Rise in seroprevalence of herpes simplex virus type 1 among highly sexual active homosexual men and an increasing association between herpes simplex virus type 2 and HIV over time (1984–2003)

Objectives Herpes simplex virus type 1 and type 2 (HSV-1 and HSV-2) are both highly prevalent. The rate of genital HSV-1 transmission is reportedly increasing over time. HSV-2 is considered to be an important risk factor for HIV transmission. We therefore studied changes in the HSV-1 and HSV-2 prevalence in a large cohort of men who have sex with men (MSM) over a 20-year time period. Methods Among 1847 HIV-infected and HIV-uninfected MSM participating in the Amsterdam Cohort Studies, seroprevalence of HSV-1 and HSV-2 was determined and prevalence rate ratios (PRR) and 95% confidence intervals were calculated. Results Between 1984 and 2003 the HSV-1 and HSV-2 prevalence decreased among HIV-uninfected MSM (P < 0.001), but remained stable among HIV-infected MSM. HSV-1 prevalence increased among men with at least 200 sexual partners over lifetime (PRR: 1.49, P < 0.001). The association between HIV infection and HSV-2 became stronger over time (PRR: 3.45, P < 0.001). Conclusions Seroprevalence of HSV-1 and HSV-2 remained high among HIV infected MSM from 1984 to 2003. The association of HIV and HSV-2 increased during the HIV epidemic. Since the proportion of sexual transmission of HSV-1 is rising, it is important to study the potential role of HSV-1 as risk factor for HIV acquisition.
Introduction
Herpes simplex virus type 1 (HSV-1) is widespread in the general population, while herpes simplex virus type 2 (HSV-2) is more restricted to risk groups such as men who have sex with men (MSM). HSV-1 prevalence is around 70% in the general population [1, 2]. Transmission usually occurs during childhood through oral contact and normally causes oropharyngeal infection. Childhood HSV-1 transmission has declined in industrialised countries, resulting in a lower prevalence of HSV-1 and leaving a larger population of adolescents at risk for sexual transmission of HSV-1. Earlier studies have reported sexually related risk factors for HSV-1 infection in women [3]. Among those persons attending the STD clinic and blood donors, HSV-1 infection is associated with younger age of first intercourse [4]. However, less is known about sexually related risk factors for HSV-1 infection among MSM [5]. HSV-2 infection is usually transmitted sexually and is considered as a marker for sexual risk behaviour in populations [6]. In HIV-infected MSM, the prevalence of HSV-2 is as high as 61% [1], while being 15–25% in the general population [7, 8]. HSV-2 is a risk factor for HIV acquisition, especially in the African setting [9] and in MSM [10]. HSV-2 infected persons are more susceptible for HIV [11, 12]. Moreover, HIV-infected persons are more likely to have subclinical reactivation of HSV-2 and are therefore more likely to transmit the virus [13].
We previously demonstrated a decline in the prevalence of HSV-2 among MSM in Amsterdam between 1984–1997, which could be explained by a decrease in sexual risk behaviour [5]. However, in the second half of the 1990s their sexual risk behaviour increased after effective HIV therapy became generally available. This may have caused an increase in the prevalence of HSV-2 and possibly also in HSV-1 since 1996.
We here studied the trend in HSV-1 and HSV-2 prevalence among homosexual men over a 20-year time period (1984–2003) and whether risk factors for infection changed during this period.
Methods
Study population
In 1984 an open and prospective cohort study on HIV seroconversion and AIDS among sexually active HIV-negative and positive homosexual men was started. The Amsterdam Cohort Study (ACS) is still ongoing, although entry criteria with respect to HIV status and age have changed over time. From 1984 until May 1985, both HIV-positive and HIV-negative men were included. From May 1985 until February 1988, only HIV-negative men were allowed in the study. From February 1988 through 1994, HIV-positive and HIV-negative men could enter the study, but since 1995, they must be ≤30 year of age.
At an ACS visit, a standardised questionnaire is administered regarding demographics, sexual behaviour, and medical history for sexually transmitted infections (STI). Blood samples are collected for immunologic and virologic testing and for storage. For this study, stored sera (collected at the first cohort visit) taken from ACS participants with at least two cohort visits (1847/2100 (88%)) were tested for HSV-1 and HSV-2.
Laboratory methods
Sensitive and specific FDA approved serological assay for HSV-1&2 was used (HerpeSelect by FOCUS technologies, USA). Its manufacturer recommends an index value > 1.1 as positive. However, there is evidence that using this cut-off value in HSV-2 studies yields a high rate of false positive results in populations with multiple infections, such as those in Africa [14]. Raising the positive cut-off will increase the specificity [14]. Since the optimal cut-off for our target population has not been established, 100 samples with results in the range of 0.9 and 3.5 were re-tested with a highly specific Western blot. HSV-2 ELISA and Western blot results were concordant for 80/100 samples. The proportion of samples that were positive with both ELISA and Western blot increased with increasing index value (Fig. 1). Based on these results, we consider a cut-off value of ≥2.1 as being positive and an index value <2.1 is classified as negative. The HSV-2 index value <2.1 had 36% concordance with Western blot results, while the HSV-2 index value of ≥2.1 showed 93% concordance. There were no differences in Western blot outcomes between HIV-infected and HIV uninfected MSM.
Fig. 1Comparison of HSV-2 serology by ELISA and Western blot. 100 samples were tested with HSV-2 ELISA and re-tested with the Western blot to identify discrepancies between the two assays
Blood samples were tested for HSV-1 and HSV-2 at the Public Health Laboratory of the Health Service of Amsterdam. The Western blot was conducted at the Institute for Pathology and Medical Research (ICPMR) in Sydney, Australia. Blood samples are also tested for HIV antibodies by enzyme linked immunosorbent assay (ELISA) (Abbot Laboratories, North Chicago, Illinois, USA; Vironostika, Organon, Teknika, Boxtel, the Netherlands), and when positive, are confirmed by Western blot.
Variables and statistical analyses
The statistical analyses were based on the data collected at entry of the cohort. Variables used in this study were calendar year of ACS entry, HIV-status, age, nationality, education, age of first homosexual contact, lifetime sexual partners, and self-reported history of syphilis and gonorrhoea in the past 5 years.
Variables concerning sexual practices included orogenital, anogenital and oroanal contact in the prior 6 months. Some changes in the questions were made in the questionnaires between 1984 and 2003 regarding the sexual practices. Oroanal contact was not asked for from 1889 to 1994, resulting in approximately 25% missing values on this variable. From 1995 onward, the most important difference was that orogenital contact with ejaculation was asked, while in the previous years orogenital contact in general was asked. The percentage of participants not having orogenital contact was somewhat higher for the years 1995 and 1996.
The prevalence of HSV-1 and 2 at the ACS entry was determined and risk factors for HSV-1 and 2 were assessed by calculating prevalence rate ratios (PRR), with their 95% confidence interval (CI). Odds ratios could not be interpret as relative risks, since the rare event assumption was not reached. Therefore PRR’s were directly estimated, using a modified Poisson regression approach [15]. This approach provided a correctly estimated standard error for the estimated relative risk. Since inclusion criteria with respect to age and HIV-status changed over time, all risk factor analyses were adjusted for age and HIV-status. Variables that were statistically significant were included in the multivariate model, using a stepwise forward approach forcing age and HIV-status in the model.
We tested whether risk factors changed over time by testing for interaction between variables under investigation and calendar time in the multivariate model. Calendar time therefore was categorised as 1984–1986, 1987–1991, 1992–1996, 1997–2003.
Confounding was defined to be present when the included variable caused a change of the prevalence ratio by more than 10%. Interaction was defined to be present when the addition of an interaction term improved the original model and the P-value was <10%. Statistical significance was defined as a P-value <0.05. To reduce residual confounding when measuring the association between HSV and sexual practices, three variables measuring sexual practices over the prior 6 months were included in the model at the same time, together with the lifetime sexual partners. We modelled time trends in the HSV-1 and 2 prevalence with calendar time as a continuous variable using restricted cubic splines with four knots, resulting in a smoothly varying curve.
For 77 MSM the HSV-1 index value was missing and 91 MSM had a missing HSV-2 index value. Participants with a missing index value for both HSV-infections were not included in the analyses. MSM with a missing index value for one HSV-infection, but with a known HSV status of the other HSV-infection were included in the analyses for the known HSV serostatus.
Finally, sensitivity analyses for HSV-2 were conducted by using the cut-off value of 1.1, as recommended by the manufacturer and by using the cut-off value of 3.5, excluding those with an index value in the grey area (between 0.9–1.1 and 0.9–3.5). However, time trends in prevalence and risk factors found were comparable to when 2.1 was the cut-off value (data not shown).
Results
General characteristics
Between 1984 and 2003, a total of 1847 MSM had at least two visits. General characteristics of the total study group are presented in Table 1. Of the 1847, 1207(65%) MSM were HSV-1 antibody positive, while 759/1847(41%) of the men were HSV-2 antibody positive. Of the total group, 558(30%) were positive for both. Participants were predominantly of Dutch nationality (86%) and had a median age of 29 years (interquartile range: 25–36).
Table 1Demographic and sexual characteristics of 1847 homosexual men, for the total study group, and for HSV-1-infected participants and HSV-2-infected participants separately, between 1984 and 2003, with the prevalence ratios for HSV-1 and HSV-2 with their 95% confidence intervalsaCharacteristicsTotalHSV-1-infectionPRR (95% CI)Overall P-valueHSV-2-infectionPRR (95% CI)Overall P-valueTotal18471207 (65)759 (41)Year of study entry<0.0001<0.00011984–1986943675 (72)1461 (49)11987–1991165113 (68)0.87 (0.78–0.97)92 (56)0.84 (0.72–0.98)1992–1996222138 (43)0.88 (0.79–0.96)80 (36)0.66 (0.56–0.77)>1997517281 (54)0.76 (0.70–0.84)126 (24)0.47 (0.40–0.55)Index value<0.9–5358530.9–1.1–29321.1–2.1–100112≥2.1–1107759Missing7791Age<0.0001<0.0001<30 years100257012521≥30 years8456371.20 (1.13–1.29)5071.98 (1.75–2.23)Nationality:0.0060.62Dutch1586 947 (60)1615 (39)1Northern/central Europe116 72 (62)1.04 (0.92–1.18)54 (47)1.08 (0.90–1.38)Non-European145 117 (81)1.17(1.07–1.28)75 (52)1.04 (0.91–1.23)Education0.0050.007Low9677 (80)157 (59)1Middle669408 (61)0.85 (0.76–0.95)239 (36)0.72 (0.59–0.87)High971574 (59)0.81 (0.73–0.81)387 (40)0.79 (0.66–0.94)Missing11577 (67)54 (47)Sexual partners in lifetime1–20860497 (58)1<0.0001277 (32)10.5121–200531360 (68)1.12 (1.04–1.22)212 (40)2.30 (0.93–1.21)>200443339 (77)1.24 (1.15–1.34)262 (59)1.07 (0.95–1.20)Age of first homosexual contact (median, IQR)18 (15–20)17 (15–20)1.01(1.01–1.02)^<0.000117 (15–20)0.99 (0.99–1.01)c0.32HSV co-infection5685681.29 (1.14–1.47)<0.00015681.167 (1.08–1.24)<0.0001HIV infection (%)513 367 (72)1.11 (1.03–1.18)0.007312 (61)1.12 (1.00–1.24)0.05History of gonorrhoea in the past 5 years1053 666 (63)0.88 (0.83–0.93)0.0005424 (40)0.88 (0.79–0.97)0.02History of syphilis in the past 5 years278 219 (79)1.15 (1.07–1.24)0.001197 (71)1.51 (1.36–1.69)<0.0001Orogenital contact in the past 6 monthsb1363892 (65)0.99 (0.82–1.22)0.46565 (41)0.69 (0.55–0.87)0.01Anogenital contact in the past 6 monthsb1222819 (67)1.11 (1.14–1.60)0.002551 (45)1.36 (1.16–1.60)0.0002Oroanal in the past 6 monthsb1081700 (65)1.00 (0.91–1.09)0.99438 (41)0.98 (0.85–1.12)0.80aSince ACS inclusion criteria have changed over times all analyses were adjusted for age (per 10-year increase) and HIV status at cohort entrybAnalyses are also adjusted for the sexual techniques and number of partners to exclude residual confoundingcPer 10 year of increase
Prevalence of HSV-1 and HSV-2 over time
There was an overall decline in the prevalence of both HSV-1 and 2 between 1984 and 2003 (Tables 1, 2). To investigate time trends in HSV seroprevalence we included an interaction term between time and HIV-status (Fig. 2a, b). Among HIV-negative MSM, the HSV-1 prevalence decreased significantly over time, P < 0.001 (Fig. 2a). Among HIV-positive MSM, the HSV-1 prevalence remained stable over time, P = 0.35(Fig. 2a).
Table 2Multivariate model of risk factors associated with HSV-1 infectionHSV-1Overall P-value(a) HSV-1 infectionYear of study entry<0.00011984–198611987–19910.91 (0.70–1.18)1992–19960.83 (0.66–1.02)>19970.75 (0.63–0.90)Age1.13(1.07–1.18)<0.0001HIV serostatusNegative10.01Positive1.10 (1.02–1.18) NationalityDutch10.0006Northern or Central European1.05 (0.92–1.20)Non European1.62 (1.12–1.36) Education 0.25Low1Middle 0.90 (0.79–1.00)High0.84 (0.76–1.06)Sexual partners in lifetime1–2010.00321–2001.13 (1.05–1.25)>2001.13 (1.04–1.23)History of Gonorrhoea in the past 5 years0.97 (0.90–1.03) 0.11History of Syphilis in the past 5 yearsOrogenital contact in the past 6 months1.12 (0.88–1.43)0.42Anogenital contact in the past 6 months1.08 (0.97–1.20) 0.20Oroanal contact in the past 6 months1.02 (0.70–1.13)0.81HSV-2Overall P-value(b) HSV-2 infectionYear of study entry<0.00011984–198611987–19910.86 (0.70–1.06)1992–19960.58 (0.48–0.71)>19970.47 (0.39–0.56)HIV serostatusNegative1<0.0001Positive1.50 (1.37–1.68)HSV coinfection1.15 (1.02–1.30)0.02History of Syphilis in the past 5 years1.21 (1.08–1.36)0.001Orogenital contact in the past 6 months0.69 (0.56–0.84)<0.0001Anogenital contact in the past 6 months1.20(1.08–1.42)0.02Oroanal contact in the past 6 months1.00(0.87–1.15)0.72Fig. 2(a) HSV-1 prevalence in the Amsterdam Cohort Study among MSM, according to the HIV status (1 = HIV positive, 0 = HIV negative) and the 95% confidence interval. (b) HSV-2 prevalence in the Amsterdam Cohort Study among MSM, according to the HIV status (1 = HIV positive, 0 = HIV negative) and the 95% confidence interval
The HSV-2 prevalence significantly decreased among HIV-negative men, P < 0.001(Fig. 2b). Results from the regression models showed that, after adjustment for changes in age, nationality, education and changes in sexual risk behaviour, the decline in HSV-2 prevalence among HIV negative MSM remained significant (PRR adjusted 0.92, P < 0.001). In contrast, the HSV-2 prevalence remained stable over time for men infected with HIV (P = 0.12). Again, this result was observed after controlling for age, demographic characteristics and sexual behaviour.
Risk factors
Risk factors for HSV-1 and HSV-2 infection, adjusted for age and HIV-status, are presented in Table 1.
In the final model calendar year, HIV-status, nationality, and number of lifetime sexual partners remained independent predictors for HSV-1 infection (Table 2). For HSV-2 infection, earlier year of study entry, positive HIV-status, HSV-1 co-infection, a history of syphilis and sexual behaviour remained independent predictors (Table 2).
Changing risk factors over time
Different interaction terms were included in the model. It appeared that the effect of calendar year differed between HIV-infected and HIV-uninfected MSM for both HSV-1 and HSV-2.
As shown in Fig. 2a, the association between HIV and HSV-1 became stronger over time. This was due to the decline in HSV-1 prevalence over time among HIV-negative MSM but not among HIV-positive MSM (Fig. 2a).
For HSV-2 the association with HIV infection increased with calendar year and was highest after 1996 (Fig. 2b).
For HSV-1, also the effect of calendar time differed with respect to nationality, number of lifetime sexual partners, and with HSV-2 co-infection. As shown by the regression model, a decrease in HSV-1 infection over time was observed only among MSM with Dutch or Northern/Central-European nationality. The association between HSV-1 and having non-European origin became stronger over time. The adjusted PRR was 1.08 (P = 0.3) before 1986 and the adjusted PRR became 1.58 (P < 0.0001) for the time period after 1996.
Also the association between HSV-1 and a higher number of lifetime sexual partners became stronger after 1996. Figure 3a shows the prevalence of HSV-1 infection over time according to the number of sexual lifetime partners. A decrease in the HSV-1 prevalence was seen among MSM with fewer than 21 partners (P < 0.0001), while among MSM with more than 200 partners, the HSV-1 prevalence increased between 1988 and 2003 (P = 0.01).
Fig. 3(a) Prevalence of HSV-1 and the 95% confidence interval over time among HIV negative MSM, according to number of lifetime sexual partners; 1 = 1–20, 2 = 21–200, 3>200 partners. (b) Prevalence of HSV-2 and the 95% confidence interval over time among HIV negative MSM, according to number of lifetime sexual partners; 1 = 1–20, 2 = 21–200, 3 > 200 partners
For HSV-2, the effect of calendar time differed with respect to the number of lifetime sexual partners and with HSV-1 co-infection. A large number of lifetime partners also was strongly associated with HSV-2. However, a decrease in the HSV-2 prevalence was seen among all categories of lifetime sexual partners (Fig. 3b), but this decrease was stronger for MSM with fewer than 21 partners and for those MSM with 21–200 partners.
Discussion
In the present study, we demonstrated an overall decrease in HSV-1 and HSV-2 prevalence among HIV-negative MSM, but not among HIV-positive MSM. In the 1984–2003 period, the association between HSV-2 and HIV among MSM became stronger over time, and HSV-1 prevalence increased in highly sexually active HIV-negative MSM. To our knowledge, this is the first study based on almost 20 years of HSV-1 and HSV-2 prevalence data among MSM.
The decrease seen in the seroprevalence of HSV-1 and HSV-2 over time, could not be explained by changes in demographic characteristics or sexual behaviour. The decrease in HSV-1 is likely to reflect a decrease in childhood transmission by the oropharyngeal contact of HSV-1. Since fewer individuals are infected in childhood with HSV-1, there is a growing population of persons at risk at the time they become sexually active, resulting in a larger proportion of sexual transmission of HSV-1. Two risk factors for HSV-1 infection that could be important in its sexual transmission were identified by this study. First, HIV infection is associated with HSV-1 seropositivity. Second, the prevalence of HSV-1 was higher among highly sexual active MSM (at least 200 lifetime sexual partners). An association also shown earlier by others [3].
HIV infection in this respect may reflect an epidemiological marker for sexual risk behaviour for HSV-1 transmission. HSV-1 prevalence did not decrease in those infected with HIV, and we consider that genital HSV-1 infection has a growing role in the acquisition of HIV.
Likewise, HSV-2 prevalence did not decline over time among those infected with HIV, whereas a decline was noted among HIV uninfected MSM.
Although HSV-2 is sexually transmitted, we did not find an association between a higher number of lifetime partners and HSV-2 infection. HSV-2 was also highly prevalent among MSM with 1–20 lifetime partners. Mainly between 1984 and 1995, there were no major differences in the proportion of MSM infected with HSV-2. This suggests that HSV-2 is highly sexual transmissible and when having a low number of life time partner the risk of receiving a HSV-2 infection is still very high.
The results of this study show a protective effect of orogenital contact for HSV-2 infection, which might be explained by the fact that anogenital contact is a stronger predictor for HSV-2 infection. All variables measuring sexual practices are included in the analyses at the same time. Since most MSM practised all the techniques during the same time period these practices could not be analysed as independent risk factors. The stronger effect of anogenital contact might have overruled the effect of orogenital contact, resulting in a protective effect of orogenital contact.
The overall prevalence of HSV-2 in this study is similar to that among MSM in San Francisco in 1989, but higher than the prevalence found in more recent studies in the US [16–18]. The lower prevalence in those more recent studies probably reflects the decline in HSV-2 prevalence over time, as found in our study.
Russell et al., found high prevalence rates of HSV-2 among HIV-infected MSM in Australia [1]. The HSV-2 seroprevalence was more than twice as high as among HIV-uninfected MSM; there was no significant difference in HSV-1 prevalence between HIV-infected and HIV-uninfected MSM.
Several epidemiological studies have described an association between HIV and HSV-2 [10]. HSV-2 is recognised as a risk factor for HIV acquisition in MSM. In addition, HSV-2 may up-regulate HIV and increase local HIV replication on mucosal surfaces, leading to an increased risk of HIV transmission. Our study is the first to show an increase in the association between HSV-2 and HIV since 1996, suggesting that HSV-2 may play a growing role in driving the HIV epidemic in MSM. If this is the case, prevention of HSV-2 may well contribute to the prevention of HIV among highly sexually active MSM. Although serological screening for HSV-2 among MSM is still under debate, the increasing association between HSV-2 seropositivity and HIV is an argument in its favour. Several reasons against screening have been raised, such as the lack of a reliable serological test. We are aware of the low specificity of the various HSV-2 serological assays. However, these serological assays might be useful as a screening tool, when used with an increased cut-off value less individuals will be classified as false positive. A second argument against serological screening is that HSV-2 infection is largely asymptomatic and condom use appears only partially protective against HSV transmission. These factors complicate the prevention of HSV-2 and genital HSV-1 infection. However, it has been shown that half of the patients, initially unaware of their HSV-2 infection are able to recognise symptoms after being educated to do so [19]. Also, knowledge of the HSV-2 status of a sexual partner has been associated with a reduced risk of HSV-2 transmission [20]. Antiviral drugs used as suppressive therapy will lower the frequency of recurrences by 70–80% [21, 22]. A combined approach of offering serological screening to highly sexual active MSM together with encouraging condom use to reduce the risk of HSV transmission and using suppressive therapy among those with recurrent lesions might eventually play an effective part in controlling the HIV epidemic among MSM.
One limitation of our study is its cross-sectional design. As a consequence, we cannot reveal the relation between HIV and HSV infection, being unable to determine which occurred first. As both HIV and HSV are sexually transmitted diseases, their association may well reflect shared sexual behavioural practices leading to transmission as well as a biological relation. Longitudinal studies, in which incident HIV and HSV cases are captured are therefore needed to give more insight into the relationship between HIV and HSV as affected by changes in sexual risk behaviour.
The results of this study have two implications for HIV and HSV research among highly sexually active MSM. First, it appears that HSV-2 and HIV are now more strongly related than in the early days of the HIV epidemic. As a vaccine against HSV-2 for MSM is not yet available, a determination of the extent to which the prevention of HSV-2, specially aimed for MSM at high risk for HIV, can contribute to controlling the HIV epidemic is needed. Second, since the extent of sexual transmission of HSV-1 is rising, we need to clarify its potential role as a risk factor for HIV acquisition in longitudinal studies.


Dynamics of amino acid metabolism of primary human liver cells in 3D bioreactors

The kinetics of 18 amino acids, ammonia (NH3) and urea (UREA) in 18 liver cell bioreactor runs were analyzed and simulated by a two-compartment model consisting of a system of 42 differential equations. The model parameters, most of them representing enzymatic activities, were identified and their values discussed with respect to the different liver cell bioreactor performance levels. The nitrogen balance based model was used as a tool to quantify the variability of runs and to describe different kinetic patterns of the amino acid metabolism, in particular with respect to glutamate (GLU) and aspartate (ASP).
Introduction
Bioreactor technology for extracorporeal liver support using primary human liver cells has been developed within the last decade [1]. The design of a bioreactor for maintaining the hepatocyte’s full functionality is of great importance. The used multi-compartment bioreactor consists of three interwoven, independent capillary membrane systems. Two of them (compartments 1 and 2, in the following aggregated to the ‘perfusion compartment’) provide decentralized plasma flow and the third one (compartment 3) provides oxygen supply to the cells, which are localized in the extracapillary space (compartment 4, ‘liver cell compartment’). This three-dimensional spatial structure represents an artificial equivalent of the hepatic vasculature at the lobular level. The bioreactor is integrated into a perfusion system that enables monitoring and control of system conditions (see Fig. 1). Previous studies have shown that primary liver cells reconstitute to tissue-like structures after inoculation into the bioreactor and that they maintain metabolic activities over several weeks [2–4]. This bioreactor therefore provides a valuable tool to analyze the dynamics and network structures of the physiological and molecular interactions of liver cells under standardized conditions that closely reflect the situation in the natural organ.
Fig. 1Scheme of the liver cell bioreactor with the perfusion circuit, the two inflow streams and the outflow stream. Measured data were acquired from the waste
Recently, data from this bioreactor system has been analyzed statistically as well as by fuzzy cluster and rule based data mining and pattern recognition methods in order to identify early performance predictors for the bioreactor’s long-term performance [5–7]. With respect to the prominent role of hepatocytes in the amino acid metabolism, the analysis and modeling of metabolic pathways of amino acids could provide important information on the functional state of hepatocytes cultured in vitro. In initial studies the role of amino acid regulation in extracorporeal liver support systems was analyzed [8]. More recently, three different modeling paradigms, i.e. correlation networks, Bayesian networks and systems of differential equations were applied to characterize the concentration profiles of six amino acids and related nitrogen-containing compounds in bioreactor cultures of primary human liver cells [6]. The present work reports on the model based analysis of the measured kinetics of 18 amino acids as well as ammonia (NH3) and urea (UREA) of 18 liver cell bioreactor runs.
Materials and methods
Cells and bioreactors
Primary human liver cells were isolated according to a method described elsewhere [9] from human donor livers (n=18) that were not suitable for transplantation due to organ injury. After isolation, cells were cultured within bioreactors over 7–34 days. (This broad variability in duration is caused by their use for liver support from the seventh day onwards and by the performance. Low performance runs were finished earlier. High performance runs were continued for a longer period of time. However, only the first 6 days of the runs were analyzed in this study.) Concentrations of free amino acids in the culture perfusates were determined by an automated reversed phase high performance liquid chromatography system (RP-HPLC) with precolumn derivatization using the ophthaldialdehyde method [10]. NH3 and UREA concentrations were determined using routine clinical analyzers (Roche Diagnostics, Heidelberg, Germany).
With respect to the model based analysis, two compartments of the multi-compartment capillary membrane bioreactor system were considered: The ‘liver cell compartment’ with the volume V2=600 mL contains the liver cells in the inter-capillary space. The ‘perfusion compartment’ with the volume V1=900 mL supplies a stream through the inside of the capillaries. This perfusion stream carries the concentrations of the compounds that are supplied to or removed from the bioreactor. Decentralized mass exchange at low gradients is achieved by independent perfusion of two medium capillary systems, enabling different perfusion modes [10]. In this study, counter-directional medium flow was performed to facilitate rapid matter distribution. Due to the high flow rate of the perfusion stream (250 mL min−1), an almost ideal mixing within the perfusion compartment can be assumed (Fig. 1). To this perfusion compartment two time-variant inflow streams are added with the flow rates FA(t) and FB(t) as defined by Eq. (1). FA(t) follows a step function from FA1=150 mL h−1 down to FA2=50 mL h−1 switching at tA(=1 day). FB(t) switches from FB1=0 up to FB2=1 mL h−1 at the time tB (see Table 4). The outflow rate F0(t) to the waste equals the sum of both inflow rates. (This is realized by overflow keeping a constant pressure in the perfusion compartment).  The inflow rate FA(t) carries the 18 amino acids and NH3 at the concentrations cAi (see Table 1). The inflow rate FB(t) carries only the amino acid aspartate (ASP) at the concentration cB15=1,500 μmol L−1, i.e. cBi=0 for all i≠15. The time courses of the concentrations c0i(t) in the outflow stream may be considered to describe the response of the medium to the inoculation of the bioreactor with cells. These concentrations are in steady-state at c0i(0)=cAi prior to inoculation. Due to the almost ideal mixing in the perfusion compartment, the concentrations c0i(t) in the perfusion stream are the same as in the outflow stream.
Table 1Model variables ci and c0,i for the liver cell compartment and the perfusion compartment, respectively, their initial values ci(0) and c0,i(0) before inoculation of the bioreactor with liver cells, the concentration cAi in the inflow stream fed with the flow rate FA(t) according to Eq. (1) and the stoichiometric coefficients si for nitrogenNameModel variables ci and c0,Ici(0)=c0,i(0)=cAi [μmol L−1]Stoichiometric coefficient siLEUc1, c0,122581HISc2, c0,29403ARGc3, c0,326044VALc4, c0,49871TRPc5, c0,54642PHEc6, c0,613001ILEc7, c0,75001ALAc8, c0,818251TYRc9, c0,922151LYSc10, c0,103272METc11, c0,112591SERc12, c0,128611GLYc13, c0,1319571THRc14, c0,148591ASPc15, c0,152841ASNc16, c0,161682GLUc17, c0,172651GLNc18, c0,186872NH3c19, c0,19411UREAc20, c0,2002PROTc21, c0,210–
Data
A data set with the elements ci,j,k (i=1,...,20; j=1,...,18; k=1,...,K) for 20 kinetic variables and 18 bioreactor runs at K time points tk was analyzed. The kinetics over the first 6 days of the bioreactor runs were analyzed here (tk=6 days). Concentrations of NH3 and UREA were measured daily (tk=0–6 days). Amino acid concentrations were determined up to the third day daily and every third day afterwards (tk=0, 1, 2, 3, 6 days). The samples for the measurement of the ci,j,k were taken from the waste of the liver cell bioreactor system (see Fig. 1) which was emptied daily after accumulation of the outflow stream over the period of 24 h.
The model based analysis described focuses on the amino acid metabolism as quantified by the measured time series of the concentrations of 18 amino acids (i=1,..., 18; see Table 1) as well as of NH3 (i=19) and UREA (i=20).
Each run was labeled by Lj ∈ {‘low’, ‘medium’, ‘high’} describing the performance with respect to the long-term maintenance of the functionality of the liver cells within the bioreactor. Seven runs were labeled ‘high’ (j=1,..., 7), seven runs ‘medium’ (j=8,..., 14) and four runs ‘low’ (j=15,..., 18). This performance labeling was provided by an expert based on his assessment of altogether 99 variables that were measured to quantitatively characterize the system.
Differential equation system
The differential equation system (2.1–2.10) was developed to describe the measured kinetics of the 18 amino acids as well as of NH3 and UREA (Table 1). This model takes into account two compartments as described in Cells and bioreactors, the ‘perfusion compartment’ and the ‘liver cell compartment’.
The Eq. (2.1) describe the dynamics of the components i in the perfusion compartment by four terms: The first and second term represent the fresh medium inflow with the volumetric rates FA(t) and FB(t), respectively, from the reservoir into the perfusion compartment with the volume V1. The reservoir concentrations cAi of the components i are specified in Table 1. The volumetric rates FA(t) and FB(t) are specified by Eq. (1). The third term in Eq. (2.1) denotes the outflow from the perfusion compartment into the waste with the volumetric rate F0(t). The last term describes the diffusion between the perfusion and the liver cell compartment with its rate being proportional to the difference of the concentration c0,i in the perfusion compartment and the concentration ci in the liver cell compartment. The parameter p0 is proportional to the diffusion coefficient and assumed to be the same for all compounds to simplify parameter identification.
The Eqs. (2.2–2.9) formulate the changes of the concentrations ci in the liver cell compartment with the volume V2 due to the exchange with the perfusion compartment (the first term on the right side of each differential equation) and the metabolic reactions (the following term(s) on the right side of each differential equation) as drawn schematically in Fig. 2.  The model drawn in Fig. 2 and its Eqs. (2.1–2.10) were constructed based on the mean kinetics of 20 measured variables averaged over the seven high-performance runs H1–H7 as shown in Fig. 3. The kinetics of these seven individual runs are highly correlated with their correlation coefficients being greater than 0.8 for at least six of the seven high-performance runs and the 11 amino acids MET, SER, THR, ARG, HIS, GLY, TRP, LEU, VAL, PHE, and ILE [6]. The dynamics of these amino acids are characterized by monotonously, almost exponentially decreasing kinetics [6]. Their dynamics were therefore described by first order reactions as formulated by the second term in Eq. (2.2). The observed decreasing kinetics of amino acid concentrations is the net result of anabolic and catabolic reactions, where the catabolism of amino acids (which was considered here only) surpasses the amino acid synthesis and release by proteolytic activities (which were neglected here). The second term with the parameters p1,..., p14 in Eq. (2.2) can be interpreted mainly as amino acid uptake by transamination and oxidative deamination (see Table 2). In addition to the 11 amino acids with highly correlated and monotonously decreasing kinetics, the time courses of the concentrations of the amino acids ALA, LYS, and TYR were, for simplification, also modeled using Eq. (2.2). The kinetics of the amino acids ASP, ASN, GLU, and GLN were modeled in greater detail by Eqs. (2.3–2.6). Glutamate (GLU) is known to be the central compound of amino acid metabolism. Its kinetics were found in [6] to be highly correlated with ASP. ASP was fed by the inflow rate FB(t) (see Cells and bioreactors) to avoid or compensate the exhaustion of ASP which is essential for NH3 elimination via the urea cycle. The degradation of amino acids is interpreted in a simplified form by transamination to GLU for the amino acids LEU, HIS, ARG, VAL, TRP, PHE, ILE, ALA, TYR, and LYS (denoted by ‘AAg’ in Fig. 2). The amino acids MET, SER, GLY, and THR (denoted by ‘AAn’ in Fig. 2), however, are not transaminated forming GLU, but deaminated via different pathways forming NH3. The parameters with the indices 15–18, 21–23 as shown in Fig. 2 and Table 2 describe the metabolic fluxes between ASP, ASN, GLU, GLN, and NH3. They can be interpreted in a simplified way by reactions catalyzed, e.g., by the enzymes asparaginase, aspartate aminotransferase (AST), glutamate oxalacetate transaminase (GOT), glutamine synthetase (GS) and glutamate dehydrogenase (GLDH). In general, the biochemical reactions were assumed to be either of linear or bilinear nature (first or second order). In two cases, however, additional assumptions were required to obtain sufficient model fits: (1) For the aggregated modeling of the NH3 elimination via the urea cycle in the presence of ASP a non-linear Michaelis-Menten-type kinetics was used (see Eq. 2.8). This reflects the observation that the UREA formation rate does not significantly increase any more at strongly elevated ASP concentrations (see ASP and UREA in Fig. 3). (2) Protein synthesis, i.e. anabolism, seems to be switched on or is strongly increased after the third day. This was modeled using Eq. (2.10). The total protein (PROT) was not measured, but a corresponding variable c21 was hypothetically introduced by Eq. (2.9) to improve the model fit of the other 20 variables to the measured data for t>3 days.
Fig. 2Structure of the model Eqs. (2.2–2.10). The numbers within the circles denote the indices i of the corresponding concentrations ci and the numbers at the arrows denote the indices m of the corresponding model parameters pm. (The diffusion process modeled by the parameter p0 is not shown here.) ‘AAg’ denotes the amino acids LEU, HIS, ARG, VAL, TRP, PHE, ILE, ALA, TYR, and LYS; ‘AAn’ denotes the amino acids MET, SER, GLY, and THRFig. 3Mean kinetics (± SD) of 20 measured variables (18 amino acids, NH3, UREA) averaged over the kinetics of the n=7 high performance runs H1–H7 (n=7)Table 2Parameters pm of the model (2) with interpretation, estimated values and confidence intervals [pmlow, pmhigh]; enzyme activities: AST aspartate aminotransferase, GOT glutamate oxalacetate transaminase, GS glutamine synthetase, GLDH glutamate dehydrogenase; values and units of the parameters pm of the model (2) as identified by the model fit to the mean kinetics averaged over the seven high performance runs (Fig. 3); PC_1, PC_2: first and second principal componentmInterpretation/enzymespm [pmlow, pmhigh]UnitPC_1PC_20Diffusion49628 [34681, 132980]mL h−10.18820.35301Aminotransferases (Transamination)LEU1.816 [0.000, 4.629]day−10.2679−0.01872HIS16.29 [3.42, 620.16]day−10.0251−0.29483ARG100.00 [88.75, 2655]day−1−0.14300.22534VAL1.147 [0.428, 23.112]day−10.2506−0.06175TRP15.99 [7.42, 833.88]day−10.2022−0.10636PHE4.148 [1.740, 9.520]day−10.2595−0.03647ILE3.723 [0.000, 6.213]day−10.2491−0.02688ALA1.573 [0.000, 6.213]day−10.25140.01359TYR1.135 [0.559, 3.450]day−10.1866−0.273010LYS0.192 [0.000, 2.164]day−10.23580.005311Other specific reactionsMET34.53 [7.27, 284.76]day−10.25870.117112SER10.80 [3.34, 102.71]day−10.26980.039913GLY10.76 [2.69, 251.25]day−10.26740.084914THR9.326 [3.16, 420.70]day−10.2543−0.054615Asparaginase0.0009 [0.0000, 0.002]day−1 μmol−1 L0.18250.4175163.593 [1.215, 8.505]day−10.22540.297117AST50.08 [39.25, 59.37]day−1−0.06500.034118GOT56.73 [50.35, 71.85]day−10.18190.145619Urea Cycle Km (ASP)225.76 [187.7, 378.9]day−1 μmol−1 L0.2000−0.3185205.00 [0.9375, 6.17]μmol L−1−0.10230.181321GS0.0033 [0.001, 0.007]day−1 μmol−1 L0.1198−0.1839220.0205 [0.001, 0.039]day−10.1017−0.391723GLDH, Protein Synthesis40.68 [24.12, 50.38]day−10.11210.0330240.5475 [0.465, 0.813]–−0.0057−0.1114
The model parameters were identified by fitting the model to the measured data ci,j,k minimizing the scaled mean square error (mse) as defined by Eq. (3) for run j. The model fitting error mse was scaled by the square of the maximum of the respective measured variable. According to Eq. (3), the kinetics c0,i(t) obtained from the simulation of Eqs. (2.1–2.10) with the initial values listed in Table 1 were averaged over 24 h (i.e. over the time interval of the accumulation of the bioreactor outflow in the waste where the samples for the measurements were taken from daily) and then compared with the measured data ci,j,k.  The differential equations were solved using a Runge-Kutta fourth order algorithm. The parameter identification by model fitting minimizing the mse was performed using a simplex search method. MATLAB tools (The MathWorks, Inc., Natick, MA, USA) were used for all calculations.
Results and discussions
The mean time profiles of the 20 measured variables each averaged over the high performance runs H1–H7 are shown in Fig. 3. The results of the model fitting to these averaged data are shown in Fig. 4 and the identified model parameters p0,..., p24 are listed in Table 2 (third column). The confidence intervals for the parameter values (fourth and fifth column in Table 2) were identified by repeated model fitting to the randomly disturbed time series data with means and standard deviations as shown in Fig. 3. The fit of the modeled kinetics to the measured data is acceptable for 18 of the 20 variables with the exception of lysine (LYS) and alanine (ALA). The initial increase of the LYS kinetics could be hypothetically explained by proteolytic activities. The final increase of ALA can be explained by the ALA aspartate transferase activity. The decreasing kinetics of 11 out of the 18 amino acids is explained by transamination and oxidative deamination forming GLU and NH3. The feeding of ASP after the third day results not only in an increase of the concentration of ASP but also of asparagine (ASN), GLU, and glutamine (GLN) due to the activities of the enzymes AST, GOT, GS, and asparaginase (alternatively or additionally the activity of asparagine synthetase could be included in the model).
Fig. 4Measured and simulated kinetics of the concentrations of 18 amino acids, NH3 and UREA. The measured kinetics (dots) are those of the mean profiles shown in Fig. 3 (n=7). The simulated kinetics c0i(t): (thin lines) are those obtained from the model (2). The simulated kinetics c0i(t) were averaged according to Eq. (3) over the past 24 h (thick lines) in order to use them for model fitting to the measured data that were acquired from the waste (outflow accumulated over 24 h)
Table 3 lists the model parameter values pm with the respective model fitting error mse as identified by individual model fitting to the seven high performance runs. Figs. 5–7 show the results of the model fitting to the data for the individual high performance runs H1, H5, and H7, respectively. The kinetics of run H2 (not shown here) is very similar to the mean kinetics of the seven high performance runs shown in Fig. 4. The quasi-stationary concentrations of several amino acids, such as MET and LEU, are lower in run H5 (see Fig. 6) than in run H2 (compare Fig. 4). This results in a p0 value, which is about 6.5× higher for run H5 than for run H2 (see Table 3). While the variability of the parameter values p1,..., p24 can be explained by differences in the liver cell material obtained from different individual donors (having different age, weight, liver damage, etc.), it is not entirely clear at this stage how to explain such different values of the parameter p0 that represents diffusion and potentially other phenomena taking place across and beyond the membrane between the perfusion and the liver cell compartment. These issues are addressed in greater detail in [11] as well as in studies described in [12] to elucidate the dynamics of electrolyte distribution in the bioreactor without liver cells using tracer experiments and input/output systems analysis.Table 3Values of the parameters pm of the model (2) with the values of the scaled mean square error (mse) as identified by individual model fits to the seven high performance runs H1–H7mH1H2H3H4H5H6H70937624859915419060636318770949398101012.43642.11022.75503.75785.85762.930202224.49445.133536.905850.803938.03622.913921.94173342.2608285.530379.992797.785355.361589.3504278941.50421.94421.33832.24653.23482.2254058.391812.479319.586688.639644.309819.81934.866662.86253.79438.639110.571211.08434.07560.834574.83146.49384.00106.018610.17706.62230.010283.54400.91921.77712.03255.82352.7174092.13891.56292.44631.57461.29080.854801000.02.21511.52131.69980.470801110.800524.378383.028053.8904116.586825.381312.99081210.07679.470921.121816.373531.182010.25363.38151310.05735.139727.421322.631545.111610.22233.25321411.66507.21708.506922.044225.209312.49543.0131150.00080.00070.00180.00070.01290.00080.0007165.15803.47423.85547.125130.91683.79471.20831740.468559.405837.907663.556960.347343.376069.34681869.917457.329772.578270.4225109.786752.520163.280919291.5727192.9329341.5933334.0218238.6410263.7827111.2798205.56698.42293.25044.82613.82513.78088.6594210.00140.00.01140.00260.00060.00520.0034220.05130.00.00310.02800.00040.01570.00002352.597035.600151.898249.646767.233839.360326.0489240.57070.68330.19630.59330.61320.68230.6408mse0.01620.01430.02020.01150.01350.01450.0321Fig. 5Measured data of the 20 variables of bioreactor run H1 with the simulated kinetics of the model fitted to the data of H1Fig. 6Measured and simulated kinetics as in Fig. 5, but for run H5
The model, which was developed using the kinetic patterns of high performance runs was also fitted to the data of the 11 runs with medium and low performance. The aim of these model studies is to elucidate causes of low liver cell bioreactor performance. The fit of the model with 25 parameters to 18 bioreactor runs results in an array of 25×18 parameters. Three selected parameters with their values as identified by fitting the model to the individual 18 runs are listed in Table 4. Considering a tolerable model fitting error mse of about 2% or less, the model fit proved satisfactory for six of the seven high performance runs (with the exception of run H7) and for two of the seven medium performance runs (M8 and M10). However, the model fit was not acceptable for the high performance run H7, the medium performance runs M9, and M11–M14 as well as all four low performance runs L15–L18. The main reason for the insufficient model fits is that the measured kinetics of leucine (LEU), isoleucine (ILE), valine (VAL), ALA, and other amino acids are not decreasing in these runs. This behavior is shown in Fig. 7 for run H7. In these cases some parameter values, e.g., for p1, were found to be zero (negative parameter values were set to zero; see Table 4). These zero parameter values cause that the medium and low performance runs displayed in Fig. 8 are artificially close to each other. Run H7 labeled as a high performance one appears to be a medium performance run according to the model simulation results (see Figs. 7 and 8).Table 4Parameters for the 18 liver cell bioreactor runs: tB as used in Eq. (1); the star (*) for run 13 denotes that the feed FB(t) was switched on during the period from the second to the fourth day; the scaled mean square error (mse) and the parameters p1, p2, and p22 as used in Eq. (2) were identified by model fitting to the data of the high performance runs H1–H7 (averaged) and of the individual high performance runs H1,..., H7, medium performance runs M8,..., M14 and low performance runs L15,..., L18Run No.tB[d]mse [-]p1 [d−1]p2[d−1]p22*1,000 [d−1]H1-H7 (averaged)30.00991.81616.2920.5H130.01622.4364224.494451.2761H230.01432.11025.13350H330.02022.755036.90583.1020H430.01153.757850.803928.0101H530.01355.857638.03620.4057H630.01452.93022.913915.7285H730.0321021.94170.0186M830.01471.03678.77420.0497M950.033002.86110.0381M1050.01990.000811.29470.0578M1130.03130.001032.36580.2347M1230.02590.9387168.22990.0722M132–4*0.15590.000311.64390.1391M1430.04920.000310.97870.1615L1500.2747039.92250.0572L1660.080302.59350.0268L1730.08640137.27210.0131L1830.132101.26130.0560Fig. 7Measured and simulated kinetics as in Fig. 5, but for run H7Fig. 8Biplot of the first and second principal components of the model parameter matrix {p0,...,p24} as identified for the 18 liver cell bioreactor runs (high performance runs H1,...,H7, medium performance runs M8,...,M14, low performance runs L15,...,L18)
Figure 8 displays the identified model parameters of the 18 liver cell bioreactor runs after principal component analysis (PCA) of the parameter array for the first and second principal components in a biplot [13]. The first principal component represents 53% of the total parameter variance and the first and second principal components together represent almost 64% of this variance. Most of the parameters p1–p14 (with the exception of p2, p3, p9) representing transamination and oxidative deamination activities mainly constitute the first principal component of the parameter array (see Table 2: PC_1>0.2 for these parameters). These 11 parameters are correlated: The 55 correlation coefficients calculated between the 11 parameters estimated over the 18 runs have values between 0.54 and 0.987 with the median of 0.87 and the mean of 0.84. The values of these 11 parameters are high for six of the seven high performance runs and low for run H7 as well as for the medium and low performance runs as shown in Table 4 for parameter p1. The value of the parameter p1 is strongly correlated with the bioreactor performance (p-value 0.00017 by the two-sided t-test of pm values of high versus medium or low performance runs). The mean value of the parameter p1 averaged over the high performance runs equals 2.84 (±1.77). The parameter is zero for all low performance runs. In Fig. 8 the medium and low performance runs are displayed in the left part describing low transamination and oxidative deamination activities whereas six of the seven high performance runs (with the exception of run H7) are displayed in the right part representing high transamination and oxidative deamination activities.
Some parameters are highly correlated over the 18 runs with a correlation coefficient r of more than 0.98, e.g., the parameters p11 and p13 (representing the catabolism of methionine and glycine) as well as p4 and p7. Also, the parameter p1 is highly correlated (r>0.96) with the parameters p4 and p7. The high correlation of the parameters p1, p4, and p7 representing the transamination of LEU, ILE and VAL can be hypothetically explained by the fact that LEU, ILE and VAL are branched-chain amino acids which cannot be transaminated in hepatocytes. To transaminate these amino acids, the activity of non-parenchymal liver cells is required.
Among the high performance runs there are further individual differences that are displayed in Fig. 8 by a low second principal component PC_2 (e.g., high values of the parameters p2 and p22 for run H1) and a high PC_2 (e.g., low values of the parameters p2 and p22 for runs H2, H5, and H7). The parameters p2 and p22 represent the uptake of histidine and glutamine (see Table 2). As shown in Fig. 5 for run H1, the high parameter value p2 results in a low stationary concentration of HIS and the high parameter value p22 results in high NH3 and low GLU levels. These effects could be related to the amino acid transport system N (SN1) that mediates specifically the uptake of histidine and glutamine [14]. The parameter p21 that represents the activity of GS was found to be very low for the high performance runs H2 and H5 (see Table 3). GS plays an important role in the spatial organization (zonation) of the liver and is exclusively expressed in pericentrally located hepatocytes [15]. The low GS activity in the high performance runs H2 and H5 could therefore be caused by a high proportion of periportal versus pericentral hepatocytes in the cell preparations for these two runs.
Conclusion
The kinetics of 18 amino acids and the related nitrogen-containing compounds NH3 and UREA in a primary human liver cell bioreactor were analyzed and modeled using a differential equation system. The model focuses on the kinetics of GLU and ASP as well as on the formation and elimination of NH3 and the synthesis of UREA. It describes the degradation of amino acids by transamination, oxidative deamination and other specific reactions. In addition, the activities of selected enzymes such as AST, GOT, GS, and GLDH as well as, in a more aggregated form, the activities of urea cycle enzymes are included. The differential equation system does not represent a fully mechanistic but rather a phenomenological model since essential metabolic activities had to be neglected because they cannot be identified based on the measured data.
The differential equation system allows the analysis of a number of representative liver cell functions in terms of their kinetic behavior. The identification of the model parameters by fitting the model responses to the measured data was used to generate hypotheses about the causes of specific differences between the bioreactor runs. The model fits were found to be very satisfactory for eight high and medium performance runs. The model is however inadequate for low performance runs and with respect to the LYS kinetics also for high performance runs. This is probably caused by the neglection of proteases activities in the model that appear to be relevant for low and medium performance runs. Both, protein synthesis and degradation could not be modeled in detail due to the lack of representative protein measurements.
The applied model based analysis of data obtained from the bioreactor system can be used to quantitatively evaluate the functional state of liver cell cultures under high performance conditions intended for clinical application in extracorporeal liver support systems. The approach can also be used to study the effect of several exogenous factors, e.g., of hormones or drugs, on hepatocyte metabolism in vitro. The model based analysis methods applied here therefore provide suitable tools for in silico studies supplementing in vitro studies of hepatocyte functions in a systems biological way.

bioreactor
liver support
systems biology
metabolic network


Development and application of a Japanese model of the WHO fracture risk assessment tool (FRAX™)

Summary The present study estimated the 10-year probability using the Japanese version of WHO fracture risk assessment tool (FRAX™) in order to determine fracture probabilities that correspond to intervention thresholds currently used in Japan and to resolve some issues for its use in Japan.
Introduction
Fractures related to osteoporosis have become a major health and economic burden in Asian countries just as they have in North America and Europe. An estimated 117,900 cases of hip fracture occurred in 2002 [1], and the incidence in Japan has increased in the past 10 years [1, 2]. Asia will be expected to have the highest absolute increase in fracture number because it has the largest population. Early detection of individuals with high fracture risk using clinical risk factors would have a substantial impact on reducing the burden of fractures in Asia.
A series of meta-analyses on prospective population-based cohorts has identified a number of clinical risk factors that contribute to fracture risk independently of BMD at the femoral neck [3]. The integration of these risks would, therefore, enhance the predictive value of BMD [4]. The risk factors comprise age, sex, bone mineral density, body mass index (BMI), long-term use of glucocorticoids, parental history of hip fracture, history of fragility fracture, smoking, alcohol consumption (3 or more units/day), and secondary osteoporosis such as rheumatoid arthritis. A WHO scientific group has proposed that the 10-year probability for fracture is used to express fracture risk for clinical assessment [5] and to determine intervention thresholds [3] The aim of this study was to create a fracture probability model based on the methodology of the WHO risk assessment tool (FRAX™) [6] calibrated to the epidemiology of Japan.
In addition, several problems need to be resolved before the FRAX™ model is applied to Japan. First, the FRAX™ tool inputs femoral neck BMD and the Z-score or T-score is based on the NHANES III reference data base. In Japan, BMD at the lumbar spine is widely used clinically because the physical size of Japanese people is smaller than that of Western people, giving rise to a view based on little evidence that the reproducibility of measurements at the femoral neck BMD would be poorer than that at the lumbar spine. Furthermore, data on the young adult mean (YAM) and the mean at each age are installed in the DXA systems in Japan, and programmed to calculate T- and Z-scores from Japanese reference data. In addition, the Japanese Society for Bone and Mineral Research [7, 8] provide recommendations for the diagnosis of osteoporosis and intervention based on YAM, and these are widely used in clinical practice.
Against this background, additional aims of the present study were to provide fracture probabilities based on the FRAX™ tool that were equivalent to currently accepted intervention thresholds, explore the impact of using Japanese-specific normative data for femoral neck BMD, and reassess the respective performance characteristics of BMD at the femoral neck and lumbar spine.
Methods
Models were constructed to compute the 10-year probability of hip fracture and a major osteoporosis-related fracture in Japan. A major osteoporosis-related fracture was defined as a clinical spine, hip, proximal humeral and forearm fracture. Poisson modelling was used to calculate the hazard functions. The relationship between probability and hazard functions were used to calculate the 10-year probability or fracture for a combination of the risk factors. The mortality estimates for Japan were those published by the World Health Organization for 1999, which accord with estimates from Japan [9]. The incidence of hip fractures was taken from previously published sources [1] as was the incidence of fractures at the proximal humerus and distal forearm [10]. Since the incidence of a clinical vertebral fracture was not known in Japan, we assumed that the ratio of clinical vertebral fracture incidence to that of a vertebral fracture diagnosed by radiographic surveys [11] would be the same in the Japan as it was for Sweden [12].
The relationship of clinical risk factors to fracture outcomes was assumed to be the same as that determined in a large meta-analysis of risk factors of 190,000 patient years from nine prospectively studied population-based cohorts from Europe, Australia, North America and Asia [3]. The relationship has been validated in a further 11 cohorts of population-based samples with 1.2 million patient years of observation from the same regions [4]. The independent contribution of each risk factor was used to compute probabilities of fracture in the absence of clinical risk factors or in the presence of any combination [13, 14].
In Japan, the criteria for the diagnosis of osteoporosis prepared by the Japanese Society for Bone and Mineral Research [7] are based on BMD measurements expressed as a percentages of the young adult mean (YAM) for women. In patients with no prior fragility fracture a diagnosis of osteoporosis is made where the BMD is less than 70% of YAM. In patients with a previous fracture, osteoporosis is diagnosed where the BMD is less than 80% of YAM. These diagnostic thresholds, derived by maximising sensitivity and specificity for fracture detection, are also used as intervention thresholds. In order to compare intervention thresholds using YAM with probabilities derived from the FRAX™ algorithm, T-score equivalents were used. The T-score equivalent to 70% and 80% of YAM for Japanese people is −2.7 SD and −1.8 SD, respectively, using the NHANES III reference for BMD at the femoral neck in Caucasian women aged 20–29 years [15].
The relative performance characteristics of BMD at the lumbar spine and femoral neck were examined in a population-based prospective study in Hiroshima. The Hiroshima cohort comprised 2,596 men and women (69% female, 9,803 person years, mean age 65.1 years). Details of the cohort have been previously published [11]. In brief, the participants received measurement of lumbar spine and femoral neck BMD using dual X-ray absorptiometry (DXA, QDR-2000, Hologic) during the period from 1994 to 1995 and were followed for a mean period of 4 years. Information about hip fracture, fracture of the distal radius, proximal humeral fracture and clinical spinal fracture was collected at interview by trained nurses and physicians during the biennial health examinations. One hundred eighty-six fractures were detected during the follow-up period, of which 89 were categorized as osteoporotic fractures and 31 were hip fractures. The gradient of fracture risk (increase in fracture risk per SD change in Z-score for BMD and increase in fracture risk per 0.1 g/cm2 change in BMD) at the two sites was determined by the use of Poisson models [16]. The fracture outcomes were calculated for hip fracture, a major osteoporosis-related fracture (femoral neck, distal radius, proximal humerus and clinical spine fracture) and all fractures.
The mean height and body weight for Japanese women in the Hiroshima cohort was 150 cm and 52.6 kg, respectively, giving a BMI of 23.4 kg/m2. The calculation of fracture probability was made at this BMI, but differences in BMI have little effect on predictive value for fracture risk assessment in the presence of BMD [17].
Japanese-specific T- and Z-scores and those derived from NHANES III were compared using the database of the Japanese Society for Bone and Mineral Research [7]. Data using both methods of calculation were entered into the FRAX™ tool.
Results
Ten-year probability of fracture
The 10-year probability of a major osteoporotic fracture for individuals without clinical risk factors is shown in Fig. 1 grouped by sex, age and T-score. The 10-year probability increased with age and with decreasing T-score. At younger ages, the fracture probability was similar in men and women. With advancing age, probabilities rose as expected, but the increase was greater in women than in men. In men aged 80 years, the 10-year probability for osteoporosis-related fracture exceeded 10% at a T-score of −3 SD, whereas in women of the same age, fracture probabilities exceeded 10% with T-score of −1 SD.
Fig. 1Ten-year probability (%) of osteoporotic fracture (hip, clinical spine, humerus, forearm) in Japanese men and women without clinical risk factors according to age and T-score for BMD at the femoral neck
The contribution of clinical risk factors to fracture probability is shown in Fig. 2 for women aged 65 years with a BMI of 23.4 kg/m2. In women without clinical risk factors, the 10-year probability for an osteoporosis-related fracture was 7.5%. The 10-year probability was higher in the presence of clinical risk factors. Smoking and alcohol were relatively weak risk factors, the use of long-term glucocorticoids of intermediate weight, and a parental history of hip fracture or a prior fragility fracture were associated with the highest risks. For example, the 10-year probability was 8.1% for smokers and 14.5% for individuals with a prior fracture. The 10-year probability for hip fracture was 1.1% in women without a clinical risk factor, 1.6% in smokers and 2.7% in women with a previous fracture (see Fig. 2).
Fig. 2Ten-year probability for osteoporotic (hip, clinical spine, humerus, forearm) and hip fracture (%) according to the presence of a clinical risk factor, in women at the age of 65 years and with a BMI of 23.4 kg/m2
Fracture probabilities were computed in women at the diagnostic threshold recommended in Japan. Thus, the cut-off level of BMD was set at 70% of YAM in women without a previous fracture and at 80% of YAM in those with a previous fragility fracture. In women aged 50, 60, 70 and 80 years without clinical risk factors and with BMD equivalent to 70% of YAM, the 10-year probability was 5.4%, 8.7%, 13.8% and 23%, respectively. In women having BMD equivalent to 80% of YAM and existing fracture but no other clinical risk factors, the 10-year probability was 7.1%, 10.5%, 14.7% and 23.4% at the same ages, respectively. Thus, at each age, the fracture probability was similar using the two diagnostic criteria. In contrast, the fracture probability equivalent to the diagnostic threshold in Japan rose with age, and at the age of 80 years was about four times higher than that at age 50 years (Fig. 3). Similar findings were apparent for hip fracture probability in that probabilities equivalent to the diagnostic threshold in Japan rose with age. The increase with age was more marked than for all major fractures and at the age of 80 years was about 6–40 times higher than that at age 50 years depending on the threshold used (see Fig. 3).
Fig. 3Ten-year probability of osteoporotic (hip, clinical spine, humerus, forearm) and hip fracture based on women at the threshold for the diagnosis of osteoporosis using the criteria of the Japanese Bone Mineral Metabolism Association
Comparison of lumbar spine and femoral neck BMD
The gradient of fracture risk for spine BMD and femoral neck BMD in the Hiroshima cohort indicated that lumbar spine measurements predicted all fractures, osteoporosis-related fracture and hip fracture with approximately equal gradients of risk that ranged from 1.25/SD for all fractures to 1.17/SD for hip fractures. There was no difference in the gradient of risk between men and women. In the case of hip fracture risk, the gradient of risk in men and women combined was not statistically significant with BMD measured at the lumbar spine. BMD at femoral neck had a similar or slightly higher gradient of risk for fractures compared with spine BMD, particularly in the case of hip fracture (Table 1). There was no significant difference in gradient of risk between lumbar spine BMD and femoral neck BMD with the exception of that for hip fracture where the gradient of risk was significantly higher for measurements made at the femoral neck. When gradient of risk was standardized to a constant denominator (i.e., RR/0.1 g/cm2) the findings remained unchanged (see Table 1).
Table 1Predictive ability of spine and femoral neck BMD for any, osteoporotic and hip fracture in men and women from Japan RR/SDRR/0.1 g/cm2RR95% CIRR95% CIa. Any fractureFemoral neck1.451.23–1.701.431.22–1.68Lumbar spine1.251.13–1.391.381.19–1.61b. Osteoporosis-related fracturesFemoral neck1.401.09–1.781.381.09–1.74Lumbar spine1.201.04–1.401.301.05–1.61c. Hip fractureFemoral neck2.08b1.34–3.222.111.38–3.23Lumbar spine1.170.91–1.501.250.87–1.80a Hip, clinical spine, forearm and proximal humerusb Significantly higher than lumbar spine ( = 0.049)
Japanese reference values
The reference mean in women aged 20–29 years at the femoral neck was 0.858 g/cm2 (SD = 0.120 g/cm2) using the NHANES III data. When young normal values were computed from the Japanese population the mean BMD was 0.786 g/cm2 (SD = 0.107 g/cm2). Thus the threshold for osteoporosis using the NHANES III data was 0.558 g/cm2 and that derived from the Japanese data was 0.519 g/cm2. The thresholds for osteopenia (WHO definition) were 0.738 g/cm2 and 0.679 g/cm2, respectively. Thus there were systematic differences in the T-score derived from the two data sets. A comparison of fracture probabilities computed from the z-scores using the two approaches is shown in Fig. 4 for different combinations of risk factors. The differences in probabilities were relatively modest, but as expected, the use of Japanese reference values overestimated fracture probabilities.
Fig. 4Correlation between 10-year fracture probabilities (%) in women without clinical risk factors computed from normative data using NHANES III reference values and Japanese-derived reference values for femoral neck BMD. BMI is set at 23.4 kg/m2
Discussion
This paper describes the development of the WHO fracture risk assessment tool calibrated to the epidemiology of Japan. The incidence of hip fracture, distal radius fracture and proximal humeral fracture in Japan is lower than that in North America or Northern Europe [1, 10]. However, the prevalence and incidence of spinal fracture are higher in Japan [11, 18]. A minority of all cases of morphological spinal fracture are assumed to be clinical spine fractures in the Japanese FRAX™ models. The multiplier is age and sex specific. For men the multiplier goes from approximately 33% at age 50 to 48% at age 85. For women the corresponding figures are 19% and 24%. These estimates, derived from the epidemiology of fracture in Sweden [12], have been shown to hold true for Japan [19].
The FRAX™ algorithm is suitable for assessment in men and women from the age of 40 years and calculates the 10-year probability for both hip fracture and a major osteoporosis-related fracture. One of its strengths is that it can capture the independent contribution of several clinical risk factors to fracture risk and can be used with or without information on femoral neck BMD. A more detailed account of the properties of the FRAX™ models is provided elsewhere [13]. In this paper, we focused on its application to decision-making in clinical practice with the estimation of intervention threshold i.e., the fracture probability at which intervention is currently considered to be worthwhile. The approach should be distinguished from intervention thresholds that are based on health economic analysis.
The WHO makes no specific recommendation concerning intervention thresholds, since these depend on many local factors [6]. Rather, it is suggested they should be determined by each country, based on the local healthcare situation and cost-effectiveness of the treatment of osteoporosis. Intervention thresholds, based on cost-effectiveness have been formulated in the UK, the USA and in Sweden [20–22]. In Japan, diagnostic thresholds are used as intervention thresholds. When the probabilities of osteoporosis-related fracture were determined at these thresholds, they varied with age (see Fig. 3), ranging from approximately 5% at the age of 50 years to more than 20% at the age of 80 years. Against this background, a 10-year probability of 10% for osteoporosis-related fracture may be an acceptable intervention threshold for Japan, though an optimization should take account of health economic consequences for individuals and for the health care budget.
The FRAX™ tools are designed to be extensively used in the world as a means of identifying individuals with elevated risk for fracture and aid in the determination of the threshold for therapeutic intervention, but there will be hurdles to be faced in the ease of its acceptance. Such hurdles are likely to differ from country to country. In Japan, the choice of clinical risk factors is not at issue since the risk factors adopted in the FRAX™ algorithms included the data from the Hiroshima cohort, Japan [11, 23, 24], and the validation included the Japanese Miyama cohort [13]. More problematic is the inclusion of femoral neck BMD, since the lumbar spine measurement is the most widely used in Japan. Asian physiques are smaller than those of Caucasians, and the geometric characteristics of the femoral neck in Japanese differ from those in American women, in that the femoral neck length is shorter than in the Japanese [25, 26]. Because of uncertainty regarding measurement of femoral neck BMD, measurement of lumbar BMD is widely used in Japan. The present study indicates that concerns over the use of femoral neck BMD are unfounded.
Femoral neck BMD was superior in its ability to predict hip fractures compared with spine BMD, and BMD at the spine and femoral neck had similar predictive value for fractures other than hip fracture. Fujiwara et al. [11] have also shown that the ability to predict the risk for morphological spinal fracture was similar between femoral neck BMD and lumbar spine BMD. These data on gradients of fracture risk, derived in Japan, did not differ from those reported in Western countries. Indeed, the meta-analysis used to inform the FRAX™ tool showed no evidence for heterogeneity in gradients of risk between cohorts [16]. Thus the evidence suggests that fracture risk assessment is not disadvantaged by the use of femoral neck BMD. Indeed, the converse may be true.
A further hurdle, unique to Japan, is that diagnostic thresholds differ from the WHO description of osteoporosis which defines osteoporosis on the basis of a fixed T-score threshold (≤ −2.5 SD) using an international reference standard for young (aged 20–29 years) Caucasian women [15]. In Japan, diagnostic thresholds are also derived by reference to a young population, but differ from the WHO in that a local (i.e., Japanese) standard is used and that the criteria differ in patients with or without previous fracture. In view of the widespread use of data derived from the Japanese population, the question arises whether T-scores or Z-scores derived from Japanese databases could be used in the FRAX™ tool, rather than those derived from the international reference base. In the present study, mean BMD at the femoral neck was lower in Japanese women than in the NHANES III sample from the USA, as previously shown [27]. The difference was not, however, large (approximately a half a SD). There were also differences in the SD which was smaller in Japanese women than in the NHANES III sample (0.107 and 0.120 g/cm2, respectively). Not surprisingly, the DXA-based T-score obtained from Japanese and USA populations differed as did the computed probabilities. Although the differences were small at low T-scores, when applying the FRAX™ model to Japan, it is preferable to program the system so that the Japanese T- and Z-scores are converted into the appropriate T- and Z-scores based on NHANES III.
In conclusion, a FRAX™ tool has been developed to compute fracture probabilities calibrated to the epidemiology of Japan. The tool has been used to determine possible thresholds for therapeutic intervention, based on equivalence of risk with current guidelines. The approach will need to be supported by appropriate health economic analyses. The present study indicates that the femoral neck BMD is suitable for prediction of the risk for fracture among Japanese people. However, when applying the FRAX™ model to Japan, T-scores and Z-scores should be converted into those derived from the international reference.

japan
fracture
fracture risk assessment tool
fracture probability
intervention thresholds
bone mineral density


Do We Practice What We Preach? A Review of Actual Clinical Practice with Regards to Preconception Care Guidelines

Objectives: To review what past studies have found with regard to existing clinical practices and approaches to providing preconception care. Methods: A literature review between 1966 and September 2005 was performed using Medline. Key words included preconception care, preconception counseling, preconception surveys, practice patterns, pregnancy outcomes, prepregnancy planning, and prepregnancy surveys. Results: There are no current national recommendations that fully address preconception care; as a result, there is wide variability in what is provided clinically under the rubric of preconception care. Conclusions: In 2005, the Centers for Disease Control and Prevention sponsored a national summit regarding preconception care and efforts are underway to develop a uniform set of national recommendations and guidelines for preconception care. Understanding how preconception care is presently incorporated and manifested in current medical practices should help in the development of these national guidelines. Knowing where, how, and why some specific preconception recommendations have been successfully adopted and translated into clinical practice, as well as barriers to implementation of other recommendations or guidelines, is vitally important in developing an overarching set of national guidelines. Ultimately, the success of these recommendations rests on their ability to influence and shape women's health policy.

There is a strong body of evidence to demonstrate that preconception care can modify behavioral, medical, and other health risk factors known to impact pregnancy outcomes. For example, preconception care efforts and interventions have been shown to improve folate status among women planning to conceive and to reduce the risk of fetal alcohol syndrome [1, 2]. Controlling known medical problems prior to pregnancy not only helps to optimize the mother's health, it may also improve neonatal and pediatric outcomes [3]. Preconception care efforts and interventions have been shown to lower the risk for both major and minor birth defects among the children of women with pre-existing diabetes [4]. Women with epilepsy and their offspring benefit from preconception care counseling that manages medications, optimizes seizure control and prescribes folic acid for neural tube defect (NTD) prevention [5, 6].
While there is a burgeoning knowledge base on how to improve pregnancy outcomes, there are few studies examining the effects of this knowledge base on actual practice patterns. It is not well known to what extent health care providers are translating preconception care knowledge into practice for all women capable of becoming pregnant (whether planning pregnancy or not).
Material and methods
A literature review between 1966 and September 2005 was performed using Medline to examine the impact of preconception care trials and recommendations on the clinical practice patterns of health care providers. Key words included preconception care, preconception counseling, preconception surveys, practice patterns, pregnancy outcomes, prepregnancy planning, and prepregnancy surveys. Relevant review articles regarding preconception care were also included. All studies had to be done in the United States. Studies that examined existing clinical practices and approaches directly to providing preconception care were eligible for inclusion. Studies that used indirect methods of measurement, e.g., interviews with women as a means of assessing preconception care practices of providers, were not included. A total of 11 studies were located and included analyses of practice patterns of various health care providers and specialties (see Appendix).
Results
Studies researching health care provider awareness, knowledge and practices regarding preconception care in the United States are infrequently performed, so it is difficult to fully assess health care provider approaches and practices related to preconception care. A small pilot study involving family practice nurse practitioners was done in 1987 to determine what preconception care issues were discussed with women of childbearing age during a well woman visit. Audiotapes of the visits were compared to an investigator-developed model for preconception counseling. Based on the results, the authors concluded that current obstetrical care had not expanded to include preconception counseling [7].
A study among family physicians conducted in 1991 using the Comprehensive Prevention Knowledge and Applications Survey Instrument found that only 37% of providers reported counseling women of childbearing age about preconception behaviors >75% of the time [8]. When asked how often they felt ready to counsel women on preconceptional factors, about 70% of providers said they were prepared at least three-fourths of the time. However, perceived preparedness did not equate to counseling success. Among providers who did counsel, only about one-third reported being successful at helping their patients change preconceptional behaviors at least 75% of the time.
A 1991 study conducted among family practice and internal medicine residents in an inner-city public hospital found that both groups of residents reported low levels of knowledge and management decision skills regarding preconception care when compared with standard recommendations, despite their favorable attitudes towards preconception care [9]. In this survey, close to 50% of the residents did not mention family planning during counseling sessions with women of reproductive age and 74% would not raise the issue of congenital defects in their diabetic patients seeking to become pregnant. While both family practice and internal medicine residents had favorable attitudes towards preconception care, the family practice residents scored higher in positive attitude about preconception care. There was no difference between the groups in terms of management skills, and there was no sustained difference between groups in the area of knowledge. Previous rotation through a high-risk perinatal clinic increased scores in areas of management and knowledge compared to family practice or internal medicine residents who had not rotated through such a clinic, but these differences were not significant [9].
In an effort to assess the practices of genetic counseling and screening for consanguineous couples, their pregnancies, and their children, 1,582 surveys were mailed to board certified genetic counselors and medical geneticists in the United States in 1999 [10]. While the response rate was very poor (only 20%) there was wide variability in suggested screening practices for consanguineous couples before conception, during pregnancy, after birth, and for children placed for adoption. Respondents seemed to generally agree to do screening based on ethnicity but there was no consistency regarding which genetic disorders to include in these screening efforts.
A mailed survey of obstetrician-gynecologists in 2000 focused on issues around folate for the prevention of NTDs [11]. Almost 97% of participants knew that when taken early enough, folic acid reduced the incidence of NTDs. While two-thirds of respondents said they routinely screened their pregnant patients for folate intake, only 53% screened nonpregnant women of childbearing age. The authors of the study concluded that most obstetrician/gynecologists are aware of the link between folate intake and NTDs although the data showed that physicians who routinely screened for folate intake correctly answered more survey questions than those who did not.
As part of a larger effort to improve the documentation and delivery of preconception care, Bernstein and colleagues evaluated the knowledge base and awareness of preconception care for women in their childbearing years seeking care in an inner city outpatient gynecology clinic in 2000 [12]. Providers in the clinic included physicians and nurse practitioners. A pre-intervention chart review found that many of the providers were not addressing family planning services, domestic violence, nutrition and medical risk factors, medication use, appropriate counseling and use of referral services during gynecologic visits. The authors concluded that the providers did not take the opportunity to discuss preconception care during routine gynecologic visits.
A series of studies looking at provider knowledge and practice regarding preconception and prenatal screening for cystic fibrosis (CF) was conducted during 2001–2004 using the Collaborative Ambulatory Research Network (CARN) [13]. Results showed that almost one-half of the responders did not ask nonpregnant women of childbearing age about their family history of CF, nor did they provide information on CF screening. Close to 90% of respondents did offer CF screening or inquire about a family history of CF among pregnant women. Of those Ob/Gyns who did selectively screen for CF among their pregnant patients, about 25% utilized all of the selection criteria noted in the American College of Obstetricians and Gynecologists (ACOG) recommendations.
In 2002 and 2003 the March of Dimes examined folic acid knowledge and practice patterns of health care providers in obstetric/gynecology and family/general practice settings [14]. Survey results found that, while knowledge about the need for supplementation and timing of folic acid was high, increased knowledge about unintended pregnancy rates and correct doses of folic acid for prevention of occurrence and recurrence of NTDs is needed. A little more than half of all providers knew the correct dose of folic acid (400 mcg daily) for the prevention of NTDs. This percentage dropped dramatically when providers were asked the correct dose of folic acid for a woman with a history of a previous NTD-affected pregnancy. Almost two-thirds of providers did not know the folic acid dose for recurrence prevention (4 mg daily).
In the surveys, the vast majority of providers reported always recommending folic acid supplementation for women who expressed interest in becoming pregnant, approximately 60% of respondents who provide prenatal care reported seeing less than one fifth of their prenatal patients for a preconception care visit [14]. At annual gynecologic or well-woman examinations as well as other types of patient visits, less than 60% of respondents always or usually addressed supplementation. Lack of knowledge (39%) and lack of time (30%) during a busy exam schedule were the most cited reasons providers gave for not always recommending folic acid.
In 2004 a survey was sent to Florida health care providers to establish their baseline knowledge and practice behavior regarding folic acid and NTDs [15]. At baseline, 97% of providers reported awareness of the protective nature of folic acid during preconception and early in the first trimester; however, less than half of providers reported that they discuss folic acid consumption with all women of child-bearing age. After a statewide educational program, the same providers were surveyed again to see if there had been any change in knowledge base or practice patterns. The survey done after the educational outreach efforts did demonstrate an increase in awareness and an increase in the percentage of providers who recommended peri-conceptional use of folic acid to prevent NTDs. The authors note, however, that despite these modest gains, the need for continued education and modification of provider practice patterns remains.
A recent survey by ACOG was done to assess the opinions and practices of obstetrician-gynecologists regarding preconception care, and their perception of patients’ receptiveness to preconception services [16]. The vast majority of physicians (97.3%) stated that they provide preconception care for their patients, although obstetrician-gynecologists were more likely to do so than providers who only practiced gynecology. The majority of physicians (87%) agreed with the definition of preconception care as “Specialized pre-pregnancy care that focuses on issues not typically addressed during a routine exam which are specific to ensuring an optimal pregnancy outcome.” Most of the respondents indicated that women are more likely to present for preconception care to ensure a healthy pregnancy than because of an elevated risk for a birth defect or developmental disorder. The majority of physicians agreed that preconception care is an important issue and that it has a positive effect on pregnancy outcomes, though only 21% agreed that it was a high priority in their workload. Half of the physicians said there was not enough time to provide preconception care to all women with reproductive potential, nor were there reimbursement incentives to do so.
Discussion
Preconception care- care that is initiated before pregnancy- is advocated to help women reduce their risk for adverse pregnancy outcomes and make informed decisions regarding their readiness for and timing of pregnancy [17]. Appropriate preconception care improves pregnancy outcomes and has several components. These include, but are not limited to: the systematic identification of preconceptional risks through an assessment of the woman's reproductive, family, and medical history; the family and medical history of the father; the woman's nutritional status, social concerns, and any drug or substance exposures she or the father may have (had); discussions regarding possible effects of any existing medical problems and potential interventions; screening for infectious diseases with treatment and immunization where indicated; discussions about environmental exposures, both occupational and household; a review of the circumstances of the woman's life and behavioral patterns; counseling and discussion about birth spacing including real and perceived barriers to achieving her desires; and inquiry and education regarding contraceptive use.
Traditionally, preconception care endeavors have focused on women who have a chronic medical condition, a history of a poor outcome in a previous pregnancy, or who are planning to become pregnant in the near future. For women with chronic medical conditions, the effect(s) of their illness(es) range from minimal to limited activity to hospitalization. Drawing on information for the 1996 National Health Interview Survey (NHIS), 9.9% of women ages 18–44 years had some limitations placed on their activity levels due to chronic disease [18]. There is clear evidence that the initiation of preconception counseling and care for women with some chronic health conditions, e.g., diabetes, will positively impact pregnancy outcomes [19]. For women with a history of a poor pregnancy outcome in the past, pre- or interconceptional intervention strategies have been demonstrated to be effective [20]. Limiting preconception care endeavors to women actively planning pregnancy, however, reaches a limited audience as almost 50% of pregnancies in the U.S. are unintended [21]. Comprehensive preconception care, however, encompasses much more than just these risk categories and is inclusive of all women with the potential to become pregnant.
Women of childbearing age visit their physician an average of about three times per year; these visits represent opportunities to deliver preconception care and messages related to preconception care [22]. In reviewing the literature related to the clinical practice of preconception care, it is clear that most of these opportunities are either missed or foregone. In an article focusing on preconceptional health promotion, Moos proposes 5 categorical reasons for why providers are inconsistent in applying proven prevention strategies for poor pregnancy outcomes: lack of knowledge regarding the incidence of unintended pregnancy, inadequate provider education, lack of confidence that preconceptional health counseling is valuable, a belief that women will ‘know’ to seek the care appropriate to their needs, and concerns over lack of reimbursement coverage for preconceptional visits [23].
Changing behavior patterns is no small feat, but understanding the ‘hows’ and ‘whys’ of change(s) in provider practice patterns is becoming better understood. For example, in diffusion theory, diffusion is the process through which an innovation is adopted for use or application by a community [24]. One proposed method for facilitating diffusion of innovation utilizes an expert opinion leader process whereby a designated (or identified) local physician leader communicates information about new innovation(s) or knowledge to colleagues and then follows up with them about their personal experiences with the information [25]. This approach has demonstrated effectiveness for eliciting changes in practice patterns in certain situations and settings, but it is not uniformly effective in all instances [26–29].
In his assessment of how research findings get translated into “best practices”, Green postulates that there are three areas representing barriers to the adoption of best practice: 1) accessibility gap, i.e., practitioners do not have the same resources available as the researchers; 2) credibility gap, e.g., a comparison of differences in practice settings or populations that might ‘explain’ why the research is not applicable to the general practitioner; 3) expectations gap, e.g., the practitioner views the research findings as unnecessary goals to set for their own practices [30]. These barriers are not insurmountable, and there is evidence that different approaches to changing provider behavior patterns are effective [26, 27]. In an article by Cullum, several model programs are reviewed that have demonstrated changes in the practices of health care providers related to preconception care [31].
Studies evaluating effective preconception care interventions and strategies are numerous, and continue to grow. There is also an increasing amount of information about how patients assimilate and act upon recommendations regarding preconception care. There is little data, however, that analyzes the impact and integration of preconception care innovations on daily clinical practice(s). One limitation of this literature review is that articles with secondary goals of evaluating actual clinical practices may have been overlooked. Efforts were made to include all relevant search terms but it is possible that articles written before 1985 may have been inadvertently excluded if different keywords were used at that time. Nonetheless, there is a large need for further research into the contrast between the science of preconception care and the reality of actual clinical practice.
Conclusion
Preconception care seeks to promote the health of women of childbearing age prior to conception and to improve pregnancy-related outcome. Various authors propose the integration of preconception care into routine wellness care for all women of reproductive potential [23, 32–34]. The question is how do we raise the level of importance and prioritization of preconception care during individual encounters as well as on a national health policy level? Proving the efficacy of a practice does not guarantee an actual change in practice and simply prompting providers to incorporate preconception care will not suffice. Creating the expectation that preconception care is an ongoing process during a woman's reproductive life span will require a myriad of efforts concurrently directed at providers, patients, payors, and policy makers.
In 2005, the Centers for Disease Control and Prevention sponsored a national summit regarding preconception care with a broad cross-section of stakeholders. Efforts are currently underway to develop a uniform set of national recommendations and guidelines for preconception care. Understanding how preconception care is presently incorporated and manifested in current medical practices should help in the development of these national guidelines. Knowing where, how, and why some specific preconception recommendations have been successfully adopted and translated into clinical practice, as well as barriers to implementation of other recommendations or guidelines, is vitally important in developing an overarching set of national guidelines. Ultimately, the success of these recommendations rests on their ability to influence and shape women's health policy.

preconception care
preconception counseling
preconception surveys
practice patterns
pregnancy outcomes
prepregnancy planning
prepregnancy surveys