Prevalence and predictors of health service use among Iraqi asylum seekers in the Netherlands

Background A long asylum procedure is associated with higher prevalence rates of psychiatric disorders, lower quality of life, higher disability and more physical health problems. Additional knowledge about health seeking behavior is necessary to guide governments and health professionals in their policies.
Introduction
Mental health service use differs among populations and geographical areas [3, 15, 25]. Refugees and asylum seekers have high prevalence rates of psychiatric disorders [6, 12] and adequate use of health services is important. Literature on this issue is growing, but still limited [8, 9, 16]. Health policies, including accessibility of service for asylum seekers differ between European Union countries [19]. In the Netherlands refugees have direct access to the general practitioner, while asylum seekers can only enter the curative health care system after a screening by a nurse or doctor in the asylum seeker center. This system has been criticized [18], but others claim that these professionals, involved in triage after receiving training in cultural competence, may prevent inadequate referrals.
Health service use, according to the Anderson behavioral model [1, 2], is a function of three sets of variables: predisposition, enablement and need. Predisposition includes demographic factors such as age, gender, religion, cultural factors, social network, and support. Enabling factors facilitate service use and include individual social functioning, availability of services, and costs. The third set, i.e. the need variables, consists of health related factors. Both objective (type and severity of a health problem), and subjective health indicators (disability and perceived health) belong to the need variables. McCracken et al. [15] used the above model in a community survey on depression in five European countries, and found that severity of depression, lower perceived health status, social dysfunctioning, and low level of social support were significant predictors of use. Kamperman et al. [11] used the model in a study among migrants in Amsterdam and found that there were migrant-specific mechanisms in health care consumption. Higher levels of acculturation and lower level of cultural traditionalism increased the use of mental health care facilities.
In these health service use studies, a variety of potential predictors, such as psychopathology, physical diseases, physical, and mental well-being, have been included. But disability and quality of life were not included. Nor is there a study that compared groups from the same country of origin, that differ in length of stay in a host country.
In a national community study among Iraqi asylum seekers (n = 294) in the Netherlands [12–14] we measured prevalence rates of psychiatric disorders, quality of life, disability, and physical health problems in relationship with pre- and post-migrations stressors. The study focuses on the risks of a long asylum procedure and showed that asylum seekers who stayed more than 2 years in the Netherlands had significantly higher overall prevalence rates of psychiatric disorders (66.2%), than those who arrived recently (42.0%). A long asylum procedure was an important independent risk factor for a psychiatric disorder with an odds ratio of 2.16 (CI 1.15–4.08). This ratio was higher than the odds ratio for premigration stressors, such as exposure to human rights violations. In addition the ‘long stay’ group had significantly lower quality of life, higher disability, and higher levels of physical health complaints. In the Anderson model these health indicators can be considered as need factors.
The aim of this paper is to examine patterns and predictors of health service use and their relationship with length of stay. The research questions are: (a) what is the prevalence of service use of Iraqi asylum seekers in the Netherlands, (b) is a long asylum procedure associated with a higher prevalence of service use, (c) what is the relationship between psychopathology and service use, and (d) which predisposing and other need variables predict service use. We hypothesize that a long asylum procedure is associated with a higher prevalence of service use and that higher levels of psychopathology are related to higher levels of service use.
Methods
A comprehensive description of used methods is provided in a previous article [12]. A summary is given below.
From the entire population of adult Iraqi asylum seekers, two groups were selected based on their length of stay in the Netherlands. Personal data on these groups was obtained from the Agency for the Reception of Asylum Seekers (COA). Group 1 was selected on the criterion that persons had been living in the Netherlands for less than 6 months (between September 2000 and November 2001). From the randomly selected 362 respondents, data could be used from 143 interviews. Group 2 was selected on the criterion that they were living in the Netherlands for at least 2 years. On the chosen date of May 31st, 1999, the COA found that 2,352 Iraqi asylum seekers fulfilled this criterion. From the randomly selected 474 respondents, data could be used from 151 interviews.
The questionnaire about service use—in the 2 months prior to the interview—included regular services as well as alternative services. The studied regular services are: out-patient services: preventive healthcare (nurse/doctor in center), primary healthcare (general practitioner), generic healthcare (medical specialist, non-psychiatric), social care (social worker), psychiatric services (mental health professional); in-patient services: hospital admission physical health, hospital admission mental health; use of drugs (any drugs, hypnotics, anxiolytics and analgetics). Examples of drug names were given to be sure the drugs were put in the right category. As mentioned above, in the Netherlands medical staff is present in all asylum seekers centers. They perform health assessments of recently arrived asylum seekers, facilitate entry into primary health care, and refer to the general or mental health services. Their services can be classified as gateway services [7]. The studied alternative services are: use of religious helpers or rituals, and herb-doctors or herbs.
Respondents’ predisposition to service use was measured by age, gender, religion, ethnicity, and length of stay (membership Group 1 or Group 2). Enabling factors were not measured: regular health services for asylum seekers are available and accessible in the Netherlands without financial obstacles. Need factors include: psychiatric disorders, physical health, quality of life, and disability, while post-migration living problems (PMLP) were added as a special set of need variables. Psychiatric disorders were measured with the WHO-CIDI, version 2.1 [28] and cluster diagnoses were used in the analyses. Physical health was assessed with 22 items, dealing with: perceived physical health, physical handicaps, chronic physical diseases (e.g. lung disease, epilepsy, diabetes), and chronic physical complaints (e.g. stomach problems, joint problems, headache more than 3 months). Quality of life (Qol) was assessed with the WHOQOL-BREF [22]. The first two single questions i.e.: ‘How would you rate your quality of life?’ (‘overall Qol’) and ‘How satisfied are you with your health?’ (‘Qol perceived general health’) were used in the current study. Disability (Brief Disability Questionnaire, VonKorff [26] was measured in two dimensions: total disability (total of score of 11 items on physical and social role impairments), and the total number of days with serious impairment in the last month (BDQ-days). Post-Migration Living Problems (PMLP) were assessed with a checklist, adapted from Silove et al. [21]. The 24 items were clustered, based on a factor analysis [13] as family issues, discrimination, asylum procedure, socioeconomic living conditions, and religious aspects. The items ‘lack of work’ and ‘work below level’ loaded on different factors and were analyzed as separate items.
The used Iraqi-Arabic composite questionnaire is based on a Palestinian-Arabic version [4, 5] and was culturally validated and translated with the help of a focus group. Oral interviews were taken by trained Iraqis.
Statistics
Differences between the two groups with respect to socio-demographics, psychiatric disorders, service use, and drug consumption were calculated with the χ2-test. Univariate relationships between predisposing and need variables were assessed with a correlation matrix. Ethnicity, religion, and marital status did not show a significant (P < 0.05) correlation with any of the health services. All other variables were entered into multivariate logistic regression analyses as: predisposing variables (study group, sex and age), need variables (one or more psychiatric disorders, overall quality of life, perceived quality of general health, disability (2 items), physical health (2 items), and a special set of need variables: the PMLP (7 variables). The same independent variables were used in each analysis, in line with the Anderson model. The dependent/outcome variables were: use of preventive service (nurse/doctor in the center), general practitioner, medical specialist (non-psychiatrist), social worker, mental health professional, and use of any drugs. We used a three-step procedure: in the first step each set of variables (predisposing factors, need factors and PMLP) was analyzed separately; in the second step all variables entered one analysis, that way the risk of one variable was corrected for the risks of all other variables. In step one and two, the entire dataset (n = 294) was used. In the third step the analysis was done for Group 1 and Group 2 separately in order to assess the differences of predictors between asylum seekers that had recently arrived (Group 1) and those who had stayed for more than 2 years in the asylum procedure (Group 2). Adjusted Odds Ratios (ORs), 95% confidence intervals (CIs), and P values were calculated (only the ratios with confidence intervals higher or lower than 1 are shown in the tables). Differences were considered significant at P < 0.05. All analyses were performed with SPSS version 10 [20].
Results
Socio-demographics, health and health related variables
The two study groups differed on several socio-demographic characteristics (Table 1). Group 1 contained more subjects younger than 24 years of age, and more females. The average time of stay in the Netherlands of Group 2 was more than 3 years. On the characteristics literacy, social status in Iraq, and psychiatric problems in the family, the two groups did not differ (not shown in the table, c.f. [12]. Group 2 had higher scores on prevalence of psychiatric disorders, disability and physical complaints, and lower quality of life score (Table 1). Except social-religious items, all clustered and non-clustered post-migration living problems were significantly higher in Group 2.
Table 1Socio-demographic, health characteristics and post-migration living problems in at random samples of Iraqi asylum seekers arrived <6 months (Group 1) versus >2 years (Group 2) in the Netherlands, 2000–2001VariablesGroup 1 (n = 143)Group 2 (n = 151)Total (n = 294)P-valueStay in months (mean, SD)2.51 (1.16)36.77 (6.30)20.12 (17.76)P < 0.0005, t(292) = 63.66Sex (%)P < 0.0005, χ2(1) = 27.31    Male49.778.864.6    Female50.321.235.4Age (%)P = 0.003, χ2(4) = 16.35    18–24 years21.79.315.3    25–34 years42.049.045.6    35–44 years14.725.820.4    >45 years21.715.818.7One or more psychiatric disorder (%)42.066.254.4P < 0.0005, χ2(1) = 17.44Overall quality of life (mean, SD)a2.88 (0.99)2.23 (1.14)2.55 (1.11)P < 0.0005, Z(294) = −5.29Perceived Qol general health (mean, SD)b3.06 (1.15)2.74 (1.27)2.89 (1.22)P = 0.017, Z(294) = −2.39Physical and role disability (mean, SD)c17.31 (7.43)19.25 (6.77)18.30 (7.15)P = 0.020, t(292) = −2.34Days of disability (mean, SD)d5.37 (8.24)7.68 (9.17)6.56 (8.80)P = 0.024, t(292) = −2.27Physical diseases (mean, SD)e0.85 (1.18)0.84 (0.98)0.85 (1.08)n.s.Physical complaints (mean, SD)e0.83 (1.38)1.62 (1.58)1.23 (1.54)P = 0.0005, t(292) = 4.52Post-migration living problems    Family related issues (mean, SD)f36.54 (26.55)52.81 (24.24)44.90 (26.62)P < 0.0005, t(292) = −5.49    Discrimination (mean, SD)f2.05 (7.92)11.17 (21.01)6.74 (16.65)P < 0.0005, t(292) = −4.87    Asylum proc. related issues (mean, SD)f48.58 (25.35)60.13 (23.23)54.51 (24.97)P < 0.0005, t(292) = −4.07    Socioeconomic living cond. (mean, SD)f22.35 (19.16)32.48 (21.08)27.55 (20.76)P < 0.0005, t(292) = −4.30    Socio-religious aspects (mean, SD)f12.96 (17.46)14.48 (17.19)13.74 (17.31)n.s.n.s.: not significant P ≥ 0.05aScale of 1 (very bad)–5 (very good)bScale of 1 (very bad)–100 (very good)cRange: 11 (no impairment at all)–33 (serious impairment)dRange: 0–31 (number of days of serious impairment in last months)eRange: diseases: 0–12; complaints: 0–6fRange: 0 (not worried)–100 (extremely worried about the issues)
Health service use
Table 2 shows the prevalence of service use in the 2 months prior to the interview. Overall, the most frequently used service was the preventive healthcare service, followed by the general practitioner. Sixteen (5.4%) respondents visited a mental health professional. Group 1 visited the preventive healthcare services in the center more often than Group 2 (72.0% vs. 39.7%) and Group 2 visited the mental health services more often than Group 1 (9.3% vs. 1.4%). More than 39% of the respondents used drugs, 31.0% used analgetics. Overall drug consumption was higher in Group 2 (45.7% vs. 32.2%). Use of alternative services and treatments was very low, and there was no difference between the two groups: contact religious helper (2.7%), use of religious rituals or treatment (5.1%), contact with herbal doctor (0.7%), use of herbal treatment (0.3%).
Table 2Health service use and other help seeking behavior in at random samples of Iraqi asylum seekers arrived <6 months (Group 1) and >2 years (Group 2) in the Netherlands, 2000–2001Use of services last 2 monthsGroup 1 n = 143 (%)Group 2 n = 151 (%)Total n = 294 (%)P valueUse of any health servicea76.966.271.4P = 0.042, χ2(1) = 4.119bUse of any out-patient (o-p) service74.152.362.9P < 0.005, χ2(1) = 14.992bUse of any o-p curative service38.536.437.4n.s.Use of preventive o-p service    Nurse/doctor in center72.039.755.4P < 0005, χ2(1) = 31.004bUse of o-p curative service    General practitioner32.925.829.3n.s.    Medical specialist in hospital12.617.915.3n.s.    Social worker5.66.66.1n.s.    Mental health worker1.49.35.4P = 0.003, χ2(1) = 8.846Use of in-patient service    Hospital admission physical health1.44.02.7n.s    Hospital admission mental health0.00.70.3n.sUse of any drugs32.245.739.1P = 0.018, χ2(1) = 5.643    Use of anxiolytics10.522.516.7P = 0.006, χ2(1) = 7.649    Use of hypnotics11.921.216.7P = 0.032, χ2(1) = 4.578    Use of analgetics23.837.731.0P = 0.010, χ2(1) = 6.707n.s.: P > 0.05aIncludes all regular and alternative services, including drugs, religious rituals/treatment and herbsbGroup 1 more than group 2, see text
Relationship between psychopathology and service use
Table 3 shows the univariate relationships between having ‘one or more psychiatric disorder’ and service use. Overall, respondents with psychopathology used significantly more services (70.0% vs. 54.5%), both curative and preventive ones. There was no significant difference in use of the services of the general practitioner, this was the case in both groups. Respondents with psychopathology visited a medical specialist (non-psychiatrist) much more often in Group 1, but not in Group 2 (P > 0.05).
Table 3Use of services in respondents with and without one or more psychiatric disorder in Iraqi asylum seekers arrived <6 months (Group 1), and >2 years (Group 2) in the Netherlands, 2000–2001One or more psychiatric disorderGroup 1 n = 143Group 2 n = 151Total n = 294Yes n = 60, %No n = 83, %Yes n = 100, %No n = 51, %Yes n = 160, %No n = 143, %Use of services last 2 months    Use of any health servicea88.368.7*76.047.1*80.660.4*    Use of any out patient (o-p) service86.765.1*60.037.3*70.054.5*    Use of any o-p curative service53.327.7*43.023.5*46.926.1*Use of preventive o-p service    Nurse/doctor in center83.363.9*47.025.5*60.649.3Use of o-p curative service    General practitioner41.726.529.019.633.823.9    Medical specialist (non-psychiatrist)26.72.4*21.011.823.16.0*    Social worker8.33.610.00.0*9.42.2*    Mental health worker1.71.213.02.0*8.81.5*Use of any drugs45.022.9*58.021.6*53.122.4*    Anxiolytics18.34.8*31.05.9*26.35.2*    Hypnotics18.37.2*29.05.9*25.06.7*    Analgetics31.718.148.017.6*41.917.9**P < 0.05aIncludes all regular and alternative services, including drugs, religious rituals/treatment and herbs
The use of drugs in respondents with psychopathology was higher in both groups, compared to those without psychopathology. Almost 60% of the respondents in Group 2 with psychopathology used drugs, versus 21.6% of those without psychopathology. Also the use of analgetics was higher, especially in Group 2 (48% vs. 17.6%).
Predictors of service use
In the ‘step 2’ analyses (Table 4), with all the independent variables in one analysis, the use of preventive healthcare services was predicted by Group 1 membership, low perceived quality of general health, and physical diseases. Visits to a general practitioner were predicted by age, low perceived quality of general health, and days of disability in the last month. Use of the services of a medical specialist (non-psychiatrist) was predicted by having one or more psychiatric disorders, high overall quality of life, low perceived quality of general health and physical diseases. Use of a social worker was predicted by low total disability and days of disability in the last month. Two variables predicted service use of a mental health worker: Group 2 membership, and low perceived quality of general health. Use of any drugs was predicted by age, and low perceived quality of general health. Use of analgetics (not in table) was predicted by age, lower perceived quality of general health, and physical diseases. The variables sex, physical complaints, and post-migration living problems (PMLP) did not predict any type of service use in the step 2 analyses (Group 1 and 2 together).
Table 4Multivariate logistic regression of predisposing and need (incl. PMLP: post-migration living problems) variables related to service use of Iraqi asylum seeker in the Netherlands (N = 294), 2000–2001ServiceNurse/Doctor centerGeneral practitionerMedical spec. (non-psychiatry)Social workerMental health workerUse of any drugsORaCI (95%)ORaCI (95%)ORaCI (95%)ORaCI (95%)ORaCI (95%)ORaCI (95%)Predisposing factors    Group 2 membershipb0.180.08–0.375.561.08–28.69    Age (older age)1.031.00–1.051.051.02–1.08Need factors    One or more psychiatric disorder1.341.18–2.76    Overall Quality of life1.801.18–2.76    Perceived quality of general health0.440.30–0.640.510.36–0.730.540.35–0.840.350.17–0.770.490.33–0.71    Disabilityc0.890.80–0.99    Disability daysd1.041.001–1.081.071.01–1.15    Physical diseases1.481.01–2.161.061.02–1.112.071.41–3.05PMLPaOR: Odds ratios are adjustedbGroup 2 membership >2 year in asylum procedurecDisability: total score of Brief Disability QuestionnairedDisability days in last month
Tables 5 and 6 show the ‘step 3’ analyses for Group 1 (<6 months) and Group 2 (>2 years in the Netherlands) separately. In both groups a lower score on perceived quality of general health predicted almost all types of health services as well as drug use. In Group 1, but not in Group 2, use of the services of a medical specialist (non-psychiatrist) was also predicted by physical diseases, and by having a psychiatric disorder (Table 5). In Group 2, but not in Group 1, two PMLP (asylum procedure and socio-economic living conditions) predicted visits to the nurse/doctor in the center, the general practitioner, and/or a medical specialist (Table 6). In Group 2, but not in Group 1, number of days of disability in the last month and physical complaints predicted the use of analgetics (not in table).
Table 5Multivariate logistic regression of predisposing and need (incl. PMLP: post-migration living problems) variables related to service use of Iraqi asylum seeker, <6 months in the Netherlands (Group 1: N = 143), 2000–2001ServiceNurse/doctor centerGeneral practitionerMedical spec. (non-psychiatry)Mental health workerUse of any drugsORaCI (95%)ORaCI (95%)ORaCI (95%)ORaCI (95%)Predisposing factors    Age (older age)1.061.01–1.10Need factors    One or more psychiatric disorder1.931.18–3.16    Overall quality of life2.491.05–3.163.161.29–7.75    Perceived quality of general health0.250.11–0.560.550.31–0.970.410.18–0.960.290.12–0.74    Disability daysb1.081.01–1.15    Physical diseases2.651.33–5.282.711.33–5.54PLMPaOR: Odds ratios are adjustedbDisability days in last monthTable 6Multivariate logistic regression of predisposing and need (incl. PMLP: post-migration living problems) variables related to service use of Iraqi asylum seeker, >2 years in the Netherlands (Group 2: N = 151), 2000–2001ServiceNurse/doctor centerGeneral practitionerMedical spec. (non-psychiatry)Mental health workerUse of any drugsORaCI (95%)ORaCI (95%)ORaCI (95%)ORaCI (95%)ORaCI (95%)Predisposing factors    Age (older age)1.041.00–1.09Need factors    Perceived quality of general health0.54034–0.870.430.25–0.720.540.31–0.960.410.18–0.880.580.37–0.93    Disabilityb0.920.86–0.99    Disability daysc1.071.01–1.13    Physical diseases0.560.32–0.992.161.26–3.79PMLP    Asylum procedure1.381.04–1.830.680.51–0.92    Socio-economic l.c.1.191.02–1.381.231.00–1.50aOR: Odds ratio’s are adjustedbDisability: total score of Brief Disability QuestionnairecDisability days in last month
Discussion
The main findings of this study are: There is a high overall service use among Iraqi asylum seekers in the Netherlands. The hypothesis that a long asylum procedure is associated with higher levels of service use, is not confirmed by the results, except for mental health service use and drug use. Psychopathology is related to a higher level of service use (second hypothesis), but when corrected for the influence of other predisposing and need factors, other factors, such as: high role and functional disability, and low perceived quality of general health, are more important predictors. Moreover having one or more psychiatric disorder(s) predicts the use of a medical specialist (non-psychiatrist), but does not predict mental health service use. The overall use of mental health service use is very low compared to the high prevalence of psychiatric disorder: over 80% of the asylum seekers with a psychiatric disorder used any health service, but only 8.8% visited a mental health service.
Next paragraphs will discuss the four research questions.
Prevalence of service use, relationship with length of stay
The preventive healthcare services are the most frequently visited services in both groups. As explained earlier (see “Methods”), in the Netherlands medical staff is present in all asylum seeker centers. Shortly after arrival, all asylum seekers are supposed to get a preventive medical screening by the doctor in the center doing triage for e.g. tuberculosis and AIDS. This probably explains the higher use of this service by Group 1. Gerritsen et al. [9] found that 63.7% of the asylum seekers from Iran, Afghanistan and Somalia reported a visit to the preventive healthcare services in the center. Their average length of stay was 3.4 years, comparable with Group 2. The rate of the Iraqi group in this study is much lower: 39.7%, suggesting a difference in use of this service between asylum seekers with different origins.
In contradiction to the hypothesis, the use of a general practitioner in Group 2 was not higher compared to Group 1. The use (30%) is even lower compared to the general Dutch population, which is 42%, and even more so compared to immigrants: 51% [27]. Also Van Oort et al. [24] found that asylum seekers visit the general practitioner less often than the general Dutch population: number of contacts per year 3.5 vs. 4.5. So, despite the higher prevalence of health problems, asylum seekers make less use of the general practitioner. Furthermore, use of a medical specialist is not higher in Group 2. This is explained by the fact that in the Dutch system a patient can only visit a medical specialist after referral by a general practitioner.
Drug consumption is significantly higher in Group 2 (45.7%), compared to Group 1 (32.2%), confirming the hypothesis. Gerritsen et al. [9] found an even higher rate (57.8%). The high use of analgetics is striking (see later).
Use of alternative services is very low. Maybe Iraqi asylum seekers are not interested in these services, but even in case they would be, these services are probably not easily available and accessible. We found no other studies on this issue among Iraqi refugees/asylum seekers.
Relationship psychopathology and service use
The findings indicate a huge unmet need for mental health care. About 30% of the asylum seekers with a psychiatric disorder did not visit any service, and more than 90% of the asylum seekers with a psychiatric disorder did not visit a mental health service (Table 3). However 60.6% visited a nurse/doctor in the center and 33.8% the general practitioner. Both services are important in the pathway to mental health care (see later).
We hypothesized that higher levels of psychopathology would be related with higher service use. In the univariate analyses this hypothesis stands, except for use of a general practitioner. Also, there are differences between the groups. The hypothesis is strongly confirmed when we consider drugs use. The high use of analgetics in Group 2 might be explained by the high levels of pain disorders (11.3%) and physical health complaints (66.2%) in this group. However other explanations are possible. Van Dijk et al. [23] did a qualitative study among 22 asylum seekers and concluded that it seems that “the prescription of paracetamol has become a symbol for the lack of interest of and the rejection by the health care system”. Their study reports dissatisfaction with the services of the nurse/doctor in the center, as well as with the services of the general practitioner.
Predictors of service use
After correcting the risks for all other included risk factors in multivariate analyses, low perceived quality of general health was the only significant predictor for mental health service use. Psychopathology, disability, nor physical complaints were significant predictors, while these factors were found to be important predictors in other studies (e.g. [8, 17]). Psychopathology, however, was a predictor for higher use of a medical specialist (non-psychiatrist). Our findings lead to the following hypothesis: (1) asylum seekers present themselves with physical rather than with mental problems (we know [14] that the level of physical complaints is high), (2) the staff in the center and the general practitioner do not recognize the mental health problems, and if they do (3) only a few patient are referred for adequate mental health care. This hypothesis is supported by the findings in the study of Van Oort et al. [24]. They found that in only 6% of the cases ‘mental health problems’ was recorded as reason of visiting the medical staff in the center, and only 2% of the referrals to the general practitioners were because of mental health reasons. The general practitioner diagnosed a mental health problem in 10% of the cases, and of those only 21.4% were referred to a mental health service (while 33.3% was referred to a medical specialist).
The mismatch between type of health problem and type of health service use seems to be less pronounced in Group 2: in the analyses per group (Tables 5, 6) psychopathology did not predict the use of a medical specialist in this group.
A curious finding is that overall quality of life has a positive relationship with the use of medical specialist. There might be a parallel with the phenomena that low social support is a risk factor for psychopathology [10], but high social support predicts health service use in some studies [15].
Several post-migration living problems (PMLP) predict health service use in Group 2. Especially worries about socio-economic living conditions increase the need for healthcare. The findings do not support the idea that asylum seekers look for recognition of their health problems in order to get a resident permit: there is no relationship between PMLP and mental health service use and there is a negative relationship with use of a medical specialist.
Conclusion
This study has shown that Iraqi asylum seekers have a low level of mental health service use, despite the high levels of psychiatric disorders and other health indicators, especially within the group that stayed in the asylum procedure for over 2 years.
Moreover, there is a mismatch between the type of health problem and the type of health service use: asylum seekers with a psychiatric disorder make more use of non-mental health service. There is room for improvement of the ‘gate way’ preventive services in detection and referrals of patients with mental health problems. The study results suggest that this service is a barrier rather than a facilitator in the pathway to mental health care. The general practitioner should be more involved and consulted. Mental health institutions are recommended to start and/or improve consultation and (assertive) care programs.

service use
iraq
asylum seekers
psychiatric disorders
drug consumption


Immunoglobulin and free light chain abnormalities in Gaucher disease type I: data from an adult cohort of 63 patients and review of the literature

Gaucher disease type I, the most common lysosomal storage disorder, is associated with immunoglobulin abnormalities. We studied the prevalence, risk factors, pathogenesis, and effect of enzyme relation therapy (ERT) on gammopathies in an adult Gaucher disease type I cohort (N = 63) and related the results to a review of the currently available literature. Polyclonal gammopathies and monoclonal gammopathy of undetermined significance (MGUS) in our adult GD I cohort were found in 41% and 19% of patients. These results are similar to the data from the literature and correspond to the increased risk of multiple myeloma (MM) that has been described. The prevalence of MGUS in our cohort increased with age but was not associated with disease severity or exposure time. The serum levels of free light chains of immunoglobulins were measured and were not found predictive for the development of MGUS or MM. Levels of pro- as well as anti-inflammatory cytokines, growth factors, and chemokines, especially those involved in inflammation and B-cell function, are disturbed in GD I, with the most impressive and consisting elevations for interleukin-10 and pulmonary and activation-regulated chemokine. A beneficial effect of ERT on the occurrence and progression of gammopathies was suggested from longitudinal data.
Introduction
Gaucher disease type I (GD I, OMIM #230800) is the most common lysosomal storage disorder. The disease is characterized by a deficiency of the lysosomal enzyme glucocerebrosidase (glucosylceramidase), which leads to the accumulation of glucocerebroside in macrophages. The lipid laden macrophages are called Gaucher cells and are mainly found in liver, spleen, and bone marrow, resulting in the most important clinical features hepatosplenomegaly, skeletal disease, and cytopenia [1, 2]. Since 1991, GD I can be effectively treated with recombinant glucocerebrosidase (enzyme replacement therapy, ERT, imiglucerase; Genzyme, Cambridge, MA, USA). More recently, substrate reduction therapy (miglustat, Actelion Pharmaceuticals, Basel, Switzerland) has been registered for more attenuated disease.
An associated feature of GD I is the high frequency of polyclonal and monoclonal gammopathies [3–22]. In the general population, polyclonal hypergammaglobulinemia is a common clinical finding, especially in the setting of chronic inflammation [23], resulting from an overproduction of immunoglobulins by plasma cells or B-lymphocytes. An abnormal increase of monoclonal immunoglobulins or fragments thereof (M protein) can be found in pre-malignant conditions such as MGUS (monoclonal gammopathy of undetermined significance) and malignancies of the B-cell lineage such as multiple myeloma (MM), primary amyloidosis, Waldenström macroglobulinemia, chronic lymphocytic leukemia, and B-cell lymphoma. MGUS is found in approximately 3.2% of adults over the age of 50, increasing to 7.5% at the age of 85 [24]. Of these individuals, 1% per year shows progression to MM or a related malignancy. Accordingly, in GD I the risk of MM is strongly increased, as has been reported in recent studies [25–29]. The risk increases with advancing age [19, 26], but whether disease severity is a risk factor is currently unknown.
The mechanism explaining the relationship between GD and immunoglobulin abnormalities is far from understood. Studies have mainly focused on cytokines as the mediators between Gaucher cells, surrounding macrophages and B cells. Plasma levels of several pro-inflammatory cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1, and IL-6, as well as the anti-inflammatory IL-10, were found to be increased in GD to variable extents [4, 30–33]. IL-6 and IL-10, as well as hepatocyte growth factor (HGF), are also involved in the pathogenesis of MM [34]. Other interesting factors, known to be highly elevated in GD patients, are pulmonary and activation-regulated chemokine (PARC) [35], which is involved in initiating the adaptive immune response. Two studies have reported on the effect of ERT, with some decrease in polyclonal gammopathies [6] and a decline in monoclonal M-protein level in a single case [36].
A relatively new method to identify and monitor monoclonal gammopathies is by quantifying immunoglobulin free light chains (FLC), circulating κ and λ chains that are not bound to the immunoglobulin heavy chains, using a highly sensitive automated immunoassay [37–39]. An abnormal FLC ratio is an important independent risk factor for progression of MGUS to MM or a related disorder [40, 41]. In addition, measuring FLC is a sensitive additional tool to identify monoclonal gammopathies [42]. In GD I, in which the incidence of MGUS and MM is highly increased [26], the FLC assay may provide a useful diagnostic tool for early detection of MGUS and MM.
In this study, we present new data on the prevalence of polyclonal and monoclonal gammopathies from a large adult GD I cohort and review the currently available literature. Second, we studied whether age, disease severity, and exposure time are risk factors for the development of monoclonal gammopathies, and if this risk is subsequently decreased by the administration of ERT. In addition, for the first time, we investigated the role of FLC as a predictor of the development of monoclonal gammopathies in GD I. Finally, the pathogenesis of immunoglobulin abnormalities in GD patients was studied by means of pro- and anti-inflammatory cytokine, chemokine, and growth factor levels and is discussed in relation to previous literature.
Materials and methods
Patients and study design
The files were reviewed and archived serum samples were collected of all adult GD type I patients that were known from the outpatient clinic for inherited metabolic disorders at the Academic Medical Centre (AMC), Amsterdam, The Netherlands (N = 63). The diagnosis GD was based on measurement of deficient glucocerebrosidase activity in leukocytes and genotyping [43]. Of the 63 patients, 50 received ERT according to an individualized dosing protocol [44] and two patients received substrate reduction therapy. Data on age, sex, splenectomy, genotype, severity score index, (SSI [45]), creatinine, urea, chitotriosidase, and the presence and type of immunoglobulin class of monoclonal proteins were collected. In patients with a monoclonal gammopathy, exposure time was defined as the number of years between the diagnosis GD I and the diagnosis monoclonal gammopathy or start of ERT, whichever came first. In patients without a monoclonal gammopathy, exposure time was calculated as the number of years between the diagnosis GD I and start of therapy, or, in patients not receiving therapy, the first visit to our clinic.
MGUS was defined as the presence of a monoclonal immunoglobulin component in the serum (as determined by protein electrophoresis in agar/agarose gels and immunofixation) at a concentration of less than 30 g/l (IgG, IgA, or IgM) and normal serum calcium and creatinine [46]. Although histology is required to distinguish osteolytic lesions caused by MM from those caused by GD, we decided to classify long-existing lesions as being GD related. Bone marrow examinations were only performed when a previously unknown M protein was discovered or in case of a sharp rise in the levels of immunoglobulins or FLC. In case of MM, stage was determined according to the Durie–Salmon [47] and International Staging System (ISS) criteria [48].
We investigated whether levels of FLC could be predictive for the development of MGUS and whether these levels were related to disease severity. Therefore, we measured FLC levels in serum before start of enzyme replacement therapy in ten patients with mild GD (SSI ≤ 8) and ten patients with severe GD (SSI ≥ 9), all without a monoclonal gammopathy.
In addition, we studied whether changes in cytokine levels and in FLC and immunoglobulins at baseline and during ERT were related to the presence or absence of a monoclonal gammopathy. Therefore, the group of patients with a monoclonal gammopathy was matched for age, sex, spleen status, SSI, and the use of ERT to a control group of GD patients without a monoclonal gammopathy. Serial measurements of levels of IL-6, IL-10, and PARC at baseline and after 12, 24, and a median of 114 months of ERT (range 40–143, whatever was the last determination) were performed. These were related to changes in immunoglobulins and free light chains. Hepatocyte growth factor (HGF) was measured only at baseline. One patient did not receive therapy; the first hospital visit was used as T0.
Assays
The standard enzyme activity assay for chitotriosidase with 4-MU-chitotriose (Sigma, St. Louis, MO, USA) as a substrate was performed at pH 5.2 [49]. Serum κ and λ FLC were measured using FREELITE reagents (The Binding Site Ltd, Birmingham, England) on a BNII nephelometer (Dade-Behring, Deerfield, IL, USA) [37–39]. Monoclonal FLC were identified as values for κ or λ that exceeded the reference ranges (κ 6.2–30.2 mg/l, λ 9.1–40 mg/l) and produced an abnormal κ/λ ratio (<0.3 or >1.57). Elevated concentrations of FLC without an abnormal κ/λ ratio indicate a polyclonal or oligoclonal increase of FLC. Levels of IgG, IgA, and IgM were measured by immunoturbidimetric assay (Roche Tina-quant). IL-6, IL-10, HGF, and PARC were measured by specific enzyme-linked immunosorbent assays (ELISAs), according to the instructions of the manufacturer (IL-6 and IL-10: Sanquin, Amsterdam, The Netherlands; HGF: R&D Systems, Minneapolis, MN, USA; PARC: Biosource International, Camarillo, CA, USA).
Statistics
Values in patients are given as medians and ranges. Differences between patient groups were analyzed by the Mann–Whitney U test or by χ-square test. Linear mixed models were used to assess longitudinal changes. Correlations were tested by the rank-correlation test (Spearman coefficient, ρ). P < 0.05 was considered to represent a statistical difference.
Search strategy
To compare the prevalence of poly- and monoclonal gammopathies in our cohort with earlier case series and to study the relative risk of MM, we performed a MEDLINE search combining the MESH terms Gaucher disease with either hypergammaglobulinemia, paraproteinemias, and/or multiple myeloma. Literature on cytokines, chemokines, and growth factors in relation to the development of immunoglobulin abnormalities in GD were searched combining the MESH terms Gaucher disease with cytokines and/or chemokines. Case reports were excluded and only studies that included at least ten patients were selected.
Results
Patients
The 63 patients had a median age of 53 years (range 25–83). Thirty-two of patients (51%) were men, and 23 (37%) were splenectomized. At baseline, median SSI was 8 (range 3–19). The majority of patients had a genotype containing N370S (95%), with N370S/L444P being the most common combination (40%). All patients had creatinine and urea values within the normal range.
At the first assessment, 24 (38%) patients had normal immunoglobulin levels, 26 (41%) patients had a polyclonal gammopathy, 12 (19%) patients had MGUS, and one patient had MM (Table 1, #2). During follow-up (range 5–16 years), none of the patients with normal immunoglobulin levels or a polyclonal gammopathy developed a monoclonal gammopathy.
Table 1Characteristics of Gaucher disease type I patients with a monoclonal gammopathyPatient no.SexSxAge in 2007 or at deathTherapyMG typeIg type1MN55ERTMGUS, progression to amyloidosis and MMFree κ2MY71ERTMMIgGκ3FN46ERTMGUS, progression to amyloidosisIgGλ4MY60ERTMGUSIgGκ5MY67ERTMGUSIgGλ6MY59ERTMGUSIgAλ, IgGκ7FN67ERTMGUSIgAκ8MN56ERTMGUSIgGλ9MY65ERTMGUSIgGκ, IgGλ, IgAκ10MN56ERTMGUSIgMκ, IgMλ11FN63NoMGUSIgGλ, IgMκ12FY75ERTMGUSIgGκ13MN51ERTMGUSIgGλM Male, F female, Sx splenectomy, ERT enzyme replacement therapy, MG monoclonal gammopathy, MM multiple myeloma, MGUS monoclonal gammopathy of undetermined significance, Ig immunoglobulin
Of the 12 patients with MGUS, two developed MM and/or amyloidosis after approximately 2 years of ERT (#1 and 3). Patient #1 was diagnosed with MM and amyloidosis 25 months after start of ERT. Pulse therapy with dexamethasone was started. Three months after the diagnosis the patient died from cardiac failure. At 24 months after start of ERT, patient #3 was diagnosed with amyloidosis. She received melphalan and prednisone, but died 12 months later from cardiac failure. Patient #2 was diagnosed with MM stage IA before start of ERT. During follow-up, IgG levels gradually increased. After 7 years of ERT, treatment with reduced dose melphalan and prednisone was started, which resulted in unacceptable cytopenia. Subsequently, thalidomide, 100 mg daily, was started with beneficial effect and stable disease parameters for the last 3 years.
Of the ten remaining patients with non-progressive MGUS, six had a monoclonal, three had a biclonal, and one a triclonal gammopathy. The most common immunoglobulin type was IgGκ (Table 1; results previously presented in part in [26]).
Patients with a monoclonal gammopathy were significantly older than patients without a monoclonal gammopathy (Table 2). Exposure time to GD, defined as time from diagnosis until first assessment at our clinic, and severity of disease measures, assessed by SSI and chitotriosidase at baseline, were comparable.
Table 2Baseline characteristics of Gaucher type I patients with a monoclonal gammopathy vs patients without a monoclonal gammopathy Without MGWith MGPNo. of patients5013 Age in 2007 or at death51 (25–83)60 (46–75)0.003No. of male patients23 (46%)9 (69%)NSYears of exposure11 (0–37)18 (1–44)NSNo. of splenectomies17 (34%)6 (46%)NSSSI at baseline7 (3–19)12 (4–16)NSChitotriosidase (nmol/ml h) at baseline16,703 (5,409–132,199)22,534 (6,417–62,122)NSData reflect absolute numbers (and percentage) or median (and range).MG Monoclonal gammopathy, NS not significant, SSI severity score index
A search for studies on monoclonal and polyclonal gammopathies in GD I resulted in five series [4, 6, 16, 19, 21]. Polyclonal gammopathies were reported in 14–64% and monoclonal gammopathies in 1–35% of GD I patients (Table 3).
Table 3Studies on the prevalence of monoclonal and polyclonal gammopathies in type I Gaucher diseaseReferenceNo. of Gaucher patientsAgePolyclonal gammopathiesMonoclonal gammopathiesde Fost et al. [26]6325–8326 (41%)12 (19%)Pratt et al. [19]169–706 (38%)4 (25%)Shoenfeld et al. [21]2524–7815 (60%)2 (8%)Marti et al. [16]2341.8 ± 1810 (43%)8 (35%)Allen et al. [4]2223–6514 (64%)3 (14%)Brautbar et al. [6]50716–8114–25%5 (1%)Data reflect absolute numbers (and percentage), or, in the study of Marti et al., mean ± standard deviation.
Five studies have described the prevalence and/or relative risk of MM in GD patients [25–29]. Table 4 summarizes the data from these studies. The frequency of MM in these cohorts was 0.4–4.0%, with significantly elevated relative risks of 5.9–51.1.
Table 4Studies on the prevalence and relative risk of multiple myeloma in type I Gaucher diseaseReferenceNo. of patientsEthnic backgroundAgeControl groupNo. of patients with MMRR of MMde Fost et al. [26]131Mixed50 ± 14Dutch Cancer Registry2 (1.5%)51.1 (95% CI: 6.2–184)Lee [25]239MixedNot givenNone5 (2%)NDShiran et al. [28]48Jewish54 ± 20511 individuals from the same region2 (4%)NDZimran et al. [29]505Jewish38 ± 21Israeli Cancer Registry2 (0.4%)NDRosenbloom et al. [27]2,510Mixed33US Cancer Registry10 (0.4%)5.9 (95% CI: 2.8–10.8)Data on age reflect mean ± standard deviation; data on number of patients with multiple myeloma reflect absolute numbers (and percentage).MM Multiple myeloma, RR relative risk, 95% CI 95% confidence interval, ND not done
Immunoglobulin and FLC levels
In 20 GD patients from the Dutch cohort without a monoclonal gammopathy (ten with mild disease, SSI ≤ 8, and ten with severe disease, SSI ≥ 9), FLC were measured before start of therapy (Fig. 1a). One patient was found to have a slightly abnormal ratio, and six patients had an increase in one or both FLC, with a normal ratio. There were no significant differences in κ or λ FLC-levels between patients with severe- and patients with mild GD I. During follow-up (range 10–16 years), none of the patients developed a monoclonal gammopathy.
Fig. 1FLC levels in Gaucher disease type I patients without a monoclonal gammopathy with mild disease (SSI ≤ 8) and severe disease (SSI ≥ 9) (a). FLC levels in Gaucher disease patients with a monoclonal gammopathy and matched Gaucher disease controls (b). MG Monoclonal gammopathy. The normal range for κ was 6.2–30.2 mg/l and for λ was 9.1–40 mg/l. The normal ratio for κ/λ was 0.3–1.57
FLC levels were measured in all GD I patients with a monoclonal gammopathy (Table 1, #1–13) and matched GD I controls (#co1–co13). No serum was available of patient #12, resulting in two groups of 12 patients. Baseline immunoglobulin levels were not available in one patient with a monoclonal gammopathy (#11) and five patients from the control group (#co4, #co5, #co7, #co11, and #co13). At baseline, the patients who already had or would develop MM and/or amyloidosis (#1–3) had strongly abnormal FLC κ/λ ratios (Fig. 1b). Of the nine patients with MGUS, one (#7) had an abnormal FLC κ/λ ratio and four showed elevated levels of FLC κ or λ, but with a normal FLC ratio. The remaining four patients with MGUS had both FLC levels as well as a FLC κ/λ ratio within the normal range. Six of the patients from the control group had FLC levels and a FLC κ/λ ratio within the normal range, and six had elevated levels of one or both chains of whom only one (#co6) patient showed an abnormal FLC κ/λ ratio. During follow-up (range 6–15 years), none of the patients from the control group developed a monoclonal gammopathy.
Cytokines, chemokines, and growth factors
At baseline, IL-6 levels were within the normal range in all but four patients (Fig. 2). There was no significant difference in IL-6 levels between patients with or without a monoclonal gammopathy (median (range) 5.9 pg/ml (1.2–118.4) and 2.2 pg/ml (1.0–57.2), respectively). The majority of patients (17/24) showed elevated IL-10 levels, especially in the group of patients with a monoclonal gammopathy, although not significantly different from the patients without a monoclonal gammopathy (17.1 pg/ml (3.9–419.8) and 6.9 pg/ml (1.4–299.3), respectively, P = 0.2). PARC levels were elevated in all GD patients, without a difference between patients with or without a monoclonal gammopathy (1,400.0 pg/ml (348.9–2817.3) and 1,078 pg/ml (411.4–3,552.0), respectively). HGF was within the normal range in most (16/22) patients, without a difference between patients with or without a monoclonal gammopathy (1,751 pg/ml (926–4,791) and 936 pg/ml (424–9,382), respectively).
Fig. 2Baseline levels of IL-6, IL-10, PARC, and HGF in Gaucher disease patients with a monoclonal gammopathy and matched Gaucher disease controls. Patients with multiple myeloma and/or amyloidosis are marked by an arrow. Dotted lines reflect the upper limit of the normal range. IL Interleukin, HGF hepatocyte growth factor, PARC pulmonary and activation-regulated chemokine, MG monoclonal gammopathy
A literature search resulted in seven studies on cytokines, chemokines, and growth factors in GD I patients (Table 5). In one of these studies, mRNA levels were measured [32], while in the remaining studies, plasma levels were studied. If possible, the number of patients with cytokine levels exceeding the reference range was determined. In studies in which normal ranges were not provided [4, 32], we defined the normal range as mean ± 2SD based on the values of the control group. This was not possible in the study of Barak et al. [30], in which no detailed data were provided.
Table 5Studies on plasma levels of cytokines, chemokines, and growth factors in Gaucher type I patientsReferenceNo. of GD I patientsAgeCytokinesResults, no. of patients (%) with elevated levelsP value for difference with mean values of the control groupde Fost et al. [26]2225–83IL-64/24 (17%)NDIL-1017/24 (71%)PARC24/24 (100%)HGF6/22 (27%)Michelakakis [33]25NGTNF-α16/25 (64%)NDAllen et al. [4]2223–65IL-1βNot detectableNSTNF-α4/11 (36%)NSIL-619/22 (86%)P = 0.0001IL-1013/13 (100%)P < 0.0001Lichtenstein et al. [32]18NGIL-1β mRNA5/19 (26%)P = 0.0337TNF-α mRNA2/19 (11%)NSIL-6 mRNA3/19 (16%)NSIL-8 mRNA3/19 (16%)NSHollak et al. [31]2916–66M-CSF24/28 (86%)P < 0.001sCD1425/27 (93%)P < 0.0005IL-825/27 (93%)P < 0.0005IL-60/27 (0%)NSTNF-α0/27 (0%)NSBarak et al. [30]215–39IL-1βMean levels elevatedP = 0.01IL-1RAMean levels elevatedP = 0.01sIL-2RMean levels elevatedP < 0.001IL-6Mean levels elevatedP < 0.01IL-8Not elevatedNSTNF-α3/21 (14%)NSBoot et al. [35]5512–67PARC55/55 (100%)P < 0.0001Altarescu et al. [56]1211–67TNF-α1/12 (8%)NSNG Not given, ND not done, NS not significant
Longitudinal changes
In general, at no time point was there a statistically significant difference in plasma levels of any of the factors (i.e., IgG, IgA, IgM, FLC κ and λ, IL-6, IL-10, and PARC) between patients with and patients without a monoclonal gammopathy (Fig. 3). In addition, using mixed model analysis, in neither of the two patient groups, a significant decrease or increase could be established in plasma levels of any of the measured factors. The only exception was PARC, which decreased in all patients, except for one patient who did not use therapy (#11). Changes in PARC levels could not be related to changes in levels of immunoglobulins or free light chains. There were no strong correlations (i.e., rho > 0.6) between any of the factors.
Fig. 3Longitudinal changes in plasma levels of immunoglobulin heavy and free light chains, IL-6, IL-10, and PARC. Dotted lines reflect the normal range. Numbers reflect Gaucher disease patients with a monoclonal gammopathy as described in Table 1 (#1–13) and matched Gaucher disease controls (#co1–co13) without a monoclonal gammopathy. IL Interleukin, PARC pulmonary and activation-regulated chemokine
More specifically, immunoglobulin levels remained stable or decreased in all patients, except for patient #11, who showed an increase in IgM. This patient was different from other MGUS patients because she did not receive therapy. Free light chain levels were more variable, but clearly FLC levels of patients who were to develop or already suffered from MM and/or amyloidosis remained high during ERT (#1–3). Although in most patients no trends could be distinguished, a consistent decrease in free light chains and immunogobulins was seen for example in patient 7, who had an IgAκ M protein. This patient responded very well to ERT, but other patients with excellent clinical responses did not always show a similar decrease in M-protein levels. No relationship was found between clinical response and decrease in either levels of immunoglobulins or FLCs.
The course of cytokines did not show a relationship with changes in either FLC or immunoglobulin levels, although in the untreated patient with increasing levels of IgM (#11), also an increase in IL-6 and PARC was noted. On the contrary, a spontaneous decrease in IL-6 was found in patient #2, without therapy for his MM. In patient #1, IL-10 showed a clear decrease after start of ERT but before the diagnosis amyloidosis and MM was made.
Discussion
In addition to the classical symptoms of GD I, such as hepatosplenomegaly, skeletal disease, and cytopenia, there is increasing evidence for existence of co-morbidities, including increased risk of cancer and abnormal immunoglobulin profiles. In fact, polyclonal gammopathies and MGUS occurred in 41% and 19%, respectively, of our adult Gaucher cohort, which is in line with findings from the literature (14–64% and 1–35% of patients [4, 6, 16, 19, 21]). In addition, we established that the relative number of biclonal (3/10) and triclonal (1/10) gammopathies within the MGUS patients was especially high considering that in general, biclonal gammopathies occur in 3% of subjects with MGUS [24], while triclonal gammopathies occur only very sporadically [50]. The types of immunoglobulin heavy and light chain immunoglobulins were comparable to the findings for MGUS in a non-GD population, with a predominance of IgGκ [24]. As in the general population, the prevalence of MGUS in our cohort increased with age, but there was no relationship with disease severity measures, as assessed by exposure time, baseline SSI, and chitotriosidase levels. A relationship with age was first suggested by Pratt et al. [19], who investigated 16 patients aged 9 to 70 years and established an MGUS in four patients, all being over 50 years of age. Indirectly, he also showed an association with severity of disease, since all patients with splenomegaly had either a monoclonal or polyclonal gammopathy.
Considering the relatively high risk of monoclonal gammopathies in GD, the question was raised whether sensitive measuring of FLC would be useful for the early detection of monoclonal gammopathies in GD I patients. In a non-GD population, MGUS patients showed an abnormal FLC ratio in 44% of cases, which was an important risk factor for progression to MM or a related disorder [40, 41]. We found that the three GD patients who had or would soon develop a B-cell malignancy could easily be identified by their strongly abnormal FLC κ/λ ratios. It was however, not possible to identify GD patients with MGUS on the basis of abnormal FLCs since 44% of GD patients with MGUS had both FLC levels as well as a FLC κ/λ ratio within the normal range. A slightly abnormal ratio was not predictive for progression to MM or MGUS in the single patient without a monoclonal gammopathy, even after 15 years of follow-up. None of the patients had a decrease in renal function, implicating that any FLC increase was indeed due to increased production by plasma cells.
MGUS patients are at risk for developing MM at a rate of 1% of patients per year. Accordingly, the prevalence of MM in GD I cohorts of 0.4–1.5% clearly exceeds the general prevalence of 0.02% (deduced from The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI)). The assumed diversity in relative risk of developing MM (5.9 and 51.1) can simply be explained by age differences in the cohorts: the highest number of MM patients was clearly established in the highest age groups.
In a recent consensus report on hematological manifestations of Gaucher disease, the higher incidence of multiple myeloma in Gaucher disease is acknowledged, and it is recommended that Gaucher patients should have their immunoglobulin profile determined at diagnosis and monitored every 2 years (patients <50 years) or every year (patients >50 years) [51].
Hypotheses on the pathogenesis of immunoglobulin disorders in GD have mainly focused on the accumulated glucocerebroside as the causative agent for chronic stimulation of macrophages surrounding Gaucher cells. Cytokines of macrophagic origin could in turn stimulate B cells, leading to a polyclonal gammopathy which may eventually transform, directly or via MGUS, into MM. Interestingly, plasma cells can be found in close contact with Gaucher cells, suggestive of local interaction (Fig. 4).
Fig. 4Bone marrow aspirate showing plasma cells surrounding a Gaucher cell in a patient with Gaucher disease type I and multiple myeloma
In our study, we measured IL-6, IL-10, and HGF, three factors important for differentiation and/or proliferation of plasma cells. In addition, IL-6 and IL-10 are also involved in inflammation and in abnormal bone remodeling in MM, aspects that are also present in GD. We found elevated IL-10 levels in most patients, especially those with a monoclonal gammopathy, and predominantly normal levels of IL-6 and HGF. Previous studies have also described increased IL-10 levels [4]. However, findings on IL-6 in GD I have been variable [4, 30–32]. Inconsistent results have also been reported for the pro-inflammatory cytokines TNF-α and IL-1β and the chemokine IL-8 [4, 30–33, 52]. Although not included in this study, increases have been described for the growth factor M-CSF (monocyte/macrophage-colony stimulating factor), the monocyte/macrophage activation marker sCD14 (soluble CD14) [31], sIL-2R (soluble IL-2 receptor), and IL-1RA (IL-1 receptor antagonist) [30]. The most impressive increase, both in our study as well in as in the study by Boot et al. [35], has been found for PARC, a chemokine that is assumed to be involved in B-cell differentiation by recruiting T cells and CD-38 negative mantle zone B lymphocytes to antigen-presenting cells.
In addition to the in vivo studies, it has been described that murine macrophages that were stimulated in vitro by glucocerebroside release the pro-inflammatory lymphocyte-activating factor (LAF = IL-1) in a dose-responsive manner. This effect was not seen on incubation with galactocerebroside, sphingomyelin, and ceramidetrihexoside [53]. Possibly, only minor glycolipid accumulation is sufficient for the development of B-cell derangement. In fact, a glucocerebrosidase deficient mouse (L444P homozygote) showed evidence of B-cell proliferation, as well as elevated serum IgG levels, even though storage cells were not found [54].
The heterogeneity in plasma cytokine levels that have been found could be explained by differences in patient populations and cytokine assays. In addition, it is not known whether plasma levels adequately reflect tissue levels. Nevertheless, clearly both pro- as well as anti-inflammatory cytokine levels are disturbed in GD I. It is unlikely that Gaucher cells directly induce inflammation, since Gaucher cells have a phenotype resembling anti-inflammatory alternatively activated macrophages. More likely, pro-inflammatory cytokines are produced by surrounding macrophages, with Gaucher cells compensating for the inflammatory compounds [55].
Now that it is generally accepted that ERT effectively reduces hepatosplenomegaly, cytopenia and skeletal disease, the focus should be shifted to new challenges, including the prevention and treatment of Gaucher associated co-morbidities. Novel data from our study suggest a beneficial effect of ERT on the occurrence and severity of gammopathies in GD patients. First, none of the patients developed MGUS during ERT. We did see progression of MGUS to amyloidosis and MM in two patients. However, since both patients developed these diseases relatively shortly after start of ERT, one could envision that irreversible changes leading to malignant transformation had already taken place, and could not be corrected by ERT. Second, we found that immunoglobulin levels either decreased or remained stable in both patients with and without a monoclonal gammopathy. The only patient with a strong increase in immunoglobulins (IgM) was a patient not using ERT. A clear influence of ERT on levels of FLC, IL-6, and IL-10 was not found in our study. A previous report also found a decrease in immunoglobulin levels during ERT, although only in patients with a polyclonal gammopathy, and not in patients with a monoclonal gammopathy [6]. Decreases in the levels of IL-10 [4], M-CSF, sCD1431, and PARC [35] have been described. Nevertheless, to draw firm conclusions on a possible positive effect of ERT on gammopathies in GD I, our presumptions should be confirmed in larger studies. This would require an international multicenter effort that should aim to establish whether indeed these long-term complications can be prevented by ERT and whether this would justify the early start of treatment.
In summary, there is a high prevalence in GD I of both polyclonal as well as monoclonal gammopathies, including MM. The risk of these diseases increases with age. Mechanisms causing gammopathies remain to be elucidated, but include the disturbance of cytokine levels involved in inflammation and B-cell function. ERT is likely to have a beneficial effect in preventing the occurrence and the progression of gammopathies.

free light chains
gaucher disease
mgus
multiple myeloma
cytokines


Breast cancer risk factors in relation to breast density (United States)

Objectives Evaluate known breast cancer risk factors in relation to breast density.
Introduction
Numerous studies have shown that breast density, as assessed through mammography, is an important breast cancer risk factor [1–5]. Relative to the lowest classification of breast density (fatty tissue), women with the highest classification (extreme density) may have a 2- to 6-fold increased risk of breast cancer [5–8]. In addition to its role in breast cancer risk, breast density reduces mammographic accuracy [9–12], potentially increasing the risk of a later stage breast cancer diagnosis.
Previous studies indicate that established breast cancer risk factors, including family history of breast cancer, age at first birth, parity, and postmenopausal hormone use, have similar associations with breast density. In contrast, the influence of age at menarche, which in most studies is inversely related to breast cancer risk [e.g., 13–15], remains uncertain. Some studies have found positive associations [6, 16, 17], at least one suggests an inverse association [18], and others found no relation between age at menarche on breast density [19, 20].
In an effort to clarify inconsistent results from previous studies, we evaluated established breast cancer risk factors in relation to breast density in a large population of women enrolled in a statewide mammography registry. Our intention was to determine whether characteristics associated with breast cancer risk were also related to breast density, a finding that would be consistent with the notion that density mediates breast cancer risk. We were particularly interested in assessing the influence of menarcheal age.
Methods
The New Hampshire Mammography Network (NHMN) registers all consenting women who undergo mammography at participating mammographic facilities in our state. Details of the registry have been described previously [21, 22]. For the present study, potentially eligible women were NH residents of ages 30–89, who had at least one mammogram registered in the NHMN from 1 May 1996 to 20 June 2002.
The epidemiological data used in this analysis arose from three sources: a self-administered questionnaire completed by the patient, a patient intake form administered face-to-face by the radiologic technologist, and a standardized clinical assessment form completed by the radiologist. The questionnaire collected height, weight, place of birth, ethnicity, marital status, education, insurance coverage, reason for the current visit, past history of clinical breast examinations and mammography, age at menarche, parity, and age at first birth. The questionnaire also queried women regarding the date of their last menstrual period and history of gynecological surgery. This information was used to classify women as premenopausal (still having periods naturally) or postmenopausal (periods had stopped permanently) either naturally, because of chemotherapy/radiation, or surgery). The patient intake form obtained date of birth, family history of breast cancer (in the subject’s mother, sister, daughter, or other relative), personal history of breast cancer, history of breast procedures, type of exam conducted at current visit, examination outcomes, recommendation for further work-up or follow-up, and current use of postmenopausal hormone therapy (HT). The clinical assessment form obtained the type of exam conducted at current visit, breast density, examination outcomes and recommendation for further work-up or follow-up. All three forms are completed during the woman’s first NHMN mammography visit. Patient intake and clinical assessment forms are also completed at subsequent mammography visits, and the questionnaire is updated as possible.
Breast density, the outcome variable, estimates the proportion of fibroglandular tissue in the breast, relative to fat. Breast density was recorded on the standardized clinical assessment form by interpreting radiologists using the American College of Radiology Breast Imaging Reporting and Data System® (BI-RADS®) classification (1 = fatty, 2 = scattered density, 3 = heterogeneously dense, and 4 = extremely dense) [23]. In the event of discordance in the density of the right and left breast, the woman was classified according to the higher density classification. Breast density readings were available for 162,933 (95.4%) of the 170,815 women who had at least one mammogram recorded in the registry.
To optimize temporal correspondence between the women’s characteristics and the classification of breast density, the statistical analyses were, when possible, based on the woman’s breast density on the date of the first recorded mammogram. When data for variables (other than HT use) were unavailable for the date of the mammogram, we searched forward in the NHMN records to retrieve replacement information corresponding to a subsequent mammography visit. Informative forward searches retrieved information from subsequent mammographic encounters occurring, on average, within 24 months of the index mammogram, and reduced missing values by 3–9%.
Current body mass index (BMI; kg/m2) was missing for 18,195 women, and the analyses were confined to 144,018 women for whom this measure was available. Included in the analytic sample were 131,480 (91%) women with a screening mammogram, 10,885 (8%) with a diagnostic mammogram, and 1,653 (1%) for whom the reason for the mammogram was not recorded. The majority of women, 136,283 (95%) had no personal history of breast cancer, 6,033 (4%) had a prior history of breast cancer based on NHMN records or the patient intake form, and 1,702 (1%) had unknown breast cancer status.
We used unconditional logistic regression analyses to generate odds ratios (OR) and 95% confidence intervals (CI) [24] for the association between factors and breast density, dichotomized as heterogeneously/extremely dense (dense) or fatty/scattered density (not dense). Statistical significance required a probability value of <0.05 (two-sided test). OR were computed using the cutpoints shown in the tables. Tests of trend and the corresponding OR were based on the categorical (age at menarche) or the continuous form of the variable (age, BMI, age at first birth, parity), in accordance with the method of data collection.
Because breast density was inversely associated with age (p for trend <0.0001) and current BMI (p for trend <0.0001), terms for these variables, using the continuous form, were included in all models. We found no evidence of confounding by the other variables shown in Table 1 (fully adjusted OR were within 10% of those adjusted for age and BMI). Model building began with terms representing the main effects, and included interaction terms involving age, BMI, and menopausal status as suggested by visual inspection of the stratified analyses. The presence of statistical interactions was formally tested using likelihood ratio tests. The interaction term representing BMI was defined as BMI ≥30 (high BMI), versus <30 (low BMI). The final multivariable model, based on all women, contained terms for age and BMI in their continuous form, BMI (high, low), family history of breast cancer, age at menarche, age at first birth, parity, menopausal status, current use of HT, and terms for the interactions involving BMI (high, low) and age at menarche, age at first birth, and parity, a term for the interaction involving age and current HT use, and a term for the interaction between age at first birth and menopausal status. We repeated the analyses in parous women, in women with a screening mammogram, and in women who did not have a personal history of breast cancer.
Table 1Distribution of women’s characteristics by breast density, dichotomized as dense versus not denseCharacteristics of study sample (n = 144,018)Not denseDensen%n%Age (years)    30–396,728810,01116    40–4924,9703129,95448    50–5921,5372713,59722    60–6915,160195,4339    70–7910,349133,0395    80–892,47637741    All women81,2205662,79844Education    <High school6,88782,7034    High school graduate27,4853417,29128    College graduate35,2704331,11350    Post graduate9,9711210,68117    Missing1,60721,0102Marital status    Not married25,2353116,90527    Married54,1816744,59871    Missing1,80421,2952Current BMI    <202,64036,47910    20–22.499,8481216,57026    22.5–24.9915,4801916,06026    25–27.4914,266189,81216    27.5–29.9912,689166,35310    30–34.9915,197195,3268    35+11,100142,1984Family history breast cancer    No55,5916841,23466    Yes25,6293221,56434Age at menarche    <116,13083,0945    1114,028179,32815    1220,9282615,83325    1322,0242718,28129    149,430128,53414    15+7,41396,83011    Missing1,26728981Age at first birtha    <2015,537218,55415    20–2430,5744118,67434    25–2916,6942214,27626    30–346,03886,70512    35+1,97232,4784    Missing4,52665,0269Parity    05,87987,08511    17,02397,66512    219,7912418,26229    317,6522213,03221    411,723146,90511    5+13,795175,8969    Missing5,35773,9536Menopausal status    Premenopausal29,1323637,55460    Postmenopausal46,8745821,89535    Missing5,21463,3495HT useb    No32,8296312,86751    Yes16,3383110,72242    Missing2,92161,6557aAge at first birth in parous womenbA small proportion of women (5.5%) who reported whether they used HT did not give their menopausal status
Results
In all women, scattered density (45%) was most frequently recorded, followed by heterogeneous density (34%), fatty breasts (12%), and extremely dense breasts (10%). The distribution of factors by breast density, classified as not dense versus dense, is shown in Table 1. In general, based on the cutpoints shown, there was a tendency for younger women, those with higher education, and married women to have denser breasts. Women with lower BMI also had greater breast density, consistent with our use of BI-RADS categories, which assess the proportion of fibroglandular tissue (versus fat) in the breast. Women with a family history of breast cancer, later menarcheal age, later age at first birth, and low parity had a tendency toward higher breast density. High breast density was more common in premenopausal than in postmenopausal women, and in women currently using HT compared to nonusers.
Age-stratified analyses, adjusted for age and BMI, showed a weak, positive influence of family history of breast cancer across all age groups assessed (Table 2). A small positive association between age at menarche and breast density appeared to vary by age, with weaker effects in the older and youngest age groups. There were no clear age-related patterns for either the positive effect of age at first birth or the inverse effect of parity or menopausal status. Current HT use, compared to nonuse, was inversely associated with breast density in the younger age groups, and positively associated in women of age 50 or more. In women of age 70 or more, those using HT, compared to nonusers, had twice the odds of having dense breasts.
Table 2Odds ratios (OR) and 95% confidence intervals (CI) for the association with breast density according to age groupCharacteristicaAge group<4040–4950–5960–6970+n = 16,739n = 54,924n = 35,134n = 20,593n = 16,628OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)Family history breast cancer    NoReferenceReferenceReferenceReferenceReference    Yes1.06 (0.99, 1.13)1.10 (1.06, 1.14) 1.07 (1.02, 1.13) 1.10 (1.03, 1.18) 1.11 (1.02, 1.20) Age at menarche    Overall OR (95% CI)b1.04 (1.01, 1.06)1.06 (1.05, 1.08)1.03 (1.02, 1.05)1.01 (0.99, 1.03)1.01 (0.98, 1.03)    p for trendb0.002<0.00010.00010.310.70    <11ReferenceReferenceReferenceReferenceReference    110.99 (0.85, 1.18)1.14 (1.04, 1.24)1.28 (1.15, 1.42)1.06 (0.90, 1.24)1.11 (0.89, 1.38)    121.04 (0.89, 1.21)1.15 (1.06, 1.25)1.26 (1.15, 1.39)1.12 (0.96, 1.30)1.18 (0.96, 1.45)    131.15 (0.98, 1.35)1.25 (1.16, 1.36)1.27 (1.15, 1.40)1.20 (1.03, 1.39)1.00 (0.82, 1.23)    141.21 (1.02, 1.44)1.34 (1.22, 1.46)1.37 (1.23, 1.53)1.23 (1.05, 1.46)1.22 (0.99, 1.51)    15+1.29 (1.08, 1.55)1.45 (1.32, 1.60)1.36 (1.21, 1.52)1.05 (0.89, 1.25)1.15 (0.92, 1.43)Age at first birthc    Overall OR (95% CI)d1.08 (1.06, 1.10)1.05 (1.04, 1.06)1.07 (1.06, 1.08)1.07 (1.05, 1.09)1.06 (1.05, 1.08)    p for trendd<0.0001<0.0001<0.0001<0.0001<0.0001    <20ReferenceReferenceReferenceReferenceReference    20–241.06 (0.95, 1.19)1.14 (1.08, 1.21)1.21 (1.14, 1.29)1.23 (1.12, 1.34)1.07 (0.93, 1.24)    25–291.09 (0.98, 1.22)1.19 (1.12, 1.26)1.40 (1.30, 1.51)1.51 (1.36, 1.68)1.28 (1.11, 1.49)    30–341.35 (1.18, 1.53)1.23 (1.15, 1.36)1.58 (1.42, 1.76)1.64 (1.39, 1.95)1.42 (1.18, 1.70)    35+1.67 (1.30, 2.16)1.48 (1.35, 1.63)1.85 (1.58, 2.17)1.90 (1.44, 2.49)1.47 (1.14, 1.91)Parity    Overall OR (95% CI)d0.86 (0.84, 0.88)0.91 (0.90, 0.93)0.90 (0.86, 0.91)0.89 (0.87, 0.90)0.87 (0.85, 0.89)    p for trendd<0.0001<0.0001<0.0001<0.0001<0.0001    0ReferenceReferenceReferenceReferenceReference    10.74 (0.65, 0.86)0.90 (0.83, 0.87)0.87 (0.79, 0.97)0.87 (0.73, 1.05)0.88 (0.73, 1.05)    20.58 (0.52, 0.66)0.73 (0.69, 0.78)0.77 (0.71, 0.85)0.77 (0.67, 0.89)0.66 (0.57, 0.78)    30.48 (0.42, 0.55)0.69 (0.65, 0.74)0.67 (0.61, 0.73)0.65 (0.57, 0.76)0.59 (0.51, 0.69)    40.49 (0.42, 0.57)0.61 (0.57, 0.66)0.61 (0.55, 0.67)0.55 (0.48, 0.64)0.49 (0.42, 0.58)    5+0.38 (0.33, 0.45)0.60 (0.55, 0.66)0.52 (0.47, 0.58)0.49 (0.42, 0.56)0.39 (0.33, 0.46)Menopausal status    PremenopausalReferenceReferenceReference    Postmenopausal0.69 (0.60 , 0.79 )0.71 ( 0.68, 0.75)0.71 (0.67, 0.75)N/AN/AHT use    NoReferenceReferenceReferenceReferenceReference    Yes0.69 (0.57, 0.84)0.80 (0.75, 0.86)1.39 (1.32, 1.46)1.82 (1.70, 1.95)2.02 (1.84, 2.22)aAdjusted for age and current BMI as continuous variablesbBased on the cutpoints showncAmong parous womendBased on the continuous form of the variable
Analyses stratified on BMI suggested that the influence of some factors was less evident in women of BMI ≥30 (high BMI) than in those of BMI <30 (low BMI) (Table 3). In particular, the positive influence of age at menarche and age at first birth, and the inverse influence of parity were least apparent in the highest BMI group. The data suggested an inverse effect of being postmenopausal, relative to premenopausal, which decreased consistently across the BMI groupings, but the change in OR from the lowest to the highest BMI group was slight. Although the influence of current HT use fluctuated somewhat over BMI groups, there were no obvious patterns.
Table 3Odds ratios (OR) and 95% confidence intervals (CI) for the association with breast density according to BMI groupCharacteristicaCurrent BMI <22.522.5–25.4925.5–27.4927.5–29.99≥30n = 35,547n = 37,153n = 18,744n = 18,775n = 33,799OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)OR (95% CI)Family history of breast cancer    NoReferenceReferenceReferenceReferenceReference    Yes1.09 (1.03, 1.14)1.03 (0.99, 1.08) 1.08 (1.01, 1.15) 1.15 (1.08, 1.23) 1.09 (1.03, 1.15) Age at menarche    Overall OR (95% CI)b1.04 (1.03, 1.06)1.05 (1.03, 1.06)1.04 (1.02, 1.06)1.03 (1.01, 1.05)1.01 (0.99, 1.03)    p for trendb<0.0010.0010.0010.0040.25    <11ReferenceReferenceReferenceReferenceReference    111.25 (1.09, 1.43)1.17 (1.05, 1.31)1.13 (0.98, 1.31)1.20 (1.05, 1.38)1.10 (0.99, 1.22)    121.36 (1.20, 1.55)1.26 (1.13, 1.40)1.10 (0.96, 1.27)1.16 (1.01, 1.32)1.09 (0.98, 1.21)    131.43 (1.26, 1.63)1.33 (1.20, 1.48)1.16 (1.01, 1.33)1.21 (1.06, 1.38)1.06 (0.94, 1.16)    141.54 (1.34, 1.75)1.44 (1.28, 1.61)1.25 (1.08, 1.45)1.19 (1.02, 1.38)1.16 (1.02, 1.31)    15+1.52 (1.32, 1.75)1.43 (1.27, 1.61)1.26 (1.07, 1.48)1.33 (1.13, 1.56)1.06 (0.93, 1.21)Age at first birthc    Overall OR (95% CI)d1.08 (1.07, 1.09)1.07 (1.06, 1.08)1.07 (1.06, 1.09)1.06 (1.04, 1.07)1.03 (1.02, 1.04)    p for trendd<0.0001<0.0001<0.0001<0.0001<0.0001    <20ReferenceReferenceReferenceReferenceReference    20–241.21 (1.13, 1.31)1.20 (1.13, 1.28)1.15 (1.05, 1.26)1.13 (1.03, 1.23)1.04 (0.96, 1.11)    25–291.39 (1.29, 1.51)1.34 (1.25, 1.44)1.21 (1.10, 1.34)1.31 (1.19, 1.45)1.10 (1.01, 1.19)    30–341.45 (1.32, 1.60)1.55 (1.42, 1.70)1.40 (1.23, 1.59)1.42 (1.25, 1.63)1.11 (0.99, 1.24)    35+1.79 (1.56, 2.06)1.84 (1.61, 2.09)1.88 (1.56, 2.28)1.62 (1.33, 1.97)1.29 (1.08, 1.53)Parity    Overall OR (95% CI)d0.86 (0.85, 0.87)0.88 (0.87, 0.89)0.90 (0.88, 0.92)0.91 (0.89, 0.93)0.93 (0.92, 0.95)    p for trendd<0.0001<0.0001<0.0001<0.0001<0.0001    0ReferenceReferenceReferenceReferenceReference    10.75 (0.67, 0.84)0.80 (0.73, 0.89)0.93 (0.81, 1.08)1.13 (0.98, 1.30)0.95 (0.85, 1.07)    20.61 (0.55, 0.66)0.67 (0.62, 0.73)0.74 (0.66, 0.84)0.90 (0.79, 1.02)0.85 (0.78, 0.94)    30.52 (0.48, 0.58)0.58 (0.53, 0.63)0.68 (0.60, 0.77)0.80 (0.71, 0.91)0.81 (0.73, 0.89)    40.44 (0.39, 0.48)0.52 (0.47, 0.57)0.60 (0.52, 0.69)0.71 (0.62, 0.81)0.75 (0.68, 0.84)    5+0.37 (0.34, 0.41)0.44 (0.40, 0.48)0.52 (0.45, 0.60)0.64 (0.56, 0.74)0.66 (0.59, 0.74)Menopausal status    PremenopausalReferenceReferenceReferenceReferenceReference    Postmenopausal0.67 (0.63, 0.72)0.68 (0.64, 0.72)0.71 (0.65, 0.77)0.73 (0.67, 0.79)0.75 (0.70, 0.80)HT use    NoReferenceReferenceReferenceReferenceReference    Yes1.23 (1.15, 1.31)1.33 (1.26, 1.42)1.34 (1.23, 1.46)1.41 (1.30, 1.55)1.34 (1.25, 1.45)aAdjusted for age and current BMI as continuous variablesbBased on the cutpoints showncAmong parous womendBased on the continuous form of the variable
Analyses stratified by menopausal status revealed largely similar results for most variables, but the relationship between age at first birth and breast density was stronger in the postmenopausal women (Table 4).
Table 4Odds ratios (OR) and 95% confidence intervals (CI) for the association with breast density according to menopausal statusCharacteristicaMenopausal status PremenopausalPostmenopausaln = 66,686n = 68,769OR (95% CI)OR (95% CI)Family history of breast cancer    NoReferenceReference    Yes1.09 (1.05, 1.13)1.08 (1.04, 1.12) Age at first birth    Overall OR (95% CI) 1.05 (1.04, 1.06)1.03 (1.02, 1.04)    p for trend<0.0001<0.0001    <11ReferenceReference    111.12 (1.03, 1.21)1.15 (1.06, 1.25)    121.12 (1.04, 1.22)1.20 (1.12, 1.30)    131.22 (1.13, 1.32)1.21 (1.12, 1.31)    141.31 (1.21, 1.43)1.29 (1.18, 1.40)    ≥151.40 (1.28, 1.53)1.25 (1.15, 1.36)Age at first birthb    Overall OR (95% CI)c1.05 (1.05, 1.06)1.07 (1.06, 1.08)    p for trendc<0.0001<0.0001    <20ReferenceReference    20–241.07 (1.01, 1.13)1.21 (1.15, 1.27)    25–291.11 (1.05, 1.17)1.41 (1.33, 1.49)    30–341.21 (1.13, 1.29)1.57 (1.45, 1.71)    ≥351.48 (1.35, 1.62)1.77 (1.57, 2.00)Parity    Overall OR (95% CI)c0.90 (0.87, 0.91)0.88 (0.88, 0.89)    p for trendc<0.0001<0.0001    0ReferenceReference    10.87 (0.81, 0.93)0.86 (0.79, 0.93)    20.70 (0.66, 0.74)0.72 (0.68, 0.77)    30.63 (0.60, 0.68)0.62 (0.58, 0.67)    40.57 (0.53, 0.62)0.55 (0.51, 0.59)    ≥50.52 (0.49, 0.57)0.46 (0.43, 0.50)HT use    NoNAReference    YesNA1.47 (1.41, 1.52)aAdjusted for age and current BMI as continuous variablesbAmong parous womencBased on the continuous form of the variable
We assessed risk factors and potential interactions in a multivariable model (Table 5). Only one factor, family history of breast cancer, which showed a weak but significant positive effect (OR 1.09; 95% CI 1.05–1.14) on breast density, was not involved in an interaction. The possible interaction involving age and age at menarche, suggested by the age-stratified analyses, was not statistically significant (p = 0.10). A statistically significant interaction was found between current HT use and age (p < 0.0001). Interactions were also present between BMI and age at menarche (p = 0.04), age at first birth (p < 0.0001), and parity (p = 0.01). We also noted a significant interaction between menopausal status and age at first birth (p = 0.004), but the coefficients were inconsistent across categories of age at first birth. When the interaction between menopausal status and age at first birth was omitted from the multivariable model, results for the remaining terms were essentially unchanged. A possible interaction involving age, menopausal status, and HT was not significant (p = 0.10).
Table 5Betas (β) and standard errors (SE) for the association between factors and breast density in all women and in subgroupsaCharacteristicAll women n = 144,018Women without breast cancer n = 136,283β (SE)β (SE)Intercept4.82 (0.17)4.88 (0.17)Age−0.04 (0.001)−0.04 (0.001)Current BMI−0.12 (0.003)−0.12 (0.003)Family history of breast cancer0.09 (0.02)0.09 (0.02)BMI <30−0.53 (0.11)−0.53 (0.11)Postmenopausal−0.43 (0.09)−0.43 (0.09)HT use−1.22 (0.11)−1.25 (0.11)Age at menarche    15+−0.01 (0.10)−0.03 (0.11)    140.04 (0.10)0.01 (0.10)    13−0.01 (0.08)−0.03 (0.08)    120.08 (0.08)0.06 (0.08)    110.04 (0.08)0.02 (0.08)BMI <30 * Age at menarche    15+0.30 (0.12)0.32 (0.12)    140.24 (0.11)0.28 (0.11)    130.22 (0.09)0.24 (0.10)    12 0.12 (0.09)0.15 (0.10)    110.11 (0.10)0.14 (0.10)Age at first birth    Nulliparous0.38 (0.16)0.13 (0.17)    35+0.19 (0.22)0.25 (0.23)    30–34−0.19 (0.15)−0.18 (0.15)    25–29−0.11 (0.12)−0.12 (0.12)    20–240.14 (0.11)0.13 (0.11)BMI <30 * Age at first birth    Nulliparous0.57 (0.10)0.59 (0.11)    35+0.41 (0.18)0.40 (0.18)    30–340.31 (0.11)0.32 (0.12)    25–290.12 (0.08)0.15 (0.08)    20–240.06 (0.06)0.06 (0.06)Postmenopausal * Age at first birth    Nulliparous0.09 (0.15)0.10 (0.15)    35+−0.08 (0.19)−0.14 (0.19)    30–340.27 (0.13)0.26 (0.13)    25–290.25 (0.11)0.23 (0.11)    20–24−0.05 (0.10)−0.04 (0.11)Paritya    10.29 (0.09)0.28 (0.09)    20.22 (0.07)0.23 (0.07)    30.14 (0.07)0.17 (0.07)    40.09 (0.07)0.11 (0.08)BMI <30 * Parityb    10.30 (0.10)0.30 (0.10)    20.22 (0.07)0.20 (0.08)    30.18 (0.08)0.15 (0.08)    40.09 (0.08)0.07 (0.09)Age * HT use0.03 (0.002)0.03 (0.002)aBased on a multivariable model containing all terms shown in the tablebThe nulliparous parameter and its interaction with BMI were set to 0, since these variables were linear combinations of other variables
We repeated the analyses in the subgroup of women who did not have a personal history of breast cancer, and found similar results (Table 5). The findings were also comparable when the analyses were confined to parous women, or to those with a screening mammogram (data not shown).
Discussion
Evidence accumulating for nearly 30 years supports the association between breast density and breast cancer [1–5]. Although the notion remains controversial, breast density may be a biomarker of risk [25]. In addition to its influence on breast cancer risk, breast density reduces the accuracy of screening mammography [11, 12, 26, 27], particularly in younger women [9] who tend to have denser breasts [28]. Perhaps as a direct consequence of reduced screening accuracy, breast density is associated with increased risk of interval breast cancers [11], with an adverse impact on breast cancer prognosis.
Most studies of breast cancer risk have shown an inverse effect of age at menarche [13–15, 29], but previous studies of the relationship between age at menarche and breast density have produced inconsistent results. A positive association was seen in two studies [16, 18], including a large HMO population of nearly 30,000 women in Seattle [16]. Studies of breast cancer family members [17], Singaporean women [20], and Hispanic women [19] found no association between menarcheal age and breast density. Findings from the HMO-based study suggested the positive effect of age at menarche was stronger in the youngest and oldest age groups [16], whereas a study of nearly 5,000 women in Guernsey found significant positive effects only in postmenopausal women [6]. In contrast, our age-stratified analyses suggested weaker effects in the oldest age groups, although the interaction involving age and age at menarche was not statistically significant. Also in this study, analyses stratified on BMI suggested that age at menarche was positively associated with breast density in most BMI groups, but the association was tenuous in women with high BMI, and the interaction involving age at menarche and high BMI was statistically significant. A positive association between age at menarche and breast density, even if confined to women with lower BMI, seems paradoxical, given the usual inverse association between age at menarche and breast cancer and the strong positive association between breast density and breast cancer risk.
Consistent with some [16–18, 20, 30, 31], but not all [6, 19] previous efforts, our findings show that age at first birth and parity are generally associated with breast density in a pattern resembling known associations with breast cancer risk. However, our stratified analyses indicate that the influence of the reproductive variables is less pronounced in women with high BMI, and our modeling results confirmed interactions involving BMI and both variables. These findings, along with our findings for age at menarche, are consistent with the possibility that hormonal or reproductive events are less influential in heavier women, whose circulating hormone levels may be influenced by conversion in peripheral adipose tissue. We also noted a stronger positive influence of age at first birth in postmenopausal women, although a previous study of Native American women found stronger effects in premenopausal women [30]. Our sample was large, and the multivariable results were inconsistent across categories of age at first birth; thus, it is possible the interaction between menopausal status and age at first birth was due to statistical artifact rather than true effect modification.
Only one variable, family history of breast cancer, was not involved in interactions with age, BMI, or menopausal status. Although previous studies have not shown an effect of family history on breast density [3, 6, 19], this is likely due to limited power to detect a weak association. The modest inverse effect of menopausal status has been noted previously [19, 20, 30]. Most previous reports, although not all [20], found a positive association between use of HT and breast density [16, 17, 19, 30, 32, 33], resembling the well-known association between use of these hormones and breast cancer risk. The large size of our study allowed an assessment of HT use in young postmenopausal women, and these analyses showed a modest but significant inverse effect in postmenopausal women less than 50 years of age, a phenomenon that has no clear explanation. In women of age 50 or more, the positive effect of HT on density increased with age, perhaps reflecting a corresponding decrease in density in untreated women in the same age group. Consistent with our findings, at least two previous studies of breast density showed an increasing effect of HT use when examined over increasing age groups [16, 32]. A prospective study of breast cancer risk also noted stronger HT effects in older women [37], although this is not always seen [36]. The age-related increase observed in our study could potentially reflect a longer duration of HT use, but at least two studies have shown that most of the increase in breast density occurs soon after HT initiation [33, 34], and duration of use was not associated with increased breast density in the HMO study [16]. In contrast, the positive influence of HT on breast cancer risk is usually observed for current/recent and long-term use [35–40]. While speculative, it is possible that sustained breast density associated with long term HT use mediates the relationship between HT and breast cancer risk. Finally, our data did not indicate a stronger effect of HT on breast density in leaner woman, but a few studies [38–40], including a collaborative analysis of 51 studies [38], suggested a stronger association between HT and breast cancer risk in leaner women.
Although the type of HT used (estrogen alone or estrogen combined with progesterone) was not assessed in our study, a possible role of progesterone is suggested by reports that breast density is greater during the luteal phase of the menstrual cycle [41–43]. In addition, at least two studies have found substantially greater changes in parenchymal patterns in women initiating use of a combined estrogen plus progesterone hormone regimen, as opposed to single agent estrogen [33, 34]. Results from the Women’s Health Initiative randomized clinical trials of postmenopausal hormones also indicate that the increased risk of breast cancer is due to the combined regimen [44] rather than single agent estrogen [45].
Strengths of our study include the large size of our sample, allowing analyses stratified by relatively refined age and BMI groups, which has not been possible in most previous studies, and good representation of the underlying population. Epidemiologic data were obtained on the time of the mammographic visit, ensuring updated information, and importantly, a high level of correspondence between use of hormone replacement therapy and the assessment of breast density. Limitations of our study include 11% of women for whom BMI is missing, reliance of self-report for BMI, and a lack of information regarding the type and duration of HT use. Our definition of family history of breast cancer included second degree relatives, which may have attenuated the effect of this variable. Also, the BI-RADS scores are qualitative, as opposed to digital quantification of density, and were applied by community radiologists, who despite being trained to use this system, may apply it differently. Nevertheless, our general findings in terms of the direction of effects for reproductive factors and HT use were compatible with those of most previous studies.
In conclusion, our results, based on the largest study to date, confirm earlier findings that most established breast cancer risk factors behave similarly in relation to breast density, consistent with the notion that breast density mediates breast cancer risk. However, our data indicated an inverse effect of HT in younger women, and a positive influence in older women, which has not been reported previously. We also noted effect modification by BMI, in which the positive effects of age at menarche and age at first birth, and the inverse effects of parity were less apparent in heavier women. Further investigation, including biological studies, may elucidate the complex interrelationships of hormones, BMI, breast density, and breast cancer and potentially offer opportunities for breast cancer prevention.

hormone replacement therapy
reproductive history
mammographic breast density