarachnoid shunts were inserted. Post-operative recovery was uncomplicated and at 12 months follow-up, the sphincter disturbance, lower limb weakness and mobility problems had almost resolved.
Fig. 3Intra-operative view of the thoracic spinal cord under the operating microscope showing, dense arachnoidal adhesions (grey arrows), the anteriorly placed arachnoid cyst at T10 level (a, black arrow) and the syrinx cavity at T8 (b, black arrow)
Discussion
The present case is a rare report of widespread arachnoiditis, with the formation of an arachnoid cyst and syrinx in the thoracic cord (radiologically evident at operation), secondary to the use of Myodil, 30 years previously. Although, we did not observe Myodil droplets on the MRI scans, in the absence of other risk factors, the history of Myodil usage seems the likely contributory agent for the pathological changes noted [3, 4, 6]. Aggressive surgical intervention led to symptomatic benefit.
Spinal arachnoiditis following myelography with certain agents such as isophendylate (Myodil and pantopaque) is a rare (approximately 1%) but a recognised complication [3, 4, 6]. This risk appears to be greater with oil based contrast agents such as isophendylate, than other modern water based agents such as metrizamide (amipaque) and iohexol (omnipaque) [3, 4]. Furthermore, the risk of arachnoiditis is augmented by previous spinal surgery and existing spinal canal stenosis at adjacent levels [3, 4]. However, this was not applicable to our patient.
In the present case there was radiological and operative evidence of not only arachnoiditis, but also an extramedullary arachnoid cyst and intramedullary syrinx formation. There have been rare reports of spinal arachnoiditis, arachnoid cyst and syrinx formation, but this typically followed spinal surgery or trauma [1, 2, 5, 8]. The present case is unusual in that our patient developed the same sequelae, but after myelography with Myodil.
While the precise pathophysiology of these abnormalities is unclear, they may be interrelated. Thus, the arachnoidal adhesions may interfere with CSF flow pathways around the thoracic cord, causing trapping of CSF and the formation of the arachnoid cyst [3]. Moreover, the altered dynamics of CSF flow may also cause a pressure differential, promoting the formation of the syrinx [2, 5]. Although, only a partial release of the dense arachnoidal adhesions was possible in our patient, drainage of the cyst and the syrinx produced symptomatic relief [7].
In the modern era of computed tomography and magnetic resonance imaging, the need for myelography has diminished and the use of isophendylate has been discontinued. Nevertheless, the present case illustrates some of the rare sequelae of its use in the past, manifesting decades later.

arachnoid cyst
myodil
syringomyelia
thoracic spine


Lifetime cumulative number of menstrual cycles and serum sex hormone levels in postmenopausal women

Objective Lifetime cumulative number of menstrual cycles is related to breast cancer risk. The aim of this study is to investigate the relation between this index and serum sex hormone levels in postmenopausal women.
Introduction
Breast cancer is strongly related to various hormonal factors. We know that different hormones play a key role in the pathogenesis of invasive breast cancer in both, premenopausal and postmenopausal women [1–4], likely representing a direct effect of circulating hormones [5–7].
The patterns of risk associated with reproductive history suggest that a prolonged exposure to ovarian hormones increases breast cancer occurrence [8]. It has been well established that early age at menarche, late age at menopause, null parity and late age at first full term pregnancy (FFTP) are important risk factors for breast cancer [9–11]. These characteristics may have long lasting effects on hormone metabolism, although data regarding reproductive characteristics and hormone levels are still inconclusive [12–16]
In an effort to measure the lifetime exposure to endogenous hormones, the concept of the “lifetime cumulative number of menstrual cycles” was introduced as an index that could reflect total ovarian exposure [17, 18]. The “lifetime cumulative number of cycles” summarizes information such as menarche, parity, menopause, regularity pattern, breast feeding and oral contraceptive (OC) use. In previous studies we, but also others, related the cumulative numbers of menstrual cycles to breast cancer risk [17, 18]. Ovarian function, and therefore, “lifetime number of menstrual cycles” is defined by age at menopause, and age at menopause is related to postmenopausal estrogen levels [19]; with this rationale we studied the association between the “lifetime number of menstrual cycles” and hormone levels after the menopause. The hypothesis fits in, where a common factor at hypothalamic level may affect the speed by which oocytes are being used, increasing postmenopausal gonoadothopin levels and maybe, stimulating ovarian testosterone release from the remaining theca cells, as well as non ovarian estrogens from adrenal origin.
The aim of this study was to evaluate the relation between the “lifetime cumulative number of menstrual cycles” and the reproductive characteristics that contribute to this estimate on one side, and plasma concentrations of sex hormones on the other side, in order to elucidate the mechanism by which reproductive risk factors could influence hormone-related cancers.
Study design and methods
The Prospect-EPIC study is one of the two Dutch cohorts participating in the European Prospective Investigation into Cancer and Nutrition; a multi-center prospective study carried out in 23 centers from 10 European countries [20].
A detailed description of the study population and data collection of the Prospect-EPIC study has been published elsewhere [21]. Briefly, 17,357 women aged 49–70, residing in Utrecht and its surroundings were recruited through a regional, population-based, breast cancer screening program between 1993 and 1997. Base-line characteristics were collected on the basis of two self-administered questionnaires (regarding lifestyle and nutrition). The lifestyle questionnaire contains information on demographic characteristics, family history, past and current morbidity, reproductive history (gynecologic and obstetric), smoking habits, and physical activity. In addition, anthropometric measurements (height, weight, and waist and hip circumference) were taken as well as a 30 ml blood sample. Within 24 h, samples of 4 ml serum, 9 ml citrate plasma, 2 ml white blood cells, and 2 ml red blood cells were fractionated into 0.5 ml aliquots and stored at −196°C. Blood samples were successfully drawn from 97.5% of the participants [21]. All women signed a written informed consent and the study was approved by the Institutional Review Board of the University Medical Center Utrecht.
Of all Prospect-EPIC participants a 10% random sample (n = 1736) was taken and hormone measurements were done. For the present study, participants who used hormone replacement therapy (HRT) or OC in the previous twelve months were excluded (n = 311), as well as those women with history any cancer other than non-melanoma cancer (n = 25). From the remaining 1,400 participants only those who underwent natural menopause were eligible (n = 860) because only in them we had reliable information about ovarian activity not masked by the absence of menstrual cycles due to hysterectomy. Natural menopause was defined as the complete cessation of menstrual bleeding for 12 months preceding enrollment due to natural causes (according to self-report).
We computed the “lifetime cumulative number of menstrual cycles” of the women in this study. For parous women, the number of menstrual cycles before a FFTP was also estimated as we previously described [17]. Briefly, the “lifetime cumulative number of menstrual cycles” was computed from age at first menstrual period to age at menopause. Ages at menarche, at menopause and at FFTP were recorded as continuous variables. Age at regularization of cycles was computed according to the time between menarche and the establishment of regular periods (immediately after menarche, after 1, 2, 3, 5, or 10 years, after FFTP or never regular). The regularity pattern was defined categorically as 24, 26, 28, 30 and 32 day cycles, or always irregular. Information about OC use included: age at start, number of years of use and age at last use. Data about number of and age at each pregnancy, stillbirth, miscarriage and abortion, if any, were available, as well as the number of weeks of breastfeeding for each pregnancy.
We took all the previously mentioned variables into account when computing the “lifetime cumulative number of menstrual cycles”, considering that each pregnancy, stillbirth, miscarriage or abortion, resulted in an absence of cycles for a period of 36, 28 and 12 weeks respectively. We assumed that lactation results in a 6-week absence of cycles [22]. Menstrual cycles due to OC use were computed as 28 days duration (see Appendix). Considering that irregular cycles are a combination of long and short menstrual periods, the number of irregular cycles was computed according to each participant’s regularity pattern; the total number of irregular years was recorded in order to make further adjustments. The number of menstrual cycles was impossible to compute in the participants who reported to be always irregular or never having regular periods, therefore we added the “irregular” category as such in the analysis (See appendix).
Bounded levels of estrogens and androgens were measured, levels of estrone (E1), estradiol (E2), androstenedione, testosterone, sex hormone binding globulin (SHBG) and dehydroepiandrostenedione sulfate (DHEAS) were measured in plasma using commercially available double-antibody radioimmunoassay (RIA) kits (Diagnostic System Laboratories Inc., Webster, TX, USA). The following kits were used: E1: DSL-8700, E2: DSL-39100, androstenedione: DSL-4200, TST: DSL-4100 SHBG: DSL-6300 and DHEAS: DSL-2700. The intra-assay coefficients of variation were 5.6%, 3.9%, 4.3%, 7.7%, 3.0% and 5.2% respectively. The inter-assay coefficients of variation were 11.1%, 4.1%, 6.3%, 8.1%, 4.0% and 5.3% respectively. All hormone assays were performed by the laboratory of the Hormones and Cancer group at the International Agency for Research on Caner (IARC). Although technically SHBG is not a hormone, for reasons of convenience it will be referred as such. We calculated the free-androgen index (FAI) dividing testosterone by SHBG and multiplying by 100, the SHGB/E2 index was calculated dividing SHBG by E2.
Data analysis
Means with their standard deviation (SD) values, or frequencies of baseline characteristics were calculated. Concentrations of all hormones were logarithmically transformed to produce approximately normal distributions. To make the results easily interpretable, we transformed the mean and its 95% confidence interval (CI) back to their original scale, resulting in geometric means and 95% CIs. Geometric mean levels and 95% CIs were calculated for each hormone (SHBG, E1, E2, androstenedione, testosterone, DHEAS), SHBG/estradiol index (SHBG/E2) and free androgen index (FAI).
Using analysis of covariance models, geometric mean hormone levels were calculated for the different categories of cumulative number of menstrual cycles (lifetime and before a FFTP). The same analysis was also used for categories of age at menarche, age at menopause, parity, number of children and age of FFTP. In order to build the adjusted model we classified confounders in three categories as follows: Group A contained characteristics clearly related to hormone levels such as age, years since menopause and body mass index (BMI); Group B contained factors possibly related to hormone levels like OC and HRT use, smoking habits, physical activity and alcohol consumption; group C contained characteristics unlikely related to hormone levels such as marital status, education level and family history of breast cancer. We first adjusted for all confounders of type A, B, and C, then we built models by deleting B and C types of characteristics, without affecting validity but increasing precision. In the final model we included confounders in group A and B, therefore we adjusted for age at intake as a continuous variable, number of years from menopause to inclusion in the study, BMI at examination, use of HRT ever (Y/N), use of OC ever (Y/N), smoking habits, alcohol consumption and physical activity. Information on current alcohol use was obtained from the food frequency questionnaire, total alcohol consumption in grams per day was calculated from questions in the number of glasses of wine, beer, fortified wines, and liquor/spirits per day, week, month or year. One glass of any alcoholic beverage was assumed to contain 10g of alcohol. With the daily physical activity data from the general questionnaire, the Voorrips score was calculated (and was entered in the model as a continuous variable), this score has been developed and validated especially for women in this age group [23]. For analysis of the “lifetime cumulative number of menstrual cycles” and the “number of cycles before a FFTP” variables, we also adjusted for more than one year of irregular cycles (Y/N). Given that age at menopause can be determined using age at intake in combination of years since menopause; when analyzing age at menopause, the covariate years since menopause was not entered in the model.
To test the significance of the models’ linearity, median values in each category for every variable were computed accordingly. Subsequently these values were included as a continuous variable in a linear regression model to test for linear trends. Because there is no clear natural hierarchy, the “always irregular” category was not included when testing for trend for the “lifetime number of menstrual cycles” and the “number of menstrual cycles before a FFTP” variables. Additional mutual adjustments for reproductive characteristics did not show any difference in the observed trends. The Statistical Package for Social Sciences for WINDOWS, version 14.0 (Chicago, IL), was used for all statistical analysis, and all tests were two sided.
Results
General and reproductive characteristics of the Prospect-EPIC women that were included in this study are shown on Table  1. On average, participants were 60.2 years old at enrollment; had a BMI of 26.2 kg/m2 and the majority of them (69.4%) had at least low technical-secondary education. 113 (13.1 %) were nulliparous, and for the parous, the mean age for a FFTP was 25.5 years old. Mean age at menarche and menopause were 13.6 and 49.6 years old respectively, the time from menopause to inclusion, was on average 10.6 years. In 786 participants (91.4%) the lifetime cumulative number of cycles was calculated, as we had incomplete data regarding reproductive history in 74 women. The median number of lifetime menstrual cycles was 451.3, corresponding to 34.7 years of menstrual activity if considering an average of 13 cycles per year. From the 747 parous participants, 64 had incomplete data to compute the number of menstrual cycles before a FFT, in the remaining 683 participants (91.4%), the median number of menstrual cycles before a FFTP was 146.7 (approximately 11.3 years of menstrual activity). These results are consistent with the previous data on such estimates for a larger sample of the Prospect-EPIC participants as we previously published [17]. Table 2 shows the geometric mean hormone levels for all participants with 95% CIs of SHBG, E1, E2, androstenedione, testosterone, DHEAS, SHBG/E2 and FAI.Table 1General and reproductive characteristics of the study participants (n = 860) Mean (SD)General characteristicsAge at enrollment60.2 (5.5)BMI26.2 (4.2)Waist Hip ratio0.797 (0.59)Length (cm)163.7 (6.1)Weight (Kg)70.1 (11.9)Educational level (%)- Primary education263 (30.6)- Low technical, secondary384 (44.6)- High technical, secondary98 (11.4)- Academic115 (13.4)Ever married (%)805 (93.6)Cigarette smoking habits (%)- Never smoked402 (46.7)- Past smoker275 (32)- Current smoker183 (21.3)Mother or sister with BC (%)123 (14.3)Reproduction characteristicsAge at menarche13.6 (1.6)Nulliparous (%)113 (13.1)Age at FFTPa25.46 (3.8)Number live born childrena2.9 (1.4)Age at menopause49.6 (4.0)Years since menopause10.6 (6.0)Oral contraceptive use (%)384 (44.6)Years of OC useb6HRT use (%)66 (7.7)Years of HRT usec1Lifetime number of menstrual cycles(n = 786)165 (21.0)  ≤415177 (22.5)  416–453171 (21.8)  454–490161 (20.5)  ≥491112 (14.2)  Always irregulardNumber of menstrual cycles beforeFFTP (n = 683)  ≤118167 (25.5)  119–147160 (23.4)  148–185118 (17.3)  ≥186143 (20.9)  Always irregulard95 (13.9)a Among parous womenb Median, Among OC usersc Median, Among HRT usersd Participants in the always irregular category, reported to be never regularTable 2Geometric mean serum sex hormone and SHBG levelsHormoneMean, (95% CI)SHBG μg/ml18.1 (17.2–19.2)Estrone pg/ml15.2 (14.48–16.0)Estradiol pg/ml8.5 (8.2–8.8)Androstenedione ng/ml0.47 (0.45–0.49)Testosterone total ng/ml0.26 (0.25–0.27)DHEAS ng/ml455.1 (432.9–478.4)SHBG- Estradiol index (μg/pg)0.47 (0.44–0.50)Free androgen index [(Testosterone /SHBG*100 (ng/μg*100)]4.6 (4.4–5.0)
Table 3 shows the unadjusted geometric mean hormone levels and 95% CI for each hormone according to some general and reproductive characteristics. When testing for trend participants in the lower quartile of “lifetime cumulative of menstrual cycles” had higher levels of SHBG, androstenedione, testosterone and DHEAS than the participants with the highest quartile (P trend = 0.015, 0.003, 0.052 and 0.035 respectively), and lower levels of SHBG/E2 (P trend = 0.031).Table 3Hormones unadjusted means (95%CI) according to patient characteristics SHBG (μg/ml)Estrone (pg/ml)Estroidal (pg/ml)Androstenedione (ng/ml)Testosterone (ng/ml)DHEAS (ng/ml)SHBG/E2-indexFA-indexLifetime number of menstrual cycles    <=41519.8 (17.3–22.5)15.6 (13.9–17.4)8.5 (7.8–9.2)0.56 (0.50–0.64)0.29 (0.26–0.31)515.3 (458.6–579.1)0.431 (0.366–0.509)4.7 (4.05–5.46)    416–45319.5 (17.1–22.1)13.5 (12.1–15.0)8.5 (7.8–9.2)0.45 (0.39–0.50)0.25 (0.23–0.27)448.1 (400.3–501.5)0.435 (0.371-0.511)4.3 (3.73–4.97)    454–49017.4 (15.3–19.8)16.1 (14.4–17.9)8.8 (8.1–9.5)0.43 (0.39–0.49)0.25 (0.23–0.27)436.4 (389.1–489.3)0.504 (0.428–0.593)4.7 (4.09–5.49)     >= 49115.9 (13.9–18.2)15.9 (14.2–17.9)8.7 (7.9–9.4)0.44 (0.39–0.50)0.25 (0.23–0.27)434.8 (386.4–489.3)0.542 (0.458–0.641)5.1 (4.36–5.89)    Always irregular §16.6 (14.1–19.5)14.9 (13.04–17.1)7.9 (7.1–8.7)0.49 (0.40–0.54)0.26 (0.23–0.28)425.4 (69.2–490.2)0.476 (0.389–0.582)4.9 (4.07–5.85)    P-trend0.150.4550.6480.0030.0520.0350.0310.406Number of menstrual cycles before a FFTP*    <= 11819.5 (17.1–22.2)14.8 (13.2–16.6)8.3 (7.6–8.9)0.48 (0.42–0.54)0.27 (0.24–0.24)472.1 (420.1–530.6).426 (.362-.500)4.5 (3.86–5.15)    119–14718.2 (15.9–20.8)14.9 (13.2–16.7)8.6 (7.9–9.3)0.48 (0.42–0.54)0.26 (0.23–0.23)488.8 (433.8–550.7).471 (.399-.555)4.6 (3.96–5.32)    148–18517.3 (14.8–20.1)15.9 (13.9–18.2)9.2 (8.3–10.0)0.43 (0.37–0.50)0.26 (0.23–0.23)425.1 (369.9–488.4).529 (.437-.641)4.9 (4.17–5.89)    >= 18618.8 (16.3–21.6)14.5 (12.8–16.4)8.3 (7.6–8.9)0.45 (0.39–0.51)0.25 (0.22–0.22)419.4 (369.6–475.7)0.440 (0.370–0.524)4.4 (3.76–5.15)    Always irregular §15.5 (13.1–18.4)14.8 (12.7–17.2)8.0 (7.2–8.8)0.48 (0.40–0.56)0.27 (0.24–0.24)440.3 (377.1–513.9)0.515 (0.416-.638)5.5 (4.51–6.62)    P-trend0.5740.9920.9330.3490.3330.0870.5650.999Age at intake    <= 5418.8 (16.5–21.5)15.9 (14.3–17.8)9.4 (8.7–10.2)0.53 (0.47–0.60)0.28 (0.26–0.31)611.5 (547.5–682.9)0.499 (0.424–0.587)4.9 (4.19–5.62)    55–5918.9 (16.9–21.2)15.45 (14.1–16.9)8.6 (8.0–9.2)0.46 (0.42–0.51)0.25 (0.23–0.27)493.4 (448.6–542.7)0.453 (0.394–0.522)4.5 (3.92–5.04)    60–6417.3 (15.6–19.3)14.3 (13.0–15.6)8.3 (7.8–8.8)0.47 (0.43–0.52)0.26 (0.24–0.28)424.3 (387.5–464.5)0.480 (0.419–0.548)4.8 (4.26–5.42)    >= 6517.7 (15.8–19.8)15.4 (14.0–16.9)7.9 (7.4–8.5)0.44 (0.39–0.49)0.25 (0.23–0.27)363.9 (330.9–400.4)0.450 (0.391–0.518)4.6 (4.04–5.20)    P-trend0.3030.4490.0020.0410.106<0.00010.4990.834Age at menarche    <= 1118.9 (15.3–23.3)15.2 (12.7–18.2)8.0 (7.0–9.1)0.47 (0.39–0.57)0.28 (0.24–0.33)437.1 (363.9–525.2)0.423 (0.325–0.550)4.9 (3.93–6.29)    1215.9 (14.1–18.1)14.5 (13.0–16.1)8.8 (8.1–9.4)0.47 (0.42–0.52)0.26 (0.24–0.28)439.4 (393.6–490.5)0.549 (0.469–0.643)4.2 (4.51–5.99)    1317.5 (15.6–19.7)15.9 (14.4–17.5)8.3 (7.7–8.8)0.47 (0.42–0.52)0.26 (0.24–0.28)461.7 (417.2–510.9)0.472 (0.408–0.546)4.7 (4.10–5.32)    1418.8 (16.6–21.3)15.1 (13.5–16.7)8.6 (8.0–9.3)0.44 (0.38–0.48)0.24 (0.22–0.26)447.9 (401.7–499.4)0.458 (0.392–0.536)4.2 (3.69–4.88)    1520.8 (17.7–24.5)15.9 (13.9–18.2)8.5 (7.7–9.3)0.49 (0.42–0.57)0.28 (0.25–0.31)490.6 (426.2–564.7)0.408 (0.334–0.499)4.4 (3.70–5.32)    >= 1618.8 (16.1–21.9)14.6 (12.7–16.6)8.5 (7.7–9.3)0.53 (0.46–0.61)0.27 (0.24–0.29)457.1 (399.0–523.5)0.452 (0.372–0.549)4.6 (3.86–5.47)    P-trend0.0690.9430.8050.2620.9060.4140.1820.147Reproductive status    Parous17.5 (14.9–20.4)16.9 (14.8–19.4)8.8 (8.02–9.70)0.52 (0.45–0.60)0.26 (0.23–0.29)461.5 (402.0–529.7)0.506 (0.415–0.616)4.8 (4.0–5.7)    Nulliparous18.2 (17.2–19.414.9 (14.2–15.7)8.5 (8.13 8.76)0.47 (0.44–0.49)0.26 (0.25–0.27)454.1 (430.4–479.1)0.463 (0.429–0.500)4.7 (4.3–4.9)    P-equality of means0.6080.0830.4070.1680.8530.8310.4180.770Age at FFTP*    <= 2119.5 (16.5–22.9)15.7 (13.6–18.1)8.5 (7.6–9.3)0.48 (0.41–0.56)0.26 (0.23–0.29)481.3 (416.4–556.4)0.434 (0.355–0.531)4.4 (3.63–5.23)    22–2416.2 (14.3–18.2)14.7 (13.3–16.3)8.8 (8.2–9.4)0.47 (0.42–0.52)0.27 (0.25–0.29)468.1 (421.1–520.3)0.544 (0.470–0.631)5.2 (4.57–5.96)    25–2718.5 (16.5–20.3)15.2 (13.8–16.6)8.2 (7.6–8.7)0.47 (0.42–0.52)0.26 (0.24–0.27)454.9 (414.8–498.8)0.445 (0.391–0.506)4.6 (4.12–5.19)    >= 2819.7 (17.4–22.2)14.5 (13.06–16.6)8.5 (7.8–9.1)0.45 (0.40–0.50)0.25 (0.23–0.27)425.6 (382.4–473.6)0.429 (0.370–0.498)4.3 (3.75–4.90)    P-trend0.2960.4970.5450.4140.3380.1380.2580.337Number of liveborn    017.4 (14.9–20.4)16.9 (14.8–19.3)8.8 (8.0–9.7)0.52 (0.45–0.60)0.26 (0.23–0.29)461.5 (401.9–529.7)0.506 (0.415–0.616)4.8 (4.00–5.71)    117.5 (14.1–21.8)15.7 (13.1–18.9)7.9 (7.0–9.1)0.41 (0.33–0.50)0.24 (0.21–0.28)424.9 (351.1–514.3).456 (.346-.600)4.5 (3.48–5.69)    2185 (16.7–20.3)14.8 (13.6–16.1)8.8 (8.2–9.2)0.48 (0.43–0.52)0.26 (0.24–0.28)474.9 (436.4–516.8)0.473 (0.419–0.534)4.7 (4.21–5.23)    318.2 (16.2–20.5)14.9 (13.4–16.4)8.4 (7.7–8.9)0.45 (0.40–0.50)0.26 (0.24–0.28)459.1 (414.4–508.6)0.458 (0.395–0.531)4.6 (4.00–5.21)    >= 418.1 (15.9–20.4)15.0 (13.5–16.6)8.2 (7.6–8.8)0.48 (0.43–0.54)0.27 (0.24–0.29)425.7 (381.8–474.6)0.455 (0.389–0.532)4.7 (4.10–5.43)    P-trend0.7700.1740.2890.6240.6680.4320.4370.946BMI    <= 24.925.8 (23.7–28.2)13.3 (12.3–14.4)7.5 (7.1–7.9)0.45 (0.41–0.49)0.23 (0.22–0.25)459.5 (459.5–498.5)0.291 (0.262–0.323)3.1 (2.81–3.41)    25–29.916.8 (15.5–18.2)15.7 (14.6–16.8)8.6 (8.1–9.0)0.47 (0.44–0.51)0.27 (0.25–0.28)449.1 (449.1–484.2)0.509 (0.461-0.561)5.1 (4.70–5.63)    30–34.910.5 (9.1–12.1)17.7 (15.5–20.1)10.0 (9.1–10.9)0.51 (0.44–0.58)0.31 (0.28–0.34)467.4 (467.4–535.0)0.957 (0.803–1.14)8.5 (7.24–9.98)    >= 359.9 (7.6–12.7)21.5 (17.0–27.0)13.5 (11.6–15.8)0.56 (0.43–0.72)0.31 (0.26–0.37)439.8 (439.8–559.9)1.37 (1.00–1.87)9.0 (6.77–12.03)    P-trend<0.0001<0.0001<0.00010.052<0.00010.959<0.0001<0.0001OC use ever    yes17.6 (16.2–19.2)15.5 (14.4–16.6)8.4 (8.0–8.9)0.47 (0.43–0.51)0.26 (0.25–0.28)494.7 (459.4–532.7)0.480 (0.431–0.533)4.9 (4.4–5.3)    no18.6 (17.2–20.1)14.9 (14.1–16.0)8.5 (8.1–8.9)0.47 (0.44–0.51)0.26 (0.24–0.27)424.8 (397.1–454.3)0.460 (0.417–0.507)4.5 (4.1–4.9)    P equality of means0.3540.5530.7270.9380.6250.0030.5640.258HRT use ever    yes17.9 (14.5–21.9)18.5 (15.5–22.0)11.9 (10.5–13.4)0.52 (0.4–0.6)0.26 (0.2–0.3)488.8 (408.1–585.5)0.667 (0.515–0.863)4.6 (3.6–5.8)    no18.2 (17.1–19.3)14.9 (14.2–15.7)8.3 (7.9–8.5)0.47 (0.4–0.5)0.26 (0.2–0.3)452.4 (429.4–476.5).455 (.422-.490)4.7 (4.4- 4.9)    P-equality of means0.8750.021 <0.00050.2990.8480.4180.0050.870Age at menopause    <= 4718.4 (16.33–20.6)14.7 (13.3–16.2)8.5 (7.9–9.1)0.51 (0.46–0.57)0.28 (0.26–0.30)471.3 (425.7–521.9)0.465 (0.402–0.539)4.9 (4.3–5.6)    48–5019.8 (17.94–21.8)15.3 (14.1–16.6)8.6 (8.1–9.1)0.49 (0.44–0.53)0.26 (0.24–0.27)485.1 (445.4–528.4)0.433 (0.383–0.490)4.3 (3.8–4.8)    51–5217.5 (15.49–19.8)16.1 (14.5–17.9)8.3 (7.7–8.9)0.47 (0.42–0.52)0.26 (0.24–0.28)460.4 (413.4–512.7)0.476 (0.408–0.556)4.8 (4.2–5.5)    >= 5316.0 (14.15–18.2) 0.15914.6 (13.1–16.2)8.5 (7.8–9.1)0.41 (0.37–0.46)0.24 (0.22–0.26)389.2 (348.9–434.1)0.527 (0.450-0.616)4.9 (4.3–5.7)    P-trend0.7020.7390.0140.0170.0390.3350.950Time since menopause (years)    <618.7 (16.7–20.9)16.0 (14.5–17.6)9.2 (8.6–9.9)0.51 (0.46–0.57)0.27 (0.25–0.29)567.5 (514.6–625.9)0.493 (0.427–0.568)4.7 (4.14–5.35)    10-Jun17.9 (15.9–20.0)14.4 (13.1–15.9)8.6 (8.1–9.3)0.46 (0.42–0.51)0.25 (0.23–0.27)450.6 (408.0–497.7)0.484 (0.419–0.559)4.6 (3.99–5.19)    15-Nov17.9 (15.9–20.1)15.5 (13.9–17.1)8.2 (7.6–8.8)0.45 (0.40–0.50)0.27 (0.24–0.29)432.7 (390.7–479.2)0.457 (0.394–0.530)4.7 (4.13–5.39)    >= 1618.1 (16.2–20.2)14.9 (13.6–16.4)8.1 (7.5–8.6)0.47 (0.42–0.52)0.26 (0.24–0.28)390.1 (354.9–428.9)0.443 (0.386–0.509)4.7 (4.13–5.29)    P-trend0.7250.4890.0030.2330.828<0.00010.2460.960* Among parous participants§ The always irregular category was not included when testing for trend
In table 4 adjusted geometric means for each hormone according to the reproductive characteristics are shown. Participants in the lowest quartile of cumulative number of menstrual cycles tended to have higher SHBG (P trend = 0.013) and lower SHBG/E2 (P trend = 0.050) levels. Androstenedione and DHEAS levels increased with increasing age at menarche (P trend = 0.027 and 0.022 respectively), a similar tendency was observed for E2, although the P for trend did not reach statistical significance (P trend = .058). Women with more children, tended to have higher SBHG levels (P trend = 0.030), number of children was also related to lower SHBG/E2 (p trend = 0.010) levels. No association was found between number of cycles before a FFTP, parity, age at FFTP and any of the hormones. Age at menopause related inversely with androstenedione and testosterone; women with older age at menopause tend to have lower levels of such hormones (p trend = 0.024 and 0.015 respectively).Table 4Hormones adjusted means† (95%CI) for serum sex hormones and SHBG levels according to reproductive characteristics SHBG (μg/ml)Estrone (pg/ml)Estradiol (pg/ml)Androstenedione (ng/ml)Testosterone (ng/ml)DHEAS (ng/ml)SBHG/E2 -INDEXFA-INDEXLifetime number of menstrual cycles    <= 41519.50 (16.86–22.46)15.75 (13.83–17.94)8.62 (7.89–9.43)0.571 (0.495–0.659)0.285 (0.256–0.317)519.57(454.41–594.67)0.444 (0.373–0.528)4.76 (4.06–5.59)    416–45319.79 (17.51–22.35)13.28 (11.88–14.83)8.39 (7.78–9.04)0.448 (0.396–0.506)0.253 (0.231–0.278)436.16 (388.77–488.82)0.424 (0.365–0.492)4.27 (3.72–4.89)    454–49016.99 (14.98–19.26)16.26 (14.51–18.23)8.72 (8.06–9.41)0.434 (0.383–0.493)0.251 (0.228–0.276)427.96 (380.31–481.06)0.513 (0.440–0.597)4.76 (4.14–5.48)    >= 49115.94 (13.82–18.39)16.02 (14.07–18.24)8.53 (7.81–9.32)0.443 (0.384–0.511)0.257 (0.231–0.287)447.64 (391.51–511.83)0.535 (0.450–0.636)5.15 (4.39–6.04)    Always irregular §16.48 (13.87–19.57)15.21 (13.01–17.78)8.04 (7.24–8.94)0.462 (0.389–0.548)0.255 (0.224–0.290)427.09 (363.58–501.69)0.488 (0.397–0.601)4.93 (4.07–5.96)    P-trend0.0130.4740.8420.0890.4460.3490.0500.192Number of menstrual cycles before a FFTP*    <= 11819.83 (17.51–22.47)15.15 (13.49–17.01)8.22 (7.61–8.87)0.474 (0.416–0.540)0.265 (0.241–0.292)468.25 (415.30–527.42)0.414 (0.356–0.482)4.37 (3.81–5.02)    119–14718.41 (16.18–20.95)14.47 (12.83–16.33)8.38 (7.75–9.07)0.480 (0.419–0.550)0.253 (0.229–0.280)475.80 (420.31–538.61)0.455 (0.389–0.533)4.53 (3.92–5.22)    148–18516.09 (13.81–18.76)15.66 (13.57–18.06)8.85 (8.05–9.72)0.420 (0.358–0.493)0.258 (0.229–0.290)402.22 (347.23–465.91)0.549 (0.456–0.662)5.17 (4.36–6.13)    >= 18618.43 (16.13–21.05)14.86 (13.13–16.83)8.54 (7.88–9.27)0.461 (0.401–0.529)0.253 (0.228–0.280)445.86 (392.29–506.74)0.463 (0.394–0.545)4.54 (3.92–5.27)    Always irregular §15.52 (12.92–18.65)14.81 (12.49–17.57)8.04 (7.19–8.99)0.467 (0.386–0.566)0.267 (0.232–0.307)438.34 (367.97–522.69)0.518 (0.415–0.648)5.39 (4.397–6.60)    P-trend0.2820.9290.4160.6050.5730.4230.1960.522Age at menarche    <= 1119.26 (15.72–23.67)14.67 (12.23–17.58)7.56 (6.67–8.55)0.440 (0.360–0.538)0.272 (0.234–0.316)424.54 (352.13–511.83)0.392 (0.307–0.502)4.71 (3.75–5.90)    1218.10 (16.04–20.43)13.98 (12.55–15.58)8.53 (7.93–9.19)0.454 (0.403–0.512)0.249 (0.228–0.272)423.27 (378.42–472.95)0.471 (0.407–0.546)4.51 (3.94–5.16)    1316.35 (14.58–18.32)15.52 (14.01–17.18)8.17 (7.63–8.77)0.458 (0.409–0.513)0.254 (0.233–0.276)444.52 (400.21–493.74)0.500 (0.436–0.574)4.87 (4.29–5.53)    1417.79 (15.78–20.06)15.41 (13.85–17.15)8.58 (7.98–9.23)0.440 (0.391–0.496)0.248 (0.227–0.271)448.99 (402.22–501.69)0.482 (0.417–0.558)4.58 (4.01–5.23)    1519.73 (16.91–23.01)17.06 (14.86–19.57)9.02 (8.21–9.90)0.518 (0.444–0.603)0.286 (0.255–0.320)516.98 (448.54–595.26)0.457 (0.379–0.550)4.78 (4.03–5.67)    >= 1618.58 (15.99–21.58)15.06 (13.16–17.22)8.78 (8.02–9.63)0.553 (0.476–0.641)0.276 (0.247–0.308)494.23 (430.09–567.36)0.473 (0.395–0.567)4.79 (4.05–5.66)    P-trend0.5770.1810.0580.0270.1940.0220.6210.739Ever pregnant    yes18.17 (17.13–19.28)14.94 (14.17–15.75)8.41 (8.12–8.72)0.463 (0.437–0.491)0.258 (0.247–0.269)452.59 (428.80–478.18)0.463 (431–497)4.62 (4.33–4.94)    no16.56 (14.19–19.34)17.12 (4.91–19.65)8.88 (8.08–9.76)0.528 (0.453–0.616)0.273 (0.244–0.306)466.85 (405.05–538.08)0.536 (0.445–0.646)5.21 (4.39–6.18)    P-equality of means0.2730.0720.2990.1190.8020.6930.1520.204Age at FFTP*    <= 2119.89 (16.98–23.29)15.53 (13.45–17.94)8.26 (7.50–9.08)0.454 (0.386–0.534)0.248 (0.220–0.280)451.24 (388.77–523.74)4.05 (3.39–4.82)0.415 (0.343–0.503)    22–2417.46 (15.52–19.63)14.41 (12.96–16.04)8.46 (7.88–9.07)0.466 (0.413–0.526)0.264 (0.242–0.289)464.05 (415.71–518.53)4.84 (4.26–5.52)0.484 (0.420–0.558)    25–2717.32 (15.67–19.16)15.01 (13.69–16.44)8.06 (7.58–8.57)0.463 (0.417–0.513)0.257 (0.238–0.277)445.41 (405.05–489.80)4.84 (4.33–5.41)0.465 (0.412–0.526)    >= 2818.67 (16.63–20.99)15.07 (13.56–16.76)8.84 (8.24–9.48)0.466 (0.414–0.525)0.258 (0.237–0.282)452.59 (405.45–504.72)4.59 (4.04–5.22)0.473 (0.411–0.545)    P-trend0.7410.9880.3890.8580.8230.8500.4830.444Number of liveborn    016.56 (14.18–19.32)17.12 (14.91–19.67)8.87 (8.08–9.76)0.527 (0.452–0.615)0.273 (0.244–0.306)466.85 (404.64–538.08)0.536 (0.445–0.646)5.21 (4.39–6.18)    116.14 (13.05–19.94)16.39 (13.56–19.81)8.33 (7.32–9.49)0.411 (0.333–0.508)0.237 (0.203–0.277)438.78 (361.04–533.79)0.517 (0.400–0.667)4.69 (3.71–5.95)    217.57 (15.99–19.32)14.88 (13.68–16.20)8.71 (8.22–9.23)0.471 (0.429–0.517)0.257 (0.240–0.276)456.69 (418.63–498.19)0.496 (0.443–0.555)4.85 (4.37–5.39)    318.05 (16.13–20.19)14.73 (13.33–16.29)8.28 (7.73–8.86)0.448 (0.401–0.502)0.259 (0.239–0.282)453.05 (408.71–502.70)0.459 (0.401–0.525)4.62 (4.07–5.23)    >= 420.18 (17.85–22.83)14.83 (13.28–16.56)8.12 (7.53–8.76)0.486 (0.430–0.550)0.263 (0.240–0.288)449.89 (401.82–504.21)0.402 (0.346–0.467)4.23 (3.69- 4.85)    P-trend0.0300.0800.1170.5780.9330.7800.0100.064Age at FFTP*    <= 2119.89 (16.98–23.29)15.53 (13.45–17.94)8.26 (7.50–9.08)0.454 (0.386–0.534)0.248 (0.220–0.280)451.24 (388.77–523.74)0.405 (0.339–0.482)4.15 (3.43–5.03)    22–2417.46 (15.52–19.63)14.41 (12.96–16.04)8.46 (7.88–9.07)0.466 (0.413–0.526)0.264 (0.242–0.289)464.05 (415.71–518.53)0.484 (0.426–0.552)4.84 (4.20–5.58)    25–2717.32 (15.67–19.16)15.01 (13.69–16.44)8.06 (7.58–8.57)463 (0.417–0.513)0.257 (0.238–0.277)445.41 (405.05–489.80)0.484 (0.433–0.541)4.65 (4.12–5.26)    >= 2818.67 (16.63–20.99)15.07 (13.56–16.76)8.84 (8.24–9.48)0.466 (0.414–0.525)0.258 (0.237–0.282)452.59 (405.45–504.72)0.459 (0.404–0.522)4.73 (4.11–5.45)    P-trend0.7410.9880.3890.8580.8230.8500.4830.444Age at menopause∞    <= 4718.16 (16.20- 20.37)14.74 (13.30–16.33)8.38 (7.81–8.98)0.511 (0.456–0.572)0.281 (0.258–0.306)445.41 (401.01–494.72)0.461(0.402–0.530)4.94 (4.35–5.61)    48–5019.26 (17.53–21.16)15.41 (14.17–16.76)8.61 (8.12–9.15)0.484 (0.441–0.532)0.259 (0.241–0.277)481.06 (441.42–524.79)0.447 (0.399–0.501)4.38 (3.95–4.86)    51–5216.76 (14.86–8.89)16.23 (14.57–18.06)8.36 (7.77–8.99)0.472 (0.419–0.532)0.261 (0.238–0.285)462.20 (413.64–516.46)0.499 (0.431–0.577)5.01 (4.38–5.72)    >= 5316.96 (15.01–19.18)14.47 (12.96–16.13)8.50 (7.88–9.15)0.411 (0.364–0.464)0.238 (0.217–0.261)416.55 (372.41–466.38)0.501 (0.432–0.580)4.65 (4.06–5.33)    P-trend0.3360.7700.8540.0240.0150.6450.3740.690† Adjusted for age at intake, years since menopause, body mass index (BMI) at examination, use of hormone replacement therapy (HRT) ever (Y/N), use of oral contraceptive (OC) ever (Y/N), smoking habits, total alcohol consumption (g/day as a continuous variable) and physical activity (Voorrips total score as a continuous variable). For analysis of the “lifetime cumulative number of menstrual cycles” and “number of cycles before a FFTP” variables, we also adjusted for more than one year of irregular cycles (Y/N) § The always irregular category was not included when testing for trend * Among parous participants ∞ Number of years since menopause was not included in the model
Discussion
The results of the present study suggest that the “lifetime cumulative number of cycles” is related to SHBG and that some reproductive characteristics are also associated to postmenopausal serum sex hormone levels. We noted significant associations after adjusting for important confounders, the “lifetime cumulative number of menstrual cycles” related inversely with levels of SHBG and positively with SHBG/E2. Age at menarche directly relates to both DHEAS and androstenedione. Number of children had a significant relation with levels of SHBG and SHBG/E2 and age at menopause was indirectly associated to androstenedione and testosterone.
Several studies have investigated the relation between reproductive risk factors and endogenous sex hormone levels in postmenopausal women, but with conflicting results [6, 13, 15, 24–27]. The patterns of risk associated with reproductive history suggest that prolonged exposure to ovarian hormones increases breast cancer occurrence [8, 28, 29]. The cumulative number of menstrual cycles has been used as a proxy for ovarian activity, women in the highest quartile of the “lifetime cumulative number of menstrual cycles” estimate, have an increased breast cancer risk when compared with women in the lowest quartile [17, 18]. A significant inverse relation between number of menstrual cycles and SHBG was observed in our study. It has been previously described that SHBG decrease the proportion of E2 that is able to leave the circulation and enter the cells, and an inverse relation between concentration of SHBG and breast cancer has been observed in epidemiological studies [1, 10]. Our findings suggest therefore, that postmenopausal free levels of estrogens or androgens may be related to the “lifetime cumulative number of menstrual cycles” reflecting lifetime exposure to ovarian hormones.
Early age at menarche and late age at menopause have long being recognized as risk factors for breast cancer. A 1-year delay in the onset of menarche is associated with a 5% reduction in risk for developing breast cancer in later life [30]. We found that age at menarche has a direct relation with serum levels of DHEAS and androstenedione measured after the menopause. Androgens have been associated with breast cancer risk, women with breast cancer tend to have higher levels of DHEAS and androstenedione [2, 4, 31, 32]. It has been proposed that in postmenopausal women DHEAS might act as a moderate estrogen agonist, through competitive binding of its metabolite 5-androstene-3B,17B-diol [33]. It is also well known that the peripheral conversion of adrenal androstenedione is the principal source of E1 in postmenopausal women [34], therefore we would expect an inverse association between both DHEAS and androstenedione and age at menarche. Madigan et al. showed, after adjusting for BMI and years since menopause, that young age at menarche, was associated with higher levels of androstenedione, but also of E1 and E2 [15]. Our results are in conflict with such findings, however several studies have found no association between age at menarche and SHBG, estrogens or androgens [14, 16, 35, 36].
Childbearing and a young age at FFTP protect against breast cancer and additional pregnancies further reduce the risk [7, 10, 37, 38]. We found that parous women tend to have lower levels of E1, however this difference was not statistically significant (P = .072). Lamar et al. [14] found that nulliparous women had higher testosterone levels, a finding that could contribute to the increased risk of breast cancer in nulliparous women. Our findings also showed that the number of live births is directly related to SHBG, and indirectly to SHBG/E2 and FAI (P for trend for FAI did not reach statistical significance, P = 0.064). This observation is in conflict with the known association between low SBGH levels and breast cancer risk, and the protective effect that the number of pregnancies has on breast cancer [28, 29, 38, 39]. It is important to mention that a woman’s genetic and environmental profile may influence both, her ability to have children and her postmenopausal sex hormone concentrations, making these observations difficult to interpret. It has been hypothesized that the overall effect of parity is due to a protective effect of a young age at FFTP [40], another proposed mechanism is through lactation, as breastfeeding induces final differentiation of the terminal duct epithelium making it relatively insensitive to hormonal stimulation [41, 42]. Despite these observations we found no relation between age at FFTP and any of the hormones. No relation between parity, number of pregnancies or age at FFTP and serum hormone levels was reported by several studies. [12, 14–16, 43, 44].
We observed an indirect association between age at menopause and both, androstenedione and testosterone. Several studies have failed in finding an association between age at menopause and sex hormones [12, 14, 16], however a direct association with menopause and androstenedione has been reported [15], as well as a relation between age at menopause and E2 in a group of 173 postmenopausal overweight women [13].
To appreciate the findings of our study, some strengths and limitations need to be mentioned. The main advantage is our large sample size, to the best of our knowledge this is the largest study assessing the relationship between reproductive risk factors and serum sex hormone levels in naturally menopausal women and the first one evaluating the relationship between sex hormones and an index that can be used as a proxy of ovarian exposure. Another advantage is that we attempted to adjust for all relevant covariates, in order to obtain fully adjusted estimates. The participants in this cross-sectional study originated from a random sample of a large cohort, and blood samples where drawn from 97.5% of the 17,357 participants, favoring the generalization of our results. Given the prospective nature of the Prospect-EPIC study, and because participants were recruited through an existing population-based breast cancer screening program, we included only disease-free participants at baseline blood collection.
A limitation of this study is that only a single blood sample was assayed per participant, however it has been shown that a single measure provides substantial information in postmenopausal women for E1, E2 and SHBG [45]. Serial measurements of serum hormones in postmenopausal women suggest that a single measurement can reliably categorize women, at least over the short term. Intraclass correlation coefficients of hormone measurements in serum collected over a 3-year period were reported to be between 0.68 and 0.78 for the estrogens, 0.66 and 0.88 for the androgens, and 0.92 for SHBG [46]. We did not have information regarding premenopausal sex hormone concentrations in the participants of this study; it remains unknown whether some underlying hormonal profile could have influenced both, reproductive capabilities and postmenopausal sex hormone concentrations. It is also possible that premenopausal hormone levels are more related to reproductive factors than postmenopausal levels, and that is as a proxy for premenopausal levels that postmenopausal levels relate to reproductive characteristics. We should consider that the data used regarding reproductive characteristics are prone to recall errors. However, it is unlikely that recall errors are related to endogenous hormone levels themselves, if any misclassification occurred, it would have been non-differential.
In summary, in this group of naturally postmenopausal women, the “lifetime cumulative number menstrual cycles” was related only to SHBG, suggesting that free levels of estrogens or androgens may be related to this index, reflecting lifetime exposure to ovarian hormones. Our findings show that some reproductive characteristics are related to postmenopausal serum sex hormone levels, however we do not find clear evidence to explain the mechanism by which reproductive risk factors influence hormone-related cancers in naturally postmenopausal women. It seems that reproductive and menstrual characteristics do not affect postmenopausal breast cancer risk via a long-term effect on sex hormone levels. The association of reproductive risk factors and breast cancer could be a result of prolonged exposure to ovarian hormones, and not only the product of a direct, long-lasting effect of sex hormone serum concentrations. Further research is needed in order to unravel the biological mechanisms through which reproductive characteristics relate to breast cancer risk.

cumulative number of menstrual cycles
sex hormones
breast cancer
risk factors
reproductive characteristics
steroids