Effects of FVB/NJ and C57Bl/6J strain backgrounds on mammary tumor phenotype in inducible nitric oxide synthase deficient mice

The ability to genetically manipulate mice has led to rapid progress in our understanding of the roles of different gene products in human disease. Transgenic mice have often been created in the FVB/NJ (FVB) strain due to its high fecundity, while gene-targeted mice have been developed in the 129/SvJ-C57Bl/6J strains due to the capacity of 129/SvJ embryonic stem cells to facilitate germline transmission. Gene-targeted mice are commonly backcrossed into the C57Bl/6J (B6) background for comparison with existing data. Genetic modifiers have been shown to modulate mammary tumor latency in mouse models of breast cancer and it is commonly known that the FVB strain is susceptible to mammary tumors while the B6 strain is more resistant. Since gene-targeted mice in the B6 background are frequently bred into the polyomavirus middle T (PyMT) mouse model of breast cancer in the FVB strain, we have sought to understand the impact of the different genetic backgrounds on the resulting phenotype. We bred mice deficient in the inducible nitric oxide synthase (iNOS) until they were congenic in the PyMT model in the FVB and B6 strains. Our results reveal that the large difference in mean tumor latencies in the two backgrounds of 53 and 92 days respectively affect the ability to discern smaller differences in latency due to the Nos2 genetic mutation. Furthermore, the longer latency in the B6 strain enables a more detailed analysis of tumor formation indicating that individual tumor development is not stoichastic, but is initiated in the #1 glands and proceeds in early and late phases. NO production affects tumors that develop early suggesting an association of iNOS-induced NO with a more aggressive tumor phenotype, consistent with human clinical data positively correlating iNOS expression with breast cancer progression. An examination of lung metastases, which are significantly reduced in PyMT/iNOS−/− mice compared with PyMT/iNOS+/+ mice only in the B6 background, is concordant with these findings. Our data suggest that PyMT in the B6 background provides a useful model for the study of inflammation-induced breast cancer.
Introduction
Transgenic mouse models that develop spontaneous mammary adenocarcinomas have proven valuable in revealing molecular mechanisms underlying tumorigenesis and metastasis (Cardiff 2003; Fargiano et al. 2003). Models target specific pathways depending on the transgene being expressed under the control of the mouse mammary tumor virus long terminal repeat (MMTV-LTR) or whey acid protein (WAP) mammary gland promoters and thereby replicate genetic defects in subsets of human tumors (Cardiff and Wellings 1999; Rosner et al. 2002). Molecules from pathways known to be important in the pathogenesis of human breast cancer such as the Wnt, Ras and ErbB2/Neu pathways have been utilized, however few spontaneous mammary tumor model systems develop metastasis, a crucial aspect of cancer progression.
As demonstrated by gene expression analysis (Ma et al. 2003), the polyomavirus middle T (PyMT) model in the FVB/N (FVB) background activates the same signaling pathways as erbB2, an oncogene amplified or overexpressed in approximately 30% of human breast cancers. The model has been particularly useful for genetic studies due to its short tumor latency and high incidence of pulmonary metastasis (Guy et al. 1992). These features have enabled global expression profiling of PyMT tumors from different genetic backgrounds to identify signatures of tumor virulence (Qiu et al. 2004) that are consistent with a gene set predictive of metastasis in human tumors (Ramaswamy et al. 2003). Hence, the model has been used effectively to dissect early and late stages of mammary tumor development (Guy et al. 1994; Lin et al. 2001; Webster et al. 1998; Williams et al. 2004). In women with breast cancers involving this pathway, the course of the disease varies widely. One powerful means to understand that variability is to identify useful animal models. The PyMT breast cancer model can be used in genetic crossing experiments to identify loci having modifier effects on either tumorigenesis or metastasis (Le Voyer et al. 2000). An understanding of the genetic polymorphisms underlying these effects is important in the study of human cancer as genetic variability affects host-tumor interactions in numerous ways and is an important factor in the response to therapy.
Widely varied tumor responses to polyoma virus infection among different inbred strains of mice have been well documented (Freund et al. 1992; Gross 1983). Much of this difference can be attributed to the major histocompatibility complex (MHC) which results in immunologically mediated resistance associated with an H-2b haplotype such as in C57Bl strains (Law et al. 1967; Ting and Law 1965). Other inbred strains, notably those with the H-2d and H-2k haplotypes have moderate and weak tumor resistance respectively (Freund et al. 1992). The FVB strain has an H-2q haplotype, which may be an additional MHC locus that confers susceptibility to polyoma virus induced tumorigenesis. Alternatively, non-MHC genes can also result in a dominant pattern of tumor susceptibility as in the case of the C3H/BiDa strain (Freund et al. 1992).
Strain variations are also relevant to the study of genetic mutations in mouse models of disease. An analysis of targeted disruptions on mammary tumorigenesis in the PyMT model requires breeding of knockout models usually developed in the 129/SvJ-C57Bl/6J background into the FVB background. It is commonly recognized that the FVB strain is susceptible while the C57Bl/6J (B6) strain is more resistant to PyMT mammary tumorigenesis and F1 crosses of these strains increase tumor latency by approximately 6 days (Lifsted et al. 1998). However, it is not clear what effects continued backcrossing has on the analysis of targeted mutations in the PyMT model.
Previously we reported that inducible nitric oxide synthase (iNOS) deficiency in the PyMT model in the B6 background resulted in an increase in tumor latency that was not observed in the FVB background (Ellies et al. 2003). Since the issue of strain background is an important one for analysis of mouse models we have made iNOS knockout mice congenic in the FVB and B6 backgrounds and carried out a comparison of tumorigenesis and metastasis in both strains.
Materials and methods
Animals
PyMT (FVB/N-Tg(MMTV-PyVT)634Mul/J) mice (Guy et al. 1992) were obtained from W. Muller (McMaster University, Ontario, Canada). We acquired PyMT mice that had been backcrossed 5 generations into the B6 background from A. Varki (UCSD, La Jolla, CA). Control (C57Bl/6J) and iNOS deficient mice (B6.129P2-Nos2tm1Lau/J; Laubach et al. 1995) were purchased from The Jackson Laboratory, Bar Harbor, ME. The PyMT B6 mice were further backcrossed until congenic in the B6 background and then bred with iNOS−/− mice. iNOS−/− mice were bred into the FVB background until congenic (>10 generations) and then crossed with PyMT FVB mice. Female mice heterozygous for the PyMT transgene and homozygous for the wild type or mutated iNOS gene were used in these studies. For clarity and brevity, we have designated FVB/N-Tg(MMTV-PyVT)634Mul/JxB6.129P2-Nos2tm1Lau/J+/+ mice PyMT/iNOS+/+ and FVB/N-Tg(MMTV-PyVT)634Mul/JxB6.129P2-Nos2tm1Lau/J−/− mice PyMT/iNOS−/−. FVB mice were palpated twice weekly from 4 weeks of age and B6 mice once weekly from 8 weeks of age to monitor mammary tumor development. Tumors were measured in 2 dimensions using calipers and tumor volume estimated using the standard calculation for a sphere 4/3 × 3.14 × a × b2 where a is the smaller diameter and b is the larger diameter. After euthanizing the mice, mammary tumors were dissected and weighed and the total tumor burden calculated (tumor weight/body weight). All studies followed the NIH guidelines for the care and treatment of experimental laboratory rodents.
Mammary gland whole mounts
Pairs of inguinal mammary glands were fixed in formalin or Carnoy’s fixative, dehydrated through a graded series of ethanol solutions and defatted in xylene. Following rehydration, the mammary epithelium was stained with carmine stain (Sigma Chemical, St Louis, MO) for 30 min. After removing excess stain by washing in water, samples were dehydrated and stored in methyl salicylate (Sigma Chemical, St Louis, MO).
Tumor microvascular density
Cryosections of OCT embedded tumor samples were cut at 7 μm and stained with anti-CD31 clone 13.3 (1:200, BD Pharmingen, San Diego, CA) as previously described (Ellies et al. 2003). Five random fields per tumor were captured on the Spot RT digital camera system (Diagnostic Instruments, Sterling Heights, MI) at 200× magnification and analyzed using the public domain NIH Image program (http://rsb.info.nih.gov/nih-image/). Microvessel areas were normalized to 1 mm2 of epithelium.
Lung metastases
Paraffin embedded lungs were serial sectioned and stained with hematoxylin and eosin. Both the numbers and areas of lung metastases were measured by light microscopy using images and software from the Spot RT digital camera system. Lung area per section was measured using a dissecting scope and metastatic tumor burden calculated (mm2 of lung metastases/cm2 of lung tissue) for each animal using data from 3 sections at least 100 μm apart. At least 13 PyMT/iNOS+/+ or PyMT/iNOS−/− mice were used in this analysis.
Statistical analysis
The percentage of mice tumor free in each group was analyzed using a Kaplan-Meier survival analysis, and the log-rank statistic was used for comparison of the curves between groups. The t-test was used for comparison of 2 groups and the analysis of variance (ANOVA) followed by the Bonferroni post-test was applied to multiple comparisons. The Mann–Whitney U test was used to compare metastastic burden in the PyMT/iNOS+/+ and PyMT/iNOS−/− groups.
Results
PyMT tumorigenesis is delayed in the C57Bl/6 strain
The PyMT model was originally developed in the FVB strain due to its high fecundity. Tumor growth occurs rapidly and metastatic tumors form in the lungs (Guy et al. 1992). To compare tumorigenesis in the FVB strain with that in the C57Bl/6 strain onto which many genetically modified strains are bred, we made PyMT mice congenic in the B6 background. Examination of tumor latency showed a dramatic difference with a mean latency of 53 days for PyMT tumor development in FVB mice and 92 days in B6 mice, a delay of approximately 6 weeks (Fig. 1A). Whole mounts of mammary glands from the two strains also showed that even at weaning, hyperplasias were more advanced in FVB than B6 PyMT mice (Fig. 1B).
Fig. 1PyMT tumorigenesis in the FVB and B6 strains. (A) The day at which the first mammary tumor was detected by palpation in each mouse was recorded and a Kaplan-Meier survival plot generated. Log rank analysis indicated a highly significant difference, P < 0.001 between the curves.  FVB mice N = 44, B6 mice N = 46. (B) Whole mounts of mammary glands from 3 week old PyMT/iNOS+/+ mice of the FVB (left panel) and B6 (right panel) strains
Effects of iNOS deficiency are more apparent in the C57Bl/6 background
To determine how this difference in tumor latency may affect the phenotype observed following crossbreeding with genetically altered mice, we bred in iNOS−/− mice congenic in the two backgrounds. As previously reported, we observed a 3–4 week delay in palpable tumors in B6 PyMT mice lacking iNOS activity (Ellies et al. 2003). However, this difference was difficult to discern in the FVB background due to the rapid tumor onset (Fig. 2A). Mammary gland whole mounts reflected the latency data with a clear difference in tumor growth with iNOS deficiency being observed in the #4 glands of B6 PyMT mice (Fig. 2A).
Fig. 2Effects of iNOS deficiency on mammary tumor development. (A) Whole mounts of PyMT/iNOS+/+ and PyMT/iNOS−/− #4 mammary glands from 11 week FVB mice or 20 week B6 mice. LN = lymph node. (B) Kaplan Meier plots of the tumor latency of PyMT/iNOS+/+ annd PyMT/iNOS−/− mice of the 2 strains. Data were analyzed by the log rank test. PyMT/iNOS+/+ FVB N = 44, PyMT/iNOS−/− FVB N = 57, PyMT/iNOS+/+ B6 N = 46, PyMT/iNOS−/− B6 N = 26
PyMT tumor growth kinetics vary in FVB/N and C57Bl/6 strains
The pattern of tumor growth differed between strains and may in part account for the greater disparity in tumor development observed in the absence of iNOS in PyMT B6 mice. Once the mean tumor volume reached 1 cm3, tumor growth proceeded exponentially in the PyMT FVB mice leading to sacrifice within 3 weeks (Fig. 3A). To achieve the same final tumor volume, the PyMT B6 mice required approximately 7 weeks and growth followed a more linear pattern, suggesting more variable rates of growth of individual tumors. Indeed, when we analyzed the tumor latency of all tumors, we were able to define two distinct phases of tumor growth in the B6 strain: early, from 12–17 weeks and late, from 19–23 weeks (Fig. 3B). The major effect of iNOS deficiency appears to be a delay in the early phase of tumor development.
Fig. 3Mammary tumor growth kinetics. (A) Mean tumor volume per mouse. FVB N = 32, B6 N = 44. Data are means ± SEM. (B) Tumor latency for individual B6 PyMT/iNOS+/+ and PyMT/iNOS−/− tumors. Data were derived from examining 460 B6 PyMT/iNOS+/+and 260 PyMT/iNOS−/−tumors
Deficiency in iNOS-modulated tumor growth is site specific
In concordance with increased tumor latency and decreased tumor growth (Ellies et al. 2003) tumor burden was significantly lower in PyMT/iNOS−/− mice (Fig. 4A). An examination of tumor burden in individual mammary glands revealed a reduction in all PyMT/iNOS−/− glands compared to controls, although the numbers were only significant in the #3 and #4 mammary glands (Fig. 4A). It is interesting to note that these are the glands with the largest fat pads and may indicate a role for the stroma in the iNOS effect. To assess a role for tumor vascularization in this effect we examined tumor microvascular density (MVD) in #1and #2–5 tumors of both genotypes. Although there was a slight reduction in the mean MVD in #1 versus #2–5 tumors in the PyMT/iNOS+/+ and PyMT/iNOS−/− genotypes, it was not statistically significant, suggesting this is not the mechanism for the difference observed in tumor growth at different locations (Table 1).
Fig. 4Distribution of B6 mammary gland tumor burden. (A) Total tumor burden was significantly lower in PyMT/iNOS−/− mice, ***P < 0.001. (B) Examination of individual tumors indicated that a significant difference in genotypes was only found in the #3 and #4 mammary glands, although in each gland the mean tumor burden was lower in PyMT/iNOS−/− mice. *P < 0.05 ANOVA followed by Bonferroni post tests. PyMT/iNOS+/+ B6 N = 24 (black bars), PyMT/iNOS−/− B6 N = 25 (grey bars)Table 1Quantitative analysis of tumor microvascular densityGenotypeLocationNMVD (μm2/mm2)PyMT/iNOS+/+#19216 ± 20PyMT/iNOS+/+#2–59176 ± 12PyMT/iNOS−/−#16201 ± 11PyMT/iNOS−/−#2–511189 ± 11Data are means ± SEM
This site-specific effect prompted us to examine tumor latency in individual glands. We had observed during palpations that the #1 mammary gland was usually the first to develop tumors, however we were surprised to find a clear difference between the latency of the #1 tumors compared with all other tumors (Fig. 5). This difference was more noticeable in the absence of iNOS. The greater difference in mean latency in the #4 glands compared with the #1 glands was consistent with the tumor burden results being more significant in the #4 glands.
Fig. 5Tumor latency of individual B6 mammary glands. Tumor latency was lower in the #1 mammary gland in PyMT/iNOS+/+ mice compared with all other mammary glands (upper panel). A similar result was observed in PyMT/iNOS+/+ mice, with an increase in disparity between the latency of the #1 gland and all other glands (middle panel). When the #1 and #4 latencies for both genotypes were overlayed, a greater difference in mean tumor latency was observed between the #4 glands when compared with the #1 glands (lower panel)
iNOS effects on tumor metastasis
The PyMT model has been useful for the study of tumor metastasis due to the high penetrance of this clinically important characteristic of tumor progression. However, we found that in the FVB strain, tumors were so aggressive that no significant difference could be observed in the metastatic burden occurring in PyMT/iNOS+/+ and PyMT/iNOS−/− mice Fig 6. Although the incidence of tumor metastasis was less in the B6 strain, a statistically significant difference between the genotypes was found. We have maintained PyMT/iNOS−/− mice for up to 32 weeks and found no increase in metastatic burden (data not shown), suggesting that the decrease in metastasis is not due simply to the delayed tumor latency in these mice.
Fig. 6Tumor metastasis. (A) HE stained paraffin embedded sections of PyMT/iNOS+/+ (left panel) and PyMT/iNOS−/− (right panel) lungs. (B) Comparison of metastatic tumor burden in FVB and B6 mice *P < 0.05 Mann-Whitney U test. PyMT/iNOS+/+ FVB N = 14, PyMT/iNOS−/− FVB N = 13, PyMT/iNOS+/+ B6 N = 17, PyMT/iNOS−/− B6 N = 21
Discussion
Mouse models of mammary tumorigenesis have provided important insights into the molecular basis of human breast cancer and are essential for gathering preclinical data regarding therapeutic drug efficacy. The PyMT model in the FVB strain has a mean tumor latency of 53 days and has been used extensively in studies of mammary tumorigenesis (Cardiff 2003; Muller and Neville 2001). The model is frequently crossed with gene-targeted strains that have been generated in the B6/129 mixed background or backcrossed onto the B6 background. We had highly variable results examining iNOS effects in a mixed B6/FVB PyMT background and sought to characterize the phenotype of this mutation in congenic FVB and B6 strains. Our study demonstrates that the genetic background of the PyMT model can affect the ability to discern differences in phenotype due to genetic alterations that require time to influence tumor progression. Differences observed in the B6 background were obscured in the FVB background due to the signficantly shorter tumor latency and more rapid tumor growth in this strain.
Further analysis of mammary tumorigenesis in the B6 background indicated that tumor formation occurred in 2 phases: early, from 12–17 weeks and late, from 19–23 weeks. It is the early phase of tumor formation that is most affected in the absence of iNOS, consistent with recent data linking iNOS expression with transcriptional upregulation of signal transducers and activators of transcription 3 (STAT3) by nuclear epidermal growth factor receptor (EGFR) (Lo et al. 2005). EGFR/ErbB1, a member of the ErbB family of receptor tyrosine kinases that signal via the same pathways as PyMT (Webster et al. 1998), is also found in the nucleus of highly proliferative cells where is functions as a transcription factor (Marti et al. 1991; Wells and Marti 2002). Structural and functional characterization of the iNOS promoter has identified binding elements for both EGFR and STAT3 (Lo et al. 2005). Immunohistochemical studies of breast carcinomas show that nuclear EGFR positively correlates with iNOS and survival analysis demonstrates that high levels of iNOS are a prognostic indicator of poor survival (Lo et al. 2005). Thus the B6 PyMT mice in the presence or absence of iNOS deficiency are likely to provide an important model for further analysis of these novel molecular interactions.
Mammary tumor latency for each of the ten murine mammary glands has generally been assumed to be stoichiometric. However our data demonstrate that the #1 mammary glands have tumor initiating properties that are distinct from the other 4 pairs of mammary glands. The #1 mammary glands have the smallest fat pads and are located in the head and neck region, which is highly vascular, so we hypothesized that an increase in vascular density in the #1 tumors could be responsible for this difference. An examination of the microvacular density (MVD) of #1 tumors versus all others showed a trend in which #1 tumors had a higher mean MVD than #2–5 tumors as determined by quantitative analysis of CD31 staining. However, these values were not statistically significant, suggesting a different mechanism is responsible for the observed site-specific differences in tumor burden and tumor latency. Other factors in the stromal microevironment may also play a role in mediating this effect. The release of epidermal growth factor (EGF) from the submandibular salivary gland has been shown to promote mouse mammary tumorigenesis in virus mediated and chemical carcinogen induced models (Kurachi et al. 1985; Molinolo et al. 1998). The proximity of the #1 glands to the submandibular gland make EGF induced proliferation a plausible explanation, however further investigations will be required to determine whether the release of this growth factor primarily affects the #1 glands.
Another possible explanation for this intriguing difference is that the #1 mammary gland is developmentally distinct from the other mammary glands. The #1 gland is not the first to develop since placode 3 develops first at E11.0–11.5 on the ectodermal streak between the forelimb and hindlimb (Eblaghie et al. 2004; Mailleux et al. 2002). Placodes 1 and 5 arise from the axillary and inguinal streaks respectively and placodes 4 and 2 form where the mammary line abuts these streaks (Veltmaat et al. 2004). Recent studies indicate that signaling molecules essential for mammogenesis are placode dependent. Placodes 2 and 3 do not form in the absence of Lef1 mediated canonical Wnt signaling (Boras-Granic et al. 2006) and placodes 1, 2, 3 and 5 require Fgf10 signaling from somites underlying the ectoderm (Mailleux et al. 2002; Veltmaat et al. 2006). It is clear there are developmental differences between mammary glands and it is possible that early developmental factors result in an increased propensity for malignant transformation in the #1 mammary glands, though the identity of these factors remains to be determined.
Since metastasis is an aspect of tumor progression that is critical to patient survival, we examined the effect of iNOS on tumor metastasis in both the FVB and B6 backgrounds. No significant difference in metastatic burden was found in PyMT/iNOS−/− mice in the FVB background, however the tumor burden was reduced in lungs of PyMT/iNOS−/− mice in the B6 background compared with controls. Furthermore, the reduction in metastatic burden was not due simply to the delayed tumor latency as we maintained PyMT/iNOS−/− mice longer to compensate for the difference and found that this did not alter our previous findings (Ellies et al. 2003).
Human clinical studies have associated increased iNOS expression with poor prognosis in breast cancer patients (Loibl et al. 2005), and the use of non-steroidal anti-inflammatory drugs (NSAIDs) with a 22–39% reduction in the risk of breast cancer (Harris et al. 2005; Khuder and Mutgi 2001). To increase our understanding of the molecular pathways involved in these effects, it is essential to have animal models that recapitulate the clinical findings. We have shown that in the PyMT B6 background loss of iNOS, an important inflammatory mediator, results in increased tumor latency and decreased tumor metastasis that is not due simply to the difference in latency. This model will be useful in determining molecular pathways underlying the role of iNOS in breast cancer.

fvb/nj
c57bl/6j
strain
mouse
breast
cancer
inos
polyomavirus middle t antigen


Moebius-Poland syndrome and hypogonadotropic hypogonadism

Case report
A male infant was born at 39 weeks of gestation, with birth weight (3.1 kg) and body length (49 cm) (both 50th percentile). The occipitofrontal circumference was 33 cm (25th percentile). The newborn’s face was inexpressive due to complete facial diplegia, bilateral ophthalmoplegia with impairment of the vertical gaze of the right eye, palpebral ptosis, a carp-shaped mouth, and a high-arched palate. Muscle and skeletal abnormalities were also apparent, including the absence of the pectoralis major and trapezius muscles, as well as left cubitus valgus; left hand was hypoplastic and showed 5th finger clinodactyly. These features prompted the diagnosis of Moebius-Poland syndrome. No previous cases were known in the patient’s family. Clinical examination revealed no other dysmorphic features except for micropenis. The karyotype was normal (46 XY). During follow-up, a mild psychomotor delay was confirmed by Denver Developmental Screening Test-II. Cortical-subcortical atrophy with no other structural abnormalities was observed in magnetic resonance imaging (MRI) of the brain. Delayed puberty was recorded when the patient was 15 years of age (Tanner stage I was recorded after physical examination). The testicular volume (measured using a Prader orchidometer) was 2 ml. Anosmia-tested by a standard olfactory test (CCCRC)- was not present. An endocrinological investigation revealed low baseline serum FSH (0.5 mU/ml), LH (0.1 mU/ml) and testosterone (2 ng/dl) levels. To confirm the suspected hypogonadotropic hypogonadism, a gonadotrophin releasing hormone stimulation test was performed and a very subnormal response obtained (after 180 min and 24 h FSH concentrations were 3.5 mU/ml and 0.7 mU/ml, respectively, and LH concentrations 0.7 mU/ml and 0.2 mU/ml, respectively). GH, TSH, and ACTH levels were normal. A further MRI scan of the brain showed no pathological lesions in the hypothalamus or pituitary gland. With a diagnosis of hypogonadotropic hypogonadism established, the child was treated with gonadotrophic hormone followed by depot preparations of testosterone. The patient eventually reached a normal weight and height and attained full sexual development (final testicular volume 6–8 ml).
Discussion
The usual diagnostic criteria for Moebius syndrome include congenital facial palsy with impaired ocular abduction [1, 2, 4, 6]. Nevertheless, in a thorough review, Verzijl et al. reported that abducens nerve palsy is not always present [6]. Vertical gaze palsy was recorded in the right eye of the present patient, suggesting involvement of the 3rd cranial nerve (Fig. 1). MRI scans often demonstrate anomalies of the posterior fossa in patients with Moebius syndrome, and it has been proposed that the latter be understood as a complex congenital anomaly affecting the rhombencephalon [6]. The present patient, however, showed no such anomalies. In addition to the main signs, more than half of all patients with Moebius syndrome have other anomalies, usually limb defects [1, 3]. The specific combination of both Moebius syndrome and Poland syndrome—a rare congenital anomaly characterised by pectoralis muscle defect and ipsilateral hand abnormalities—has been reported before, and is then referred to as Moebius-Poland syndrome [5]. Some authors suggest it may represent a single developmental condition that may be due to the variable expression of a single gene or involve contiguous genes [6].
Fig. 1Patient photo
Hypogonadotropic hypogonadism is unusual in Moebius syndrome [1–3]. The present patient is only the 7th case reported to date, and the first with Moebius-Poland syndrome.

hypogonadotropic
hypogonadism
moebius syndrome
poland syndrome


Impact of a programme of mass mammography screening for breast cancer on socio-economic variation in survival: a population-based study

Background After a systematic mass mammography breast cancer screening programme was implemented between 1991 and 1996 (attendance 80%), we evaluated its impact on survival according to socioeconomic status (SES).
Introduction
Mammography screening aims at early detection of breast cancer so that adequate treatment will eventually lower breast cancer mortality. In a mass screening programme, it is therefore especially important to reach women who have the highest chance of being diagnosed with advanced stage or have the lowest survival rates.
Women from lower socio-economic strata are less likely to attend population screening programmes [1–4] and are also more likely to present with unfavourable stage at diagnosis, [1, 5, 6] although not all studies confirm this [7–9]. Lower breast cancer survival rates among the disadvantaged are usually attributed to advanced stage at presentation, but also to suboptimal access to adequate treatment. A recent population-based study in Switzerland found social class to be an independent prognostic factor [10].
The mass breast cancer screening programme was introduced in 1991 for women of 50–69 years and became fully implemented in 1996 in the south of the Netherlands covered by the population-based Eindhoven Cancer Registry, with a continuous high participation rate. Based on previous work [11] and a new postcode-based indicator of socio-economic status (SES) introduced by Statistics Netherlands [12] we were able to investigate survival according to SES for a sufficient period of time after introduction.
We studied whether survival according to SES was affected differentially by the implementation of the screening programme.
Methods
The Eindhoven Cancer Registry records data on all patients newly diagnosed with cancer in the south–eastern part of the Netherlands, an area with now 2.4 million inhabitants (about 15% of the Dutch population) and only general hospitals. Trained registry personnel actively collect data on diagnosis, staging, and treatment from the medical records after notification by pathologists and medical registration offices.
In the area of the Eindhoven Cancer Registry, a biennial breast cancer screening programme for women aged 50–69 years was started in 1991 and fully implemented in 1996. The attendance rate was more than 80% [13].
For our analyses we included all patients age 50–69 years diagnosed in 1983–2002 with invasive breast cancer in the eastern part of the registration area (about 1 million inhabitants). This population has been followed-up for vital status up to 1-1-2005. Information on the vital status of all patients was obtained initially from the municipal registries and since 1998 from the Central Bureau for Genealogy. These registers provide virtually complete coverage of all deceased Dutch citizens.
An indicator of socioeconomic status was developed by Statistics Netherlands [12] being based on individual fiscal data from the year 2000 on the economic value of the home and household income and provided at aggregated level for each postal code (average of 17 households). Socioeconomic status was categorized according to quintiles ranging from 1 (low) to 5 (high), with a separate class for postal codes with a care-providing institution (such as a nursing home). This measure is assumed to be valid ten years before and after the basic year (2000), so for patients diagnosed before 1990 we used a measure which was also based on postal code of residence, but socio-economic status (five categories) was based on data from a marketing agency (self-reported occupation and education define 45 social classes, collapsed into a 5-level indicator based on average number of years of education), as described before [11]. We also used both SES indicators for the whole study period (1983–2002) to make sure any effect of diagnostic period was not attributable to the indicator we used.
We calculated distribution of age and stage of disease according to period of diagnosis.
Stage was categorized according to the TNM classification [14]. Patients with either positive lymph nodes or metastases were considered to have advanced disease.
Chi-square test was performed of changes in the distribution across the three diagnostic periods. T-tests were performed of differences between two groups.
Crude survival analyses were performed. The log-rank test was used to evaluate significant differences between survival curves in univariate analyses. We used Cox regression models to compute multivariate rates. The proportional hazard assumption of the predictor was evaluated by applying Kaplan–Meier Curves. The predictor satisfied the assumption of proportionality as the graphs of the survival function versus the survival time resulted in graphs with parallel curves as did the graphs of the log(−log(survival)) versus log of survival time. The independent prognostic effect of SES was investigated, adjusting for age and stage of disease, and stratified according to period of diagnosis (1983–1990, 1991–1996, 1997–2002). We also calculated the age and stage-adjusted effect of period of diagnosis stratified according to SES.
Results
Median age was similar for all three periods of diagnosis (59, 60, and 59 years, respectively).
Patients diagnosed between 1997 and 2002 had a significantly more favourable stage at diagnosis than patients diagnosed in earlier periods (P < 0.0001): the proportion diagnosed with stage I (tumour smaller than 2 cm, no axillary lymph nodes involved) increased from 30% in 1983–1990 to 41% in 1991–1996 and 45% in more recent years (Table 1). The proportion with advanced disease, i.e. stage III or IV, was significantly lower in the most recent period (9.7%) compared to 1991–1996 (14%) and 1983–1989 (26%, P < 0.0001). Treatment varied over time, with a large proportion receiving systemic therapy in recent years (50%, vs. 36% and 29%).
Table 1Characteristics of all women age 50–69 years diagnosed with invasive breast cancer between 1983–2002 in Southeastern Netherlands1983–19901991–19961997–2002Totaln%n%n%n%TNMI4653064241838451,94539II6384266543805442,10843III27818152101156.254511IV1157.5684.4653.52485.0unknown382.5312.0241.3931.9Treatment*S alone27118341222491386117S + RT7665062840658362,05242S + RT + ST3052042227673361,40028S + ST10871238231134629.4ST alone261.7231.5231.3721.5Other583.8211.4130.7921.9Socio-economic status1 (low)336222851826214883182325213192034219986203308202791835519942194154102741835819786165 (high)3022031520414221,03121institution#00.0231.5331.8561.1unknown1097.1634.0834.52555.2Total1,5341,5581,8474,939100* S = Surgery, RT = Radiotherapy, ST = Systemic therapy#institution = care-providing institution such as a nursing home
The proportion of patients from the lowest socio-economic class decreased from 22% in 1983–1990 to 18% in 1991–1996 and 14% in 1997–2002 (P < 0.0001), whereas the proportion from in the higher social classes increased.
Stage distribution improved significantly over time for each social class (P < 0.01). It was similar for all SES groups in 1983–1990 (P = 0.7, Fig. 1), although the proportion with stage IV was somewhat lower in the highest classes. The stage distribution was marginally more favourable for high SES compared to the lowest SES group in both 1991–1996 and 1997–2002 (P = 0.06 both periods), although the overall effect of SES on stage was not significant in the last period of time (P = 0.4).
Fig. 1Stage distribution according to socio-economic status and period of diagnosis of patients age 50–69 years with invasive breast cancer in Southeastern Netherlands
Survival improved for all socio-economic strata over time (Fig. 2). Survival rates did not differ among patients from each of the socio-economic classes diagnosed 1983–1990 in the period (P = 0.9), 5-year survival rates being 70%, 70%, 70%, 68% and 69% for patients from the lowest to the highest social class, respectively. For patients diagnosed in 1991–1996, survival of patients with a high SES was better than that of all other socio-economic strata (P = 0.01), 5-year survival rates being 76%, 76%, 80%, 78%, and 87%, respectively. For patients diagnosed in 1997–2002 an increasing gradient in survival was observed (P = 0.002) ranging from the lowest rates for the lowest SES group to the highest for the higher classes (80%, 84%, 83%, 85%, and 89%, respectively).
Fig. 2Trend in survival according to socio-economic status for all women age 50–69 years diagnosed with invasive breast cancer in Southeastern Netherlands
Multivariate analysis (Table 2) showed that patients diagnosed between 1991 and 1996 from the lower social classes had a 29% higher risk of death compared to the highest socio-economic group, after adjusting for age and stage at diagnosis (HR for the lowest versus the highest SES group: 1.29, 95%CI: 1.0–1.7). The risk of death for low SES patients diagnosed since 1997 was twice as high as that for the highest SES group (HR: 2.01, 95%CI: 1.3–3.0). The overall effect of socio-economic status was significant in the last period (P = 0.02). Additional adjustment for treatment did not change risk estimates more than 5% (data not shown).
Table 2Multivariate regression analysis of survival of breast cancer patients age 50-69 years according to period of diagnosis, Southeastern Netherlands1983–19901991–19961997–2002HR*a95% CIHR*a95% CIHR*a95% CIAge (continuous)1.031.0–1.01.041.0–1.11.011.0–1.0Socio-economic status1 (low)1.010.8–1.21.291.0–1.72.011.3–3.021.030.8–1.31.281.0–1.71.541.0–2.330.950.8–1.21.180.9–1.61.531.0–2.340.990.8–1.31.391.0–1.81.330.9–2.05 (high)b1.001.001.00Χ2 trend0.58 (P = 0.97)5.9 (P = 0.21)11.4 (P = 0.02)TNM stageIb1.001.001.00II1.751.5–2.12.331.9–2.92.001.4–2.8III3.112.5–3.84.673.5–6.25.393.6–8.1IV9.857.6–1316.012–2216.511–24unknown2.251.4–3.52.561.4–4.64.812.3–10* HR = Hazard Ratio, CI = Confidence IntervalaAdjusted for all variables listedbReference
Age and stage-adjusted survival improved over time for all socioeconomic strata (Table 3), the largest improvements were found for the highest social classes.
Table 3Multivariate regression analysis of survival according to socio-economic status (SES) of breast cancer patients age 50–69 years in Southeastern NetherlandsSES1 (low)2345 (high)HR*a95% CIHR*a95% CIHR*a95% CIHR*a95% CIHR*a95% CIPeriod of diagnosis1983–1990b1.001.001.001.001.001991–19960.870.7–1.10.720.6–0.90.800.6–1.00.840.6–1.10.600.5–0.81997–20020.770.6–1.10.490.4–0.70.610.4–0.80.490.3–0.70.360.2–0.5X2 trend3.21 (P = 0.21)20.9 (P < 0.001)9.2 (P = 0.01)14.1 (P < 0.001)35.6 (P < 0.001)HR = Hazard Ratio, CI = Confidence IntervalaAdjusted for age at diagnosis and stage of diseasebReference
Discussion
We found that the proportion of breast cancer patients with a low SES has decreased since the introduction of a mass biennial mammography screening programme with high response rates. Although stage distribution improved for all socio-economic groups, the proportion with advanced disease decreased the most in the highest socio-economic group. In the 1980s survival was similar for all socio-economic groups, but since the introduction of screening the survival of women with a high SES has improved more than that for low socio-economic classes, also after adjustment for age and stage.
We used an indicator of socio-economic status based on the postal code of a residential area. This aggregate covers a relatively small geographical area, and thus represents a reliable approximation of individual socio-economic status. Furthermore, routinely collected income tax data (no questionnaires or interviews) have been found to provide reliable estimates of household income. Previous studies have proven that socio-economic differences based on neighbourhood data tend to reflect socio-economic differences well at the individual level [15–17]. Furthermore, this objective measure of SES is also applicable for older women (born before 1955), whose occupation or education does not always properly reflect their social class [18]. We also repeated the analyses comparing both SES indicators if they were applied for the whole study period (1983–2002) to ensure any that effect of diagnostic period was not attributable to the indicator we used, and it was not.
The lower proportion of patients with a low SES since the introduction of screening is not likely to reflect the higher attendance rate of women from a higher social class because of the very high participation rate, although this is not known according to social class. Studies from other countries have shown that SES does play a role in participation in the screening programme, [2] sometimes [3] but not always [4] due to the costs of a screening mammogram. However, the costs for the mass screening programme in the Netherlands are completely covered by public funds. Furthermore, the mean attendance rate in the Netherlands has always been rather high (about 80%), [13] and in our study area even higher than the national mean (85% in 2005) [19].
Foreign-born women are more likely to be non-attenders in the Netherlands, [20] as well as in Sweden, [21] Australia, [3] and the US, [22] for a variety of reasons. However the incidence of breast cancer among these groups of migrants is relatively low in the Netherlands and the stage distribution is comparable to that of women born in the Netherlands [20]. So this is unlikely to have affected survival rates in our study.
A lower attendance rate of low social classes will result in more advanced disease stages at presentation [23]. Before the start of the mammography screening programme, we found that the stage distribution for breast cancers diagnosed in 1980–1989 was slightly more favourable for the highest socio-economic group [24]. We have now shown that this was also true after the introduction of screening, although the differences were small. In fact, we found that, although stage distribution became more favourable for all socio-economic groups, the proportion with advanced disease decreased less in the lower socio-economic group. This differential stage distribution was also described in a recent Danish study, [6] although our differences were smaller.
The variation in survival according to SES may also be related to differences in treatment, which depends on the disease stage and varies over time. The use of surgery and radiotherapy was similar across SES groups. However, we found that the administration of adjuvant chemotherapy varied across the social strata among stage II patients (8% of the lowest SES group versus 17% of the highest SES group, P < 0.001). Patients with a higher SES seem to have benefited more from the general trend towards more adjuvant chemotherapy independent of the disease stage. This may explain, at least in part, the diverging trend in survival rates.
Another explanation for differential survival could be socio-economic variations in lifestyle. Smoking has become relatively more prevalent among low SES groups [25, 26]. This may have had an adverse effect on survival due to a poor general health while undergoing breast cancer treatment or to smoking related diseases (such as chronic obstructive pulmonary diseases (COPD) or cardiovascular disease).
Also related to an unhealthy lifestyle is obesity, which has become an increasingly important problem in the last decade, [27, 28] especially among women from the lower social classes [29].
Serious concomitant diseases besides breast cancer also affect survival rates, [30] which may explain differences in survival if comorbidity occurs more frequently in low SES groups.
Since the Eindhoven Cancer Registry has recorded comorbidity for all newly diagnosed patients since 1993, we checked whether the prevalence varied across socioeconomic strata. Indeed, the proportion of patients with comorbidity was higher among those with a lower SES (70% of patients in the lowest SES group had one or more concomitant conditions compared to 60% of the high SES group). In particular, the prevalence of diabetes and cardiovascular disease was highest in the low SES groups (diabetes in 10% with low SES and 4% with high SES, cardiovascular disease 7% and 4%, respectively).
Several studies have reported increased survival rates after the introduction of breast cancer screening 31-35. As far as we know, no studies describe a differential effect of the introduction of screening on survival rates for socio-economic strata. However, socioeconomic inequalities in mortality have been widening in recent decades in western European countries [36]. In fact, socio-economic differences in breast cancer mortality increased between 1983 and 1993 among women in Finland and Italy (Turin), but remained stable in Denmark and decreased somewhat in Norway where a mass screening programme was only introduced later [36, 37].
In conclusion, despite a very high participation rate women from lower socio-economic strata clearly benefited less from the introduction of the breast cancer screening programme than those with a lower SES, probably due to a higher prevalence of comorbidity and suboptimal treatment (for both the cancer and the concomitant disease).

breast cancer
survival
mass screening
socio-economic status


Coumarin anticoagulants and co-trimoxazole: avoid the combination rather than manage the interaction

Objective The objective of our study was to examine the management of the interaction between acenocoumarol or phenprocoumon and several antibiotics by anticoagulation clinics and to compare the consequences of this interaction on users of co-trimoxazole with those for users of other antibiotics.
Introduction
Coumarin-type anticoagulants have a narrow therapeutic range. One important aspect of their safety is their sensitivity to drug interactions, many of which have been described [9, 11].
There are several reasons why antibiotic use can be considered to be indicative of a change in anticoagulation status in users of coumarin-type anticoagulants. When the antibiotic is used for febrile illness, it may be associated with overanticoagulation [5, 12]. In two studies on the interaction between coumarin anticoagulants and antibiotics, the risk of severe overanticoagulation, defined as an International Normalised Ratio (INR) ≥6.0, was increased more in users of sulfamethoxazole-trimethoprim (co-trimoxazole) than in users of other antibiotics [12, 21]. Sulfamethoxazole is a strong inhibitor of CYP2C9 [24], the main liver enzyme involved in the metabolism of warfarin [13], acenocoumarol [19] and probably phenprocoumon [20], which could explain this stronger association with overanticoagulation. Current clinical guidelines in The Netherlands for the management of coumarin drug interactions advise healthcare givers to avoid prescribing the concurrent use of co-trimoxazole and coumarins [1]. Nevertheless, in daily practice co-trimoxazole is frequently prescribed to users of coumarins, since physicians in anticoagulation clinics assume that an interaction with co-trimoxazole can be managed in a manner similar to those used to manage interactions that arise with the concurrent use of coumarins with other antibiotics. An anticoagulation clinic will initiate one of the following procedures once it has been notified of the initiation of the use of an antibiotic: (1) measurement of the INR during the antibiotic course and adjustment of the coumarin dose depending on the INR value (a reactive dose-adjustment); (2) a preventive (coumarin) dose reduction (PDR) preceding an INR measurement during or after the antibiotic course, assuming that use of an antibiotic or the intercurrent infection itself increases the risk of overanticoagulation. The PDR approach seems even more relevant to co-trimoxazole than to other antibiotics because the CYP2C9-inhibiting effect of the former might increase the risk of overanticoagulation more than the infectious state alone. However, PDR could also lead to temporary undertreatment, and evidence for the effectiveness of this approach is currently lacking. There are no official guidelines for such dose adjustments, and the application of PDR strongly depends on the personal view of the responsible physician.
The aim of the present study was to examine the management of the interaction between coumarin anticoagulants and antibiotics by anticoagulation clinics and its consequences for users of co-trimoxazole and other antibiotics. To this end, we conducted a prospective follow-up study at four anticoagulation clinics in The Netherlands.
Materials and methods
Study design
This was a follow-up study conducted at four anticoagulation clinics in The Netherlands. We included patients who were stabilised on one of the coumarin anticoagulants acenocoumarol or phenprocoumon and who had started using one of the following antibiotics between January 2001 and October 2003: co-trimoxazole, amoxicillin, amoxicillin-clavulanic acid, clarithromycin, doxycyclin, nitrofurantoin, norfloxacin or trimethoprim. In addition to co-trimoxazole, we chose the other antibiotics based on their use for the same kind of infections, mainly those of the urinary and respiratory tract.
The subjects included in our study were prospectively followed during the antibiotic course until the last INR measurement, which occurred within 6 weeks following the starting date of the antibiotic (follow-up time). We did not intervene in the daily routine of the participating anticoagulation clinics and, in particular, we made no agreements on checking the INR of patients during the antibiotic course, on making additional INR measurements, on the time intervals between INR measurements after the antibiotic courses or on dose adjustments when antibiotics were prescribed. To assess the consequences of interaction management reliably and to avoid confounding by an unstable anticoagulation status preceding the antibiotic course, we only included stabilised patients in our study. Criteria for the assessment of stability were: (1) use of the coumarin anticoagulant for at least 50 days before initiation of the antibiotic; (2) availability of at least four INR measurements before the initiation of the antibiotic; (3) the last two INR measurements before initiation of the antibiotic were within the therapeutic range; (4) a maxim of one out of the last four INR measurements or a maxim of 30% of the INR measurements during the 50 days immediately preceding initiation of the antibiotic were outside of the therapeutic range, with no INR being above 5.5. Similar criteria for stability have been used in other studies [12, 21].
We excluded subjects from our analyses in whom the INR was not measured during the course of the antibiotic and who used the antibiotic for a period shorter than 3 days and longer than 14 days. If the INR was not measured during the course, an interaction effect of the antibiotic could be missed. Antibiotics used for less than 3 days or more than 14 days are usually prescribed for prophylaxis not for acute infections.
All patients were informed of the aims of the study and were asked for their written consent to participate in the study.
Setting and attitudes of anticoagulation clinics on antibiotic use
All anticoagulation clinics in The Netherlands monitor the INR in outpatients at a frequency varying from a few days to maximally 6 weeks. The two target therapeutic ranges are the normal therapeutic range (INR: 2.0–3.5) and the high therapeutic range (INR: 2.5–4.0).
The initiation of the use of an antibiotic is usually reported to the anticoagulation clinics by the patients, their pharmacists and/or the prescribing physicians.
The four anticoagulation clinics participating in this study had different attitudes on the management of the interaction between coumarins and antibiotics. The approach of three of the anticoagulation clinics was to decrease the coumarin dose preventively if co-trimoxazole was prescribed; one of the anticoagulation clinics applied a PDR of 20–25% in the case of co-trimoxazole use. If one of the other antibiotics examined in this study was prescribed, the application of a PDR would depend on the seriousness of the disease and on the occurrence of fever. The fourth anticoagulation clinic had no established protocols for dose reduction but indicated that it would monitor the INR of every user of co-trimoxazole within 3–5 days after initiation of the course.
Data collection
We collected relevant data on the participating patients and recorded these in a database: sex and age of patient; dosage and indication of the coumarin; prescribed antibiotics (indication, dosage and duration of use); results of INR measurements before, during and after the antibiotic course; co-medication; relevant co-morbidities (malignancies, thyroid diseases, heart failure). These data were retrieved from the medical files of the anticoagulation clinics. Patients were asked to indicate on a questionnaire for which infection the antibiotic was prescribed and whether they had suffered from fever during the antibiotic course. We recorded this as fever yes/no in our database. If the coumarin-dose was reduced as soon as the antibiotic was started in the absence of an actual INR, we recorded this as a preventive dose-reduction and calculated the percentage of the dose reduction from the data on dosage in the file of the anticoagulation clinic.
In order to assess the anticoagulation status shortly after the antibiotic course, we recorded the time spent within, above and under the therapeutic range from the starting date of the antibiotic until the last INR measurement within 6 weeks following the starting date of the antibiotic. Six weeks is the maximal period between two INR measurements if a patient is well stabilised. Furthermore, after a longer follow-up period, differences between patients could be more attributable to other factors than to the infection or antibiotic use. If after the first INR during the antibiotic course no second INR measurement was available within the 6-week period after the starting date of the antibiotic, we recorded no follow-up time and no time spent within, above or under the therapeutic range.
Outcomes
The end points of our study were chosen to assess the effectiveness of the management of the interaction between coumarin anticoagulants and co-trimoxazole and other antibiotics.
We examined the following parameters in users of co-trimoxazole with and without PDR as well as in users of other antibiotics with and without PDR:
occurrence of moderate overanticoagulation (INR >4.5) and severe overanticoagulation (INR >6.0);time spent within, above and under the therapeutic INR range from the starting date of the antibiotic until the last INR measurement within 6 weeks following the starting date of the antibiotic.
Calculations and statistical analysis
We assessed the effects of the PDR within the group of users of co-trimoxazole and within the group of users of other antibiotics by comparing the occurrence of overanticoagulation in patients for whom a PDR had been applied with the occurrence of overanticoagulation in patients for whom PDR had not been applied (logistic regression models). We also compared the occurrence of overanticoagulation and time spent within, under and above the therapeutic range of co-trimoxazole users with users of other antibiotics (reference group). These comparisons were made for patients with PDR and for patients without PDR. Finally, we compared the time spent within, under and above the therapeutic range in patients for whom a PDR had been applied with those for whom a PDR had not been applied (reference) within the groups of co-trimoxazole users and users of other antibiotics (linear regression models). In all models we adjusted for the potential confounding covariates sex, age, target therapeutic range and fever, as indicated by the patient. Covariates were added to the statistical models one at a time. We adjusted for a covariate if it changed the point estimation of the outcome of interest by 5% or more upon inclusion in the model.
Time spent within, above and under the therapeutic INR range was calculated by the step-up method described by Rosendaal et al. [15].
Although all patients were stable when they were included in our study, we re-analysed our statistically significant outcomes after excluding patients in whom destabilisation could be due to factors other than those of infection and/or fever (presence of thyroid disease, malignancy or use of other enzyme-inhibiting or-inducing drugs).
All statistical analyses were performed with the statistical software package SPSS ver. 12 (SPSS, Chicago, Ill.).
Results
A total of 424 patients who met the inclusion criteria gave their informed consent to participate in our study. Of these patients, 81 did not have assesment of the INR during the antibiotic course, 14 used the antibiotic for less than 3 days, and 3 used the antibiotic for more than 14 days.
A PDR was applied more frequently for users of co-trimoxazole (28/43; 65%) than for users of other antibiotics (60/283; 21.2%) (Table 1).
Table 1Characteristics of patients (n = 326) using antibiotics, treated by four anticoagulation clinicsCharacteristicCo-trimoxazole (n = 43)Other antibioticsa (n = 283)PDR+b (n = 28)PDR-c (n = 15)PDR+b (n = 60)PDR-c (n = 223)Men, no. (%)22 (78.6)10 (66.7)30 (50.0)114 (51.1)Age in years, mean (SD)75.4 (10.9)75.1 (8.2)72.6 (10.9)71.4 (11.2)Users of acenocoumarol, no. (%)24 (85.7)10 (66.7)52 (86.7)169 (75.8)Follow-up time, mean (SD) 33.2 (5.6)28.9 (8.0)30.4 (7.2)30.2 (7.2)Fever, no. (%)18 (64.3)6 (40.0)27 (45.0)120 (53.8)Normal target therapeutic range, no. (%)d19 (67.9)5 (33.3)30 (50.0)112 (50.2)Respiratory infections, no. (%)8 (28.6)3 (20.0)33 (55.0)116 (52.0)Urinary tract infections, no. (%)13 (46.4)8 (53.3)11 (18.3)53 (23.8)Malignancies, no. (%)1 (3.6)3 (20.0)3 (5.0)9 (4.0)Thyroid diseases, no. (%)001 (1.7)11 (4.9)Users of inhibiting drugs, no. (%)01 (6.7)5 (8.3)17 (7.6)Users of inducing drugs, no. (%)1 (3.6)01 (1.7)4 (1.8)INR measurements, mean no. (SD)3.5 (0.9)3.9 (1.5)3.5 (1.1)3.1 (1.1)Acenocoumarol, mean dose, mg/day (SD)2.42 (1.26)2.41 (1.41)2.61 (1.06)2.60 (1.12)Percentage PDR applied, mean (SD) in acenocoumarol users15.0 (7.6)10.3 (11.1)Phenprocoumon, mean dose, mg/day (SD)2.81 (0.86)2.53 (1.02)2.99 (1.31)2.36 (1.00)Percentage PDR applied, mean (SD) in phenprocoumon users17.9 (15.8)11.4 (7.0)Percentage PDR applied, all coumarins, mean (SD)15.4 (8.8)10.5 (10.6)aOther antibiotics: Trimethoprim (n = 3), doxycyclin (n = 104), amoxicillin (n = 77), amoxicillin-clavulanic acid (n = 36), clarithromycin (n = 14), norfloxacin (n = 33), nitrofurantoin (n  =  16)bPDR+, Preventive dose reduction appliedcPDR-, Preventive dose reduction not applieddNormal target therapeutic range: INR 2.0–3.5
The PDR applied was significantly greater in users of co-trimoxazole than in users of other antibiotics (15.0 and 10.3%, respectively; P value for difference: 0.036; two-sided t-test). The number of INR measurements during follow-up was significantly higher in both users of co-trimoxazole (PDR applied and PDR not applied) and users of other antibiotics (PDR applied) than in users of other antibiotics in whom a PDR was not applied. (P values of 0.028, 0.006 and 0.007, respectively; two-sided t-test). Mean daily dosages for acenocoumarol were lower in users of co-trimoxazole than in users of other antibiotics, but this difference was not statistically significant and even smaller (0.14 mg) after adjustment for differences in age (Table 1).
In co-trimoxazole users, the PDR protected strongly against both moderate and severe overanticoagulation [adjusted odds ratio (OR): 0.06, 95% confidence interval (CI): 0.01–0.51 for INR >4.5; adjusted OR: 0.09, 95% CI: 0.01–0.92 for INR >6]. For other antibiotics, the effect of the PDR on overanticoagulation was not as strong and not statistically significant (Tables 2 and 3).
Table 2Occurrence of overanticoagulation and time spent within, above and under the therapeutic range by patients using co-trimoxazole and other antibioticsaOutcomeCo-trimoxazoleOther antibioticsPDR+b (n = 28)PDR-b (n = 15)PDR+b (n = 60)PDR-b (n = 223)INR >4.5, no. (%)3 (10.7)25 (89.3)9 (15.0)45 (20.2)dINR > 6.0, no. (%)1 (3.6)4 (26.7)5 (8.3)14 (6.3)eTime within therapeutic range, mean % (95%CI)c71.1 (60.4–81.8)51.8 (34.6–69.0)76.2 (69.5–82.9)75.7 (72.2–79.3)fTime above therapeutic range, mean % (95%CI)15.0 (5.7–24.3)20.3 (10.7–29.8)12.3 (6.8–17.7)18.9 (15.5–22.2)gTime under therapeutic range, mean % (95%CI)14.0 (5.6–22.2)27.9 (7.7–48.1)11.5 (6.9–16.1)5.4 (3.7–7.2)ha Calculated for the time from the date the antibiotic was first taken until the last INR measurement within 6 weeks following the starting date of the antibiotic. Time within, above and under therapeutic range was calculated for antibiotic users in whom at least one INR measurement had been performed within 6 weeks after the INR measurement during the antibiotic course. This resulted in the exclusion from the analysis of the following number of subjects: co-trimoxazole (1 1); other antibiotics 14 (1 13). The numbers in parenthesis indicate the number of subjects with a PDR and those without a PDR, respectively.bPDR+, Preventive dose reduction applied; PDR-, preventive dose reduction not applied.c95% CI, 95% Confidence interval for the reported mean valuedRange: 7.1% for nitrofurantoin to 27.6% for norfloxacineRange: 0% for nitrofurantoin and trimethoprim to 8.6% for doxycyclinfRange: 71.8% for amoxicillin to 84.8% for norfloxacingRange: 8.6% for norfloxacin to 22.0% for amoxicillinhRange: 3.3% for norfloxacin to 11.1% for clarithromycinTable 3Odds ratios for effect of preventive dose reduction (PDR) and for (severe) overanticoagulation in users of co-trimoxazole compared with users of other antibiotics Odds ratios (95%CI)PAdjusted Odds ratios (95% CI)aPEffect of PDR on overanticoagulationb Co-trimoxazole   PDR applied, INR >4.50.10 (0.02–0.50)0.005*0.06 (0.01–0.51)0.010*   PDR applied, INR >6.00.10 (0.01–1.02)0.0510.09 (0.01–0.92)c0.042*   PDR not appliedReferenceReference Other antibiotics   PDR applied, INR >4.50.70 (0.32–1.52)0.37N.A.d   PDR applied, INR >6.01.36 (0.47–3.93)0.57N.A   PDR not appliedReferenceRisk of overanticoagulation PDR not applied   Co-trimoxazole, INR >4.54.52 (1.56–13.1)0.006*3.96 (1.33–11.8)0.013*   Co-trimoxazole, INR >6.05.43 (1.53–19.2)0.009*3.86 (1.03–14.6)0.046*   Other antibioticsReferenceReference PDR applied   Co-trimoxazole, INR >4.50.68 (0.17–2.73)0.59N.A.   Co-trimoxazole, INR >6.00.41 (0.04–3.66)0.420.30 (0.03–3.05)e0.30   Other antibioticsReferenceReference*Statistically significant difference at P ≤ 0.05aAdjusted for differences in fever as indicated by patient, age, sex, target therapeutic range, unless otherwise indicatedbPDR, Preventive dose reductioncAdjusted for differences in age and sexdN.A., Adjustment not applied because the inclusion of covariates in our model did not result in a change of at least 5% in the odds ratios (see text)eAdjusted for differences in age, sex and fever as indicated by patient
If PDR was applied, the risk of overanticoagulation was not increased in users of co-trimoxazole compared with users of other antibiotics. However, if PDR was not applied, there was a strongly increased risk of moderate as well as severe overanticoagulation in co-trimoxazole users compared with users of other antibiotics (adjusted OR: 3.96, 95% CI: 1.33–11.8 for INR >4.5; adjusted OR: 3.86, 95%CI: 1.03–14.6 for INR >6.0) (Tables 2 and 3).
During the 6-week follow-up, co-trimoxazole users with a PDR spent more time within and less time under the therapeutic range than co-trimoxazole users without a PDR, but these differences were not statistically significant. Users of co-trimoxazole without a PDR spent significantly less time within the therapeutic range than users of other antibiotics with a PDR, whereas significantly more time was spent under the therapeutic range. Moreover, co-trimixazole users with a PDR also spent significantly more time under the therapeutic range than did all users of other antibiotics (adjusted mean difference: 6.9%; 95%CI: 1.0–12.9) (Tables 2 and 4).
Table 4Comparisons of time spent within, under and above the therapeutic range by users of co-trimoxazole and other antibioticsaMean differencePAdjusted mean differencebPPDR applied  Co-trimoxazole, % time within TRc−5.1 (−17.2 to 6.9)0.40−4.2 (−17.1 to 8.6)0.51  Co-trimoxazole, % time above TR2.7 (−7.3 to 12.7)0.601.9 (−8.9 to 12.7)0.73  Co-trimoxazole, % time under TR2.4 (−6.3 to 11.1)0.58N.A.d  Other antibioticsReferenceReferencePDR not applied  Co-trimoxazole, % time within TR−23.8 (−38.2 to –9.6)< 0.001*−22.3 (−36.6 to –8.0)e0.002*  Co-trimoxazole, % time above TR1.4 (−11.7 to 14.5)0.83N.A.  Co-trimoxazole, % time under TR22.5 (14.4 to 30.6)< 0.001*20.4 (12.4 to 28.5)f< 0.001*  Other antibioticsReferenceReferenceCo-trimoxazole  PDR applied, % time within TR19.3 (0.7 to 37.9)0.042*14.6 (−5.8 to 35.1)f0.16  PDR applied, % time above TR−5.3 (−19.6 to 9.0)0.46−4.4 (−19.1 to 10.3)g0.55  PDR applied, % time under TR−14.0 (−31.6 to 3.6)0.16−10.7 (−29.0 to 7.5)g0.24  PDR not appliedReferenceReferenceOther antibiotics  PDR applied, % time within TR0.5 (−7.0 to 8.1)0.890.6 (−6.8 to 8.2)0.87  PDR applied, % time above TR−6.6 (−13.5 to 0.3)0.061N.A.  PDR applied, % time under TR6.1 (2.0 to 10.1)0.003*N.A.  PDR not appliedReferenceReferenceCo-trimoxazole, PDR applied  % time within TR−4.7 (−15.1 to 5.6)0.37−3.0 (−13.5 to 7.5)f0.58  % time above TR−2.4 (−11.9 to 7.1)0.61−3.7 (−13.3 to 6.0)h0.46  % time under TR7.2 (1.2 to 13.1)0.018*6.9 (1.0 to 12.9)h0.022*  Other antibiotics     PDR applied + PDR not appliedReferenceReference*Statistically significant difference at P ≤ 0.05aCalculated for the time from the starting date of the antibiotic until the last INR measurement within 6 weeks following the starting date of the antibiotic.bAdjusted for differences in fever as indicated by patient, age, sex, target therapeutic range, unless otherwise indicated.cTR, Therapeutic range;dN.A., Adjustment not applied because including covariates in our model did not result in a change of at least 5 % of mean difference (see text).eAdjusted for differences in sex.fAdjusted for differences in sex and target therapeutic range.gAdjusted for differences in age, sex and target therapeutic range.hAdjusted for differences in sex, target therapeutic range and fever as indicated by patients.
Co-trimoxazole users with more than a 20% PDR spent significantly more time under the therapeutic range than users of other antibiotics (adjusted mean difference: 7.4 mg; 95%CI: 0.9–14.0; P = 0.027). If less than a 20% PDR was applied, the difference between the users of co-trimoxazole and those of other antibiotics shrunk and was no longer significant.
The application of a PDR differed between anticoagulation clinics. Three of the four anticoagulation clinics participating in this study applied PDR as a rule in co-trimoxazole users (83.3–85.7%). In terms of users of other antibiotics, the application of a PDR was more varied: in three of the anticoagulation clinics PDR was sometimes applied (in 17.6–50.8% of all cases), whereas one anticoagulation clinic did not apply the PDR approach at all. The overall percentage of time spent within the therapeutic range during the first 6 weeks after initiation of an antibiotic ranged from 73.7 to 78.0% at all four anticoagulation clinics. In the anticoagulation clinic that did not apply a PDR, overanticoagulation (INR>4.5) occurred most frequently for the all antibiotics class (26.9 vs.10.8–22.7% in the other clinics), with the difference being most marked for co-trimoxazole (54.4 vs. 14.3–16.7% in the other clinics).
We also analysed our data separately for users of acenocoumarol and phenprocoumon. There were no differences in the point estimates of most of our main outcomes between users of either of these coumarins, with the exception of percentage of time spent under the therapeutic range in phenprocoumon users for whom PDR was applied. However, most of the results that were statistically significant for all coumarin users were also significant for users of acenocoumarol (n = 252; 78.2 %), whereas they were in most cases not significant for the smaller group of users of phenprocoumon (n = 71; 21.8%) (Table 5).
Table 5Main outcomes stratified for users of acenocoumarol and phenprocoumonOutcomeAcenocoumarol (n = 254)Phenprocoumon (n = 71)Protective effect of PDRAdjusted OR (95%CI)aPAdjusted OR (95%CI)aPCo-trimoxazole   PDR applied, INR >4.50.08 (0.01–0.70)0.022*0.16 (0.01–4.48)>0.3b   PDR not appliedReferenceReferenceRisk of overanticoagulation   PDR not applied   Co-trimoxazole, INR >4.54.40 (1.15–16.8)0.030*3.83 (0.55–26.7)0.18   Other antibioticsReferenceReference% Time within or under TRMean difference (95%CI)Mean difference (95%CI)   Co-trimoxazole, % time within TR−22.1 (−39.1 to –5.0)0.011*−21.4 (−49.4 to 6.6)0.13   Co-trimoxazole, % time under TR20.3 (10.9–29.7)<0.001*22.6 (5.7–39.5)0.010*   Other antibioticsReferenceReference   Co-trimoxazole, PDR applied   % time under TR9.1 (3.0–15.1)0.004*−6.7 (−23.0 to 9.6)0.42   Other antibiotics      PDR applied + PDR not appliedReferenceReference*Statistically significant difference at P ≤ 0.05aAdjusted for differences in fever as indicated by patient, age, sex and target therapeutic rangebAdjustment not applied because the number of patients with INR >4.5 was zero; OR was calculated by increasing the values of each of the cells of the crosstable by 0.5
Re-analysis of our results after excluding patients with thyroid diseases and malignancy or those using enzyme-inhibiting or -inducing drugs gave similar point estimates or trends, significance for severe overanticoagulation in users of co-trimoxazole compared to other antibiotics and for time spent within the therapeutic range for users of co-trimoxazole in whom PDR was not applied (data not shown).
Discussion
The results of the present study, in which we evaluated the management of the interaction between antibiotics and coumarin anticoagulants by anticoagulation clinics, demonstrated that a PDR reduces the risk of overanticoagulation in co-trimoxazole users to the level of other antibiotic users, but also that management of the interaction between coumarins and co-trimoxazole results in a significantly longer period of undertreatment during the first 6 weeks after initiation of the antibiotic.
In three of the four anticoagulation clinics PDR was applied more frequently and was significantly higher in users of co-trimoxazole than in users of other antibiotics, indicating that anticoagulation clinics are aware of the seriousness of the interaction between coumarins and co-trimoxazole. In the cases and case series that have reported on overanticoagulation and bleeding with the concurrent use of antibiotics and co-trimoxazole [2, 3, 6–8, 10] an effect of the intercurrent infection on the anticoagulation status could not be ruled out. However, Penning-van Beest et al. (case control study) and Visser et al. (follow-up study) both demonstrated that an increased risk of severe overanticoagulation (INR >6.0) was particularly associated with co-trimoxazole [12, 21]. A plausible explanation is the strong inhibition of the main metabolising enzyme, CYP2C9, of the coumarins by sulfamethoxazole, the sulphonamide component of co-trimoxazole [24].
Although PDRs as applied in clinical practice are effective in reducing the overanticoagulation risk in co-trimoxazole users, the price that has to be paid for the concurrent use of co-trimoxazole is a significantly prolonged period of underanticoagulation compared with the use of other antibiotics during the first 6 weeks after the antibiotic course. This difference was more marked in the subgroup of subjects in whom PDR was not applied. Possible explanations for this result are (1) the usually shorter time span between PDR and the first INR measurement (always within the course) compared to the time span between a reactive dose reduction following supratherapeutic INR and subsequent INR measurement (usually after the course) and (2) the higher reactive dose reduction which is applied in the case of severe overanticoagulation (INR >6.0). However, even co-trimoxazole users for whom the PDR had been applied had a significantly prolonged period of underanticoagulation compared with all of the users of other antibiotics (PDR applied and PDR not applied taken together). This last comparison is totally logical because our results strongly suggest that a PDR should always be applied in co-trimoxazole users, whereas this is as a rule not required in users of other antibiotics. The adjusted difference in time spent under the therapeutic range – ranging from 6.9 (PDR applied) to 22.5% (PDR not applied) – corresponds to about 2–7 days of the mean follow-up time of 30 days in otherwise stabilised patients; this time interval is clinically relevant and can be avoided by substituting co-trimoxazole.
It is not difficult to explain the prolonged period of underanticoagulation in co-trimoxazole users. The application of a PDR, which was in this study higher in co-trimoxazole users, might overcompensate for overanticoagulation, whereas the reactive dose reduction following overanticoagulation carries the same risk of overcompensation and undertreatment as PDR. Consequently, the inhibition of CYP2C9 by co-trimoxazole superimposes an additional problem upon the already potentially destabilising effects of the infection and fever. Because our results for acenocoumarol in the separate analyses were predominantly in agreement with the overall results, our findings primarily apply to acenocoumarol users. It is possible that users of phenprocoumon are less sensitive to interactions with CYP2C9 inhibitors such as co-trimoxazole [20, 22]. We do expect that our results also apply to users of warfarin, which seems to be even more CYP2C9 sensitive than acenocoumarol [18].
Our study has several limitations. Because we retrieved medical data from anticoagulation clinics, it is possible that not all of the relevant data on potentially destabilising factors, such as malignancies, thyroid diseases and the use of other inhibitors of coumarin metabolism, were available. However, by only including patients who were obviously stable at the moment of initiation of the antibiotic, we decreased the chance that such factors changed the anticoagulant status during the antibiotic course. A second limitation is the absence of data on the presence of polymorphisms of the genes encoding the coumarin-metabolising enzyme CYP2C9 or the pharmacodynamic target of coumarins, VKORC1. The genotypes of both CYP2C9 [16, 22] and VKORC1 are strongly associated with interindividual variability in coumarin dose requirements [4, 14, 17, 23]. Further studies would be needed to assess whether the risk of overanticoagulation in co-trimoxazole users differs between carriers of a CYP2C9 or VKORC1 polymorphism and wild-type patients. It should be clear that our results only apply to patients with a stabilised anticoagulation state at the initiation of the antibiotic course.
In conclusion, if co-trimoxazole is prescribed to users of coumarin anticoagulants, the interaction can be managed by applying PDR, which adequately decreases the risk of overanticoagulation, but this successful management comes at the cost of a prolonged period of underanticoagulation after the course. Consequently, rather than managing the interaction it is better to avoid prescribing co-trimoxazole as a therapeutically equivalent alternative is always available.

coumarins
co-trimoxazole
acenocoumarol
phenprocoumon
antibiotics
anticoagulation clinic
drug interaction