Skip to main content
PMC home page
Human Reproduction (Oxford, England) logoLink to Human Reproduction (Oxford, England)
. 2010 Apr 10;25(6):1561–1568. doi: 10.1093/humrep/deq090

Infant leukemia and parental infertility or its treatment: a Children's Oncology Group report

Susan E Puumala 1, Logan G Spector 2,*, Melanie M Wall 3, Leslie L Robison 4, Nyla A Heerema 5, Michelle A Roesler 1, Julie A Ross 2
PMCID: PMC2873174  PMID: 20382971

Abstract

BACKGROUND

Little is known about the potential risk factors for infant leukemia. With its very young age at diagnosis, exposures occurring in the perinatal period are suspected. Parental infertility and infertility treatment have been studied with regard to childhood cancer in general, but rarely in individual cancer subtypes.

METHODS

A case–control study of infant leukemia was conducted through the Children's Oncology Group, including cases diagnosed from January 1996 to December 2006 and controls selected through random digit dialing and birth certificate tracing. Maternal phone interviews were conducted to obtain information about infertility, infertility treatment and demographic factors. All cases as well as subgroups defined by mixed lineage leukemia (MLL) translocation status and leukemia subtype were examined. Statistical analysis was performed using multivariate logistic regression models.

RESULTS

No significant associations between infertility or its treatment and combined infant leukemia were found. In subgroup analyses, there was a significant increase in the risk of MLL− leukemia for children born to women not trying to conceive compared with those trying for <1 year for all types combined [odds ratio (OR) = 1.62, 95% confidence interval (CI) = 1.01–2.59] and for acute lymphoblastic leukemia (OR = 2.50, 95% CI = 1.36–4.61).

CONCLUSIONS

There were no positive associations between parental infertility or infertility treatment and infant leukemia. While this is the largest study to date, both selection and recall bias may have impacted the results. However, for infant leukemia, we can potentially rule out large increases in risk associated with parental infertility or its treatment.

Keywords: infant leukemia, infertility, childhood cancer, MLL translocations

Introduction

Infant leukemia, diagnosed prior to 1 year of age, is a rare subtype of childhood leukemia with an incidence of ∼40 cases per million infants in the USA in 1992–2004 (Linabery and Ross, 2008). Leukemia in infants differs from leukemia in older children with variation in type of diagnosis [acute myeloid (AML) and acute lymphoblastic (ALL)], clinical symptoms, tumor genetics and response to treatment (Zweidler-McKay and Hilden, 2008). Another feature which sets infant leukemia apart is that the majority of cases have somatic mutations in the mixed lineage leukemia (MLL) gene. In infant leukemia, ∼80% of ALL and 50% of AML have a recombination event within the MLL gene (Reaman, 2003). Mutations in the MLL gene appear to occur in utero since rearrangements in the MLL gene have been identified in neonatal blood spots collected at birth (Gale et al., 1997; Greaves, 2003). The distinction between subtype and MLL status is important in infant leukemia, since many studies indicate a potential difference in risk factors for disease based on these subgroups, thus most studies examine each combination of subtype and MLL status separately. Given its differences from leukemia in older children, it is necessary to study this subtype separately in order to discover factors which might lead to improved treatment or lower incidence rates. Also, since the relevant exposure(s) most likely occurs in utero, the study of infant leukemia could help uncover important information about carcinogenesis in general and specifically about carcinogenic exposures before or during pregnancy (Greaves, 2005).

Little is known from epidemiological studies about infertility or its treatment as possible risk factors for infant leukemia. One study of leukemia in infants (up to 18 months of age at diagnosis) looked at medication recorded in the mother's medical record during pregnancy (Ross et al., 2003). This study found a non-significant inverse association with clomiphene, an ovarian stimulant, but the analysis was only based on two cases with reported use. Other studies examining indication or treatment of infertility and childhood leukemia overall have been mixed with some finding an indication of increased risk (van Steensel-Moll et al., 1985; Roman et al., 1997; Schuz et al., 1999; Puumala et al., 2007) and others finding no association (Cnattingius et al., 1995; Shu et al., 2002; Wen et al., 2002; Shaw et al., 2004). However, all suffered from relatively low power, since there were few cases or controls reporting infertility or infertility treatment.

Many studies have been conducted to examine the association between infertility and infertility treatment and childhood cancer. Although several large cohort studies have examined assisted reproductive technology (ART) and childhood cancer, few have looked at specific diagnoses. Most studies have found that the total number of cancer cases observed in a cohort of children born after ART were similar to what would be expected (Doyle et al., 1998; Bruinsma et al., 2000; Lerner-Geva et al., 2000; Klip et al., 2001; Kallen et al., 2005; Lidegaard et al., 2005), but have been based on small numbers of cancers overall. All studies have examined children with similar ART procedures; primarily in vitro fertilization (IVF) or IVF with intracytoplasmic sperm injection (ICSI). Children have been followed to observe a cancer diagnosis before the age 15 with average follow-up times ranging from 3 years 9 months to 8.6 years. Case–control studies have examined the history of infertility or infertility treatment in general and have found some increases in risk for leukemia, hepatoblastoma, neuroblastoma and retinoblastoma (Lightfoot et al., 2005; McLaughlin et al., 2006; Puumala et al., 2007). Rarer diagnoses or subsets of diagnoses have not been frequently examined with respect to infertility or infertility treatment.

Infant leukemia and parental infertility or its treatment are potentially linked through aberrant epigenetic mechanisms. Several studies have found that children born after ART are more prone to severe disorders caused by abnormal genomic imprinting (Olivennes et al., 2001; Cox et al., 2002; DeBaun et al., 2003; Gicquel et al., 2003; Maher et al., 2003; Halliday et al., 2004; Chang et al., 2005; Sutcliffe et al., 2006; Bowdin et al., 2007; Doornbos et al., 2007). Other studies have found that epigenetic defects after ART could be due to parental infertility rather than ART treatment (Ludwig et al., 2005; Doornbos et al., 2007). Although imprinting disorders in these children are still very rare in an absolute sense, the fact that imprinting diseases are more common could indicate a more widespread disruption of epigenetic mechanisms. These disruptions could potentially manifest themselves as an increased propensity for childhood leukemia which has been shown to be epigenetically mediated (Stam et al., 2006).

Taken as a whole, the previous literature supports further investigation of infertility and/or infertility treatment and risk of infant leukemia. Thus, the current study addresses this need while adding to the sparse literature on infant leukemia and infertility and is the first study to look at this exposure within MLL subtypes.

Materials and Methods

Data for this analysis were from a Children's Oncology Group (COG) case–control study of infant leukemia. Cases were collected in two phases for this study. Both phases required cases to have a confirmed diagnosis of acute leukemia prior to 1 year of age. Patients could be diagnosed with either ALL or AML. Cases who died before the study period were eligible for study participation, since the main source of data collection was the child's mother. Children were eligible if they were not diagnosed with Down syndrome, had a biological mother who spoke English or Spanish (phase II only), had a biological mother available by telephone and were treated or diagnosed at a participating COG institution in the USA or Canada. Once cases were identified, the treating physician was contacted and asked to provide permission to contact the child's mother or parental consent to contact was obtained directly. Mothers with physician approval or consent were sent a letter explaining the study and notifying them that they would be contacted by phone. The first phase of recruitment included cases diagnosed between 1 January 1996 and 13 October 2002; the second phase included cases diagnosed between 1 January 2003 and 31 December 2006. In Phase I, 348 cases were confirmed eligible from 126 participating COG institutions and 240 of these (69%) completed interviews. In Phase II, 345 cases were identified by 133 participating COG institutions as potentially eligible for the study. Of those eligible, 203 (59%) completed interviews.

Controls were selected in two phases for this study coinciding with the case periods. In Phase I, controls were selected though random digit dialing (RDD). Numbers were generated using a modification of the methods proposed by Waksberg (Robison and Daigle, 1984). Potential phone numbers were generated from case phone numbers at diagnosis. The area code and exchange of the case phone number were retained and the last four digits were randomly selected in order to obtain a control number. For each number, up to nine contact attempts were performed. If the number resulted in no contact, a refusal or an ineligible household, subsequent numbers were generated until an eligible control agreed to participate in the study. The mother's name and address were then obtained along with permission to send a letter. Controls were obtained from 25 516 telephone numbers selected using RDD, of which 11 713 were identified as residential numbers. Using the method outlined by Slattery et al. (1995), the RDD household screening response rate was 67%. Maternal telephone interviews were successfully completed for 254 out of 430 potential eligible controls, giving a field response rate of 59% and an overall response rate of 40%.

Phase II controls were selected through state birth registries. Sixteen states that could release birth records and registered a large number of infant leukemia cases in Phase I were approached about participation, 15 of which ultimately provided rosters of birth certificate (BC) data. Controls were frequency matched to cases on year of birth and region of residence based on Phase I case distribution. The 15 states were allocated to regions to facilitate geographical matching. An introductory letter was sent to 270 potential controls providing information about the study and indicating that an interviewer would contact them by phone. Phone contact was attempted for each potential control successively until an eligible control agreed to participate. In both phases, controls were required to have a biological mother who spoke English or Spanish (Phase II) and was available by telephone. Initial contact letters were sent to mothers of 270 children from randomly selected BCs of which 267 were found eligible. A total of 70 mothers completed the interview and one partially completed the interview, giving a total field response rate of 27% (71/267).

Information was collected for cases and controls through maternal interview. The maternal interview included questions about pregnancy history, maternal exposures during pregnancy with the participating (index) child, family history of cancer and other diseases, and information about the medical history of the mother. Several questions about infertility and infertility treatment were also asked, including length of time to index pregnancy, history of infertility (more than 1 year of trying without becoming pregnant), history of doctor's visits by mother or index biological father due to non-pregnancy, specific infertility treatment, use of female hormones for ovulation stimulation, and use of female hormones for infertility or conditions related to infertility.

MLL status was determined using the case's file from his or her initial COG institution. Information about molecular or cytogenetic testing for MLL gene rearrangements at the time of diagnosis was collected and reviewed by three independent reviewers. Infants were ultimately classified into three classes: MLL+ by molecular or cytogenetic methods, MLL− by molecular or cytogenetic methods, or not enough information to determine MLL status. A total of 69 cases had unknown MLL status after review.

The institutional review boards at the University of Minnesota and the participating COG institutions approved this study. In addition, health departments for the states providing BCs also reviewed and approved this study. All participants provided informed consent prior to participating in the study.

Exposures of interest in this study included maternal age (continuous), history of recurrent pregnancy loss (2 or more, 1 or none), time to index pregnancy (not trying, <1 year, ≥1 year), specific infertility treatment (medication, surgery, or other) (yes/no), use of ovulation-stimulating drugs before or during early pregnancy (yes/no).

In addition, a composite infertility variable was constructed based on latent class analysis (LCA), including maternal age, history of recurrent pregnancy loss (2 or more, 1 or none), history of infertility (more than 1 year of trying without becoming pregnant), visit to a doctor by mother or index biological father due to non-pregnancy (yes/no), and use of ovulation-stimulating drugs before or during early pregnancy (yes/no) (Formann and Kohlmann, 1996). This method was used since infertility is difficult to measure and a couple's ‘true’ infertility status is usually unknown. The LCA combines information from many different variables in order to obtain a better measurement of the unknown ‘true’ infertility status. Models with and without maternal age were explored in order to determine if the effect of infertility was only through maternal age or if there was an independent risk factor for infertility apart from age.

The analysis used the conditional independence model which assumes that the observed variables are independent of one another given class membership. This means that once infertility (as defined by the LCA model) is taken into account, the observed variables used to measure infertility are not related to one another. Since more than three observed variables were used, the model was identifiable and no additional constraints were needed. LCA was conducted using M-Plus software (Muthen and Muthen, 1998–2004). Both two and three class models were fit to the data and model selection relied on the BIC value for model fit. Predicted class membership was categorized and used as a predictor in a logistic regression model along with potential confounders.

Descriptive methods were used to assess the appropriateness of statistical analysis and the functional forms of the relationship between exposures and outcome. Multivariate models were constructed after considering matching variables as well as confounders including maternal age (continuous), maternal education, maternal race, smoking during pregnancy, household income, gestational age and birthweight. Exposures were included in the logistic analysis if at least four cases and controls were represented in all exposure categories. Birth year was included in all analysis as a matching factor. Results are reported as odds ratios (ORs) and 95% confidence intervals (CIs).

In addition to the combined leukemia analysis, subgroups based on subtype (ALL, AML) and by MLL status (MLL+, MLL−) were examined separately. Each subgroup of cases was compared with the entire control set since there was no basis for selecting a subset of control children and using all of the controls maximized power. Model-based analysis was performed if there were at least two cases and controls in each exposure category within the subgroup. All logistic regression analyses were performed using SAS 9.1 (SAS Institute, Cary, NC, USA).

Results

A total of 443 cases and 324 controls completed interviews when both phases of recruitment were combined. We had limited data on non-respondents. As reported previously, in Phase II, non-participants had lower levels of maternal education, lower maternal age and marginally lower birthweight compared with our participants (Puumala et al., 2009). For cases, only gender and race were available for both phases. While the percentage of males did not differ (44.2 versus 49.2, P = 0.2), there was a significantly lower percentage of whites in the non-respondent group (64.0 versus 78.3, P < 0.01).

Descriptive statistics for baseline characteristics of participants are presented in Table I. Case mothers were more likely to be Hispanic, have lower education levels and have lower household income than control mothers. Case children were similar to control children with respect to gender, gestational age and birthweight.

Table I.

Baseline characteristics.

Controls
 Cases
 ORa 95% CI
n (%) n (%)
Maternal characteristics
 Race/Ethnicity
  White 273 (84.5) 334 (75.6) Ref
  Black 18 (5.6) 18 (4.1) 0.65 0.31–1.33
  Hispanic 15 (4.6) 55 (12.4) 2.62 1.42–4.83
  Other 17 (5.3) 35 (7.9) 1.66 0.88–3.11
 Education
  ≤High school 91 (28.2) 149 (33.7) 1.46 1.00–2.13
  Some post-HS 112 (34.7) 125 (28.3) Ref
  College graduate 120 (37.2) 168 (38.0) 1.10 0.76–1.58
 Household income
  ≤ $30 000 95 (29.6) 157 (35.8) Ref
  $30 001–$75 000 145 (45.2) 189 (43.1) 0.80 0.56–1.13
  >$75 000 81 (25.2) 93 (21.2) 0.56 0.37–0.84
 Smoking during pregnancy
  Yes (at least 1 cig/day) 65 (20.1) 74 (16.7) 0.90 0.61–1.32
  No 258 (79.9) 368 (83.3) Ref
Child characteristics
 Gender
  Male 156 (48.1) 218 (49.2) Ref
  Female 168 (51.9) 225 (50.8) 0.97 0.72–1.31
 Gestational age (weeks)
  <37 24 (7.4) 32 (7.2) 0.79 0.44–1.43
  37–40 259 (79.9) 360 (81.3) Ref
  ≥41 41 (12.7) 51 (11.5) 1.03 0.65–1.62
 Birthweight (g)
  Mean (SD) 3436.33 (591.76) 3477.26 (572.45) 1.12b 0.98–1.28
Open in a new tab

aLogistic regression models were adjusted for year of birth (quartiles).

bPer 500 g increase.

The final selection of confounders included household income as well as maternal race, age, education, smoking during pregnancy, birthweight and gestational age. Although geography was a matching variable, it did not have an impact on the overall results and was not included in the final models (data not shown).

Multivariate analysis for the latent class-derived infertility variable is presented in Table II. The classification did not change based on whether or not age was included in the model. No statistically significant associations were found either in combined leukemia or within any subgroup. Table III presents the results for measures of infertility which have been examined in previous studies. There was a statistically significant increased risk of MLL− infant leukemia for those who reported not trying to become pregnant compared with those trying for <1 year (OR = 1.62, 95% CI = 1.01–2.59). Subgroup analyses revealed that this statistically significant association was confined to the ALL MLL− subgroup (OR = 2.50, 95% CI = 1.36–4.61). For the AML subgroup, while not significant, the OR was <1 in women who reported using ovulation-stimulating drugs prior to or before knowledge of pregnancy (OR = 0.44, 95% CI = 0.13–1.44).

Table II.

Association between infant leukemia and latent class measure of infertility.

Controls
 Cases
 ORa 95% CI MLL+
 ORa 95% CI MLL−
 ORa 95% CI
n (%) n (%) n (%) n (%)
Combined leukemia Latent class infertility
 No 266 (82.1) 372 (84.0) Ref 192 (84.2) Ref 121 (82.9) Ref
 Yes 58 (17.9) 71 (16.0) 0.97 0.63–1.48 36 (15.8) 0.98 0.58–1.65 25 (17.1) 1.13 0.63–2.01
ALL Latent class infertility
 No 266 (82.1) 217 (82.2) Ref 131 (83.4) Ref 62 (80.5) Ref
 Yes 58 (17.9) 47 (17.8) 1.27 0.79–2.05 26 (16.6) 1.20 0.68–2.13 15 (19.5) 1.40 0.69–2.85
AML Latent class infertility
 No 266 (82.1) 149 (86.6) Ref 59 (86.8) Ref 56 (84.8) Ref
 Yes 58 (17.9) 23 (13.4) 0.65 0.36–1.18 9 (13.2) 0.70 0.29–1.67 10 (15.2) 0.89 0.40–2.02
Open in a new tab

ALL, acute lymphoblastic leukaemia; AML, acute myeloid leukaemia; MLL, mixed lineage leukaemia.

aLogistic regression models were adjusted for year of birth (quartiles), maternal age (continuous), maternal education, maternal race, smoking during pregnancy, household income, gestational age and birthweight.

Table III.

Association between infant leukemia and additional infertility-related factors.

Controls
 Cases
 ORa 95% CI MLL+
 ORa 95% CI MLL−
 ORa 95% CI
n (%) n (%) n (%) n (%)
Combined leukemia Prior fetal loss
 None 241 (74.4) 337 (76.1) Ref 171 (75.0) Ref 114 (78.1) Ref
 One 64 (19.8) 76 (17.2) 1.10 0.73–1.64 42 (18.4) 1.32 0.80–2.16 22 (15.1) 0.88 0.50–1.57
 Two or more 19 (5.9) 30 (6.8) 1.37 0.71–2.62 15 (6.6) 1.57 0.71–3.47 10 (6.8) 1.46 0.61–3.49
Maternal age [mean (SD)] (OR for 1 year increase) 29.82 (5.42) 29.08 (5.75) 0.99 0.96–1.02 28.81 (5.53) 0.98 0.94–1.02 28.98 (6.04) 0.99 0.95–1.03
Use of OS drugs
 No 309 (95.4) 424 (95.7) Ref 218 (95.6) Ref 140 (95.9) Ref
 Yes 15 (4.6) 19 (4.3) 1.01 0.48–2.13 10 (4.4) 0.88 0.35–2.22 6 (4.1) 0.98 0.36–2.70
Time to index pregnancy
 Not trying 108 (33.4) 170 (38.4) 1.22 0.86–1.74 77 (33.8) 0.98 0.63–1.53 66 (45.2) 1.62 1.01–2.59
 <1 year of trying 175 (54.2) 227 (51.2) Ref 125 (54.8) Ref 67 (45.9) Ref
 ≥1 year of trying 40 (12.4) 46 (10.4) 1.09 0.66–1.80 26 (11.4) 1.10 0.60–2.01 13 (8.9) 0.99 0.47–2.07
ALL Prior fetal loss
 None 241 (74.4) 199 (75.4) Ref 116 (73.9) Ref 61 (79.2) Ref
 One 64 (19.8) 44 (16.7) 1.13 0.71–1.79 29 (18.5) 1.44 0.83–2.49 9 (11.7) 0.62 0.28–1.39
 Two or more 19 (5.9) 21 (8.0) 1.76 0.87–3.59 12 (7.6) 1.85 0.79–4.33 7 (9.1) 2.20 0.80–6.03
Maternal age [mean (SD)] (OR for 1 year increase) 29.82 (5.42) 28.73 (5.58) 0.98 0.94–1.02 28.71 (5.49) 0.99 0.94–1.03 28.74 (5.98) 0.96 0.91–1.02
Use of OS drugs
 No 309 (95.4) 250 (94.7) Ref 150 (95.5) Ref 73 (94.8) Ref
 Yes 15 (4.6) 14 (5.3) 1.42 0.64–3.15 7 (4.5) 1.08 0.40–2.94 4 (5.2) 1.32 0.40–4.32
Time to index pregnancy
 Not trying 108 (33.4) 104 (39.4) 1.32 0.88–1.96 53 (33.8) 1.00 0.61–1.64 38 (49.4) 2.50 1.36–4.61
 <1 year of trying 175 (54.2) 127 (48.1) Ref 85 (54.1) Ref 29 (37.7) Ref
 ≥1 year of trying 40 (12.4) 33 (12.5) 1.32 0.76–2.30 19 (12.1) 1.09 0.55–2.13 10 (13.0) 2.01 0.85–4.78
AML Prior fetal loss
 None 241 (74.4) 133 (77.3) Ref 54 (79.4) Ref 50 (75.8) Ref
 One 64 (19.8) 31 (18.0) 1.01 0.60–1.72 12 (17.6) 1.06 0.48–2.36 13 (19.7) 1.16 0.56–2.40
 Two or more 19 (5.9) 8 (4.7) 0.85 0.33–2.21 2 (2.9) 0.80 0.16–4.09 3 (4.5) 0.88 0.23–3.40
Maternal age [mean (SD)] (OR for 1 year increase) 29.82 (5.42) 29.68 (5.87) 1.01 0.97–1.05 28.85 (5.60) 0.95 0.88–1.02 29.48 (6.03) 1.02 0.96–1.08
Use of OS drugs
 No 309 (95.4) 167 (97.1) Ref 65 (95.6) Ref 64 (97.0) Ref
 Yes 15 (4.6) 5 (2.9) 0.44 0.13–1.44 3 (4.4) 0.57 0.11–2.88 2 (3.0) 0.63 0.13–3.10
Time to index pregnancy
 Not trying 108 (33.4) 65 (37.8) 1.22 0.77–1.95 24 (35.3) 1.28 0.62–2.62 27 (40.9) 1.17 0.61–2.22
 <1 year of trying 175 (54.2) 94 (54.7) Ref 37 (54.4) Ref 36 (54.5) Ref
 ≥1 year of trying 40 (12.4) 13 (7.6) 0.75 0.36–1.55 7 (10.3) 1.36 0.51–3.63 3 (4.5) 0.35 0.09–1.27
Open in a new tab

aLogistic regression models were adjusted for year of birth (quartiles), maternal age (continuous), maternal education, maternal race, smoking during pregnancy, household income, gestational age and birthweight.

OS = ovarian stimulating.

Discussion

We found little evidence of a link between infertility or its treatment and infant leukemia in this study. In fact, some of the statistically significant observed associations were in the opposite direction than hypothesized (e.g. an increased risk for those not trying to become pregnant). Even though the sample size of this study was relatively small, we can generally rule out large positive effects of infertility and infertility treatment on infant leukemia.

The only other study which has examined the association between infertility treatment and infant leukemia specifically evaluated medication recorded in the mother's medical record during pregnancy and found a non-significant inverse association with clomiphene (Ross et al., 2003). In the current study, while no association was found for combined leukemia, there was an indication of a 58% decrease in the odds of AML with medication used for infertility and a similar, but non-significant, OR for the use of ovulation-stimulating drugs. The inverse association was unexpected. It could be due to a ‘healthy user’ effect, in which those who seek out infertility treatments have better health and dietary intake than women who did not use medical intervention to achieve pregnancy. However, if this were the case, we would expect to see this inverse association across all groups examined, which we did not.

We also found an increased risk in the MLL− group both in combined leukemia and for the ALL MLL− subgroup for women who indicated that they were not trying to become pregnant when they conceived the index child. With few significant findings and many comparisons, however, some significant results could be due to chance alone.

There are several strengths to this study. First, this study represents one of the largest case–control studies assembled for infant leukemia. In addition, this is one of the few studies of infant leukemia that has incorporated the presence of MLL translocations into the analyses. Finally, the exposures used in this analysis are specific to infertility or infertility treatment and, as such, can better evaluate the relationship between this exposure and infant leukemia.

There are also several limitations to this study. First, as it uses the case–control design, it is subject to recall bias. Case–control studies are uniquely prone to recall bias, which occurs when cases recall past exposures differently from controls, leading to an OR that reflects a difference between the two groups based on recall rather than an actual difference in exposure (Schlesselman and Stolley, 1982). Several studies have assessed the magnitude of maternal recall bias and have found varying levels, which were dependent upon the exposure studied (Werler et al., 1989; Drews et al., 1990). One study on malformations that examined the history of infertility found that women reported malformations much less than what was reported in medical records. Cases in this study were slightly more likely to report a history of infertility than controls (Werler et al., 1989). Another study showed little difference in the sensitivity of reporting previous spontaneous miscarriage in cases and controls in a study on sudden infant death syndrome (Drews et al., 1990). Other exposures of interest such as time to conception cannot be easily validated. We do note that the young age at diagnosis allowed information on exposures during pregnancy to be assessed after only a short period of time had passed, which may help to minimize errors in recall.

Selection bias occurs in a case–control study when the probability of participation or selection into the study is different based on case–control status and the underlying exposure of interest (Savitz, 2003). It is of particular concern in a study, such as ours, with low control response rates. An analysis of potential selection bias in our study found that controls differed from the underlying population of interest in terms of maternal age, maternal education level, birthweight, gestational age, race and marital status (Puumala et al., 2009). In the current analysis, maternal age, education and income were found to be confounders of the association between infertility measures and infant leukemia. Since these factors are also related to selection and are antecedent to the exposure and disease, selection bias due to these factors should be mitigated in the adjusted analysis (Greenland, 1996).

Another potential limitation was the recruitment of controls in two different time periods by two different methods. Controls could be different from one another and lead to problems in estimating an overall effect by combining the two study phases. However, in a study examining possible differences between the two control populations, the controls from each time period were found not to differ significantly from one another except for reported smoking during pregnancy (Puumala et al., 2009). The difference in smoking rates could be due to a temporal trend since reported smoking during pregnancy appears to be declining over time (Ananth et al., 2005; Okah et al., 2005). Thus, it was necessary to include controls from both study phases, so that differences between cases and controls would not be influenced by temporal trends.

Finally, even though this study was comparatively large, the sample size was still small in absolute terms, which may have resulted in imprecisely estimated parameters. Also, with several exposure variables of interest and multiple subgroup analyses, our results are likely to include some false positives. Taken together, our results must be interpreted with caution and considered exploratory.

We found little evidence of an association between infertility and infertility treatment and infant leukemia in the largest study of infant leukemia yet conducted. Although there are several limitations both of the study itself and the study design used, it is unlikely that a strong association was missed.

Authors' roles

S.E.P. contributed to data analysis, interpretation of results, drafting and editing of the final manuscript. L.G.S. contributed to study design, acquisition of data, interpretation of results, drafting and editing of the final manuscript. M.M.W. contributed to interpretation of results and critical editing of the final manuscript. L.L.R. contributed to study conception and design, acquisition of data, interpretation of results and critical editing of the final manuscript. N.A.H contributed to study design, acquisition of data and critical editing of the final manuscript. M.A.R contributed to acquisition of data and critical editing of the final manuscript. J.A.R conceived of and designed the study, directed its implementation, and assisted in interpretation of results and critical editing of the final manuscript.

Funding

This work was supported by National Institutes of Health [R01CA79940, T32CA099967, U10CA13539, U10CA98413 and U10CA98543] and the Children's Cancer Research Fund (Minneapolis, MN, USA).

References

  1. Ananth CV, Kirby RS, Kinzler WL. Divergent trends in maternal cigarette smoking during pregnancy: United States 1990–99. Paediatr Perinat Epidemiol. 2005;19:19–26. doi: 10.1111/j.1365-3016.2004.00593.x. [DOI] [PubMed] [Google Scholar]
  2. Bowdin S, Allen C, Kirby G, Brueton L, Afnan M, Barratt C, Kirkman-Brown J, Harrison R, Maher ER, Reardon W. A survey of assisted reproductive technology births and imprinting disorders. Hum Reprod. 2007;22:3237–3240. doi: 10.1093/humrep/dem268. [DOI] [PubMed] [Google Scholar]
  3. Bruinsma F, Venn A, Lancaster P, Speirs A, Healy D. Incidence of cancer in children born after in vitro fertilization. Hum Reprod. 2000;15:604–607. doi: 10.1093/humrep/15.3.604. [DOI] [PubMed] [Google Scholar]
  4. Chang AS, Moley KH, Wangler M, Feinberg AP, Debaun MR. Association between Beckwith–Wiedemann syndrome and assisted reproductive technology: a case series of 19 patients. Fertil Steril. 2005;83:349–354. doi: 10.1016/j.fertnstert.2004.07.964. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cnattingius S, Zack M, Ekbom A, Gunnarskog J, Linet M, Adami HO. Prenatal and neonatal risk factors for childhood myeloid leukemia. Cancer Epidemiol Biomarkers Prev. 1995;4:441–445. [PubMed] [Google Scholar]
  6. Cox GF, Burger J, Lip V, Mau UA, Sperling K, Wu BL, Horsthemke B. Intracytoplasmic sperm injection may increase the risk of imprinting defects. Am J Hum Genet. 2002;71:162–164. doi: 10.1086/341096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. DeBaun MR, Niemitz EL, Feinberg AP. Association of in vitro fertilization with Beckwith–Wiedemann syndrome and epigenetic alterations of LIT1 and H19. Am J Hum Genet. 2003;72:156–160. doi: 10.1086/346031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Doornbos ME, Maas SM, McDonnell J, Vermeiden JP, Hennekam RC. Infertility, assisted reproduction technologies and imprinting disturbances: a Dutch study. Hum Reprod. 2007;22:2476–2480. doi: 10.1093/humrep/dem172. [DOI] [PubMed] [Google Scholar]
  9. Doyle P, Bunch KJ, Beral V, Draper GJ. Cancer incidence in children conceived with assisted reproduction technology. Lancet. 1998;352:452–453. doi: 10.1016/s0140-6736(05)79186-8. [DOI] [PubMed] [Google Scholar]
  10. Drews CD, Kraus JF, Greenland S. Recall bias in a case–control study of sudden infant death syndrome. Int J Epidemiol. 1990;19:405–411. doi: 10.1093/ije/19.2.405. [DOI] [PubMed] [Google Scholar]
  11. Formann AK, Kohlmann T. Latent class analysis in medical research. Stat Methods Med Res. 1996;5:179–211. doi: 10.1177/096228029600500205. [DOI] [PubMed] [Google Scholar]
  12. Gale KB, Ford AM, Repp R, Borkhardt A, Keller C, Eden OB, Greaves MF. Backtracking leukemia to birth: identification of clonotypic gene fusion sequences in neonatal blood spots. Proc Natl Acad Sci USA. 1997;94:13950–13954. doi: 10.1073/pnas.94.25.13950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gicquel C, Gaston V, Mandelbaum J, Siffroi JP, Flahault A, Le Bouc Y. In vitro fertilization may increase the risk of Beckwith–Wiedemann syndrome related to the abnormal imprinting of the KCN1OT gene. Am J Hum Genet. 2003;72:1338–1341. doi: 10.1086/374824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Greaves M. Pre-natal origins of childhood leukemia. Rev Clin Exp Hematol. 2003;7:233–245. [PubMed] [Google Scholar]
  15. Greaves M. In utero origins of childhood leukaemia. Early Hum Dev. 2005;81:123–129. doi: 10.1016/j.earlhumdev.2004.10.004. [DOI] [PubMed] [Google Scholar]
  16. Greenland S. Basic methods for sensitivity analysis of biases. Int J Epidemiol. 1996;25:1107–1116. [PubMed] [Google Scholar]
  17. Halliday J, Oke K, Breheny S, Algar E, Amor DJ. Beckwith–Wiedemann syndrome and IVF: a case–control study. Am J Hum Genet. 2004;75:526–528. doi: 10.1086/423902. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kallen B, Finnstrom O, Nygren KG, Olausson PO. In vitro fertilization in Sweden: child morbidity including cancer risk. Fertil Steril. 2005;84:605–610. doi: 10.1016/j.fertnstert.2005.03.035. [DOI] [PubMed] [Google Scholar]
  19. Klip H, Burger CW, de Kraker J, van Leeuwen FE. Risk of cancer in the offspring of women who underwent ovarian stimulation for IVF. Hum Reprod. 2001;16:2451–2458. doi: 10.1093/humrep/16.11.2451. [DOI] [PubMed] [Google Scholar]
  20. Lerner-Geva L, Toren A, Chetrit A, Modan B, Mandel M, Rechavi G, Dor J. The risk for cancer among children of women who underwent in vitro fertilization. Cancer. 2000;88:2845–2847. doi: 10.1002/1097-0142(20000615)88:12<2845::aid-cncr26>3.0.co;2-e. [DOI] [PubMed] [Google Scholar]
  21. Lidegaard O, Pinborg A, Andersen AN. Imprinting diseases and IVF: Danish National IVF cohort study. Hum Reprod. 2005;20:950–954. doi: 10.1093/humrep/deh714. [DOI] [PubMed] [Google Scholar]
  22. Lightfoot T, Bunch K, Ansell P, Murphy M. Ovulation induction, assisted conception and childhood cancer. Eur J Cancer. 2005;41:715–724. doi: 10.1016/j.ejca.2004.07.032. discussion 725–726. [DOI] [PubMed] [Google Scholar]
  23. Linabery AM, Ross JA. Trends in childhood cancer incidence in the U.S. (1992–2004) Cancer. 2008;112:416–432. doi: 10.1002/cncr.23169. [DOI] [PubMed] [Google Scholar]
  24. Ludwig M, Katalinic A, Gross S, Sutcliffe A, Varon R, Horsthemke B. Increased prevalence of imprinting defects in patients with Angelman syndrome born to subfertile couples. J Med Genet. 2005;42:289–291. doi: 10.1136/jmg.2004.026930. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Maher ER, Brueton LA, Bowdin SC, Luharia A, Cooper W, Cole TR, Macdonald F, Sampson JR, Barratt CL, Reik W, et al. Beckwith–Wiedemann syndrome and assisted reproduction technology (ART) J Med Genet. 2003;40:62–64. doi: 10.1136/jmg.40.1.62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. McLaughlin CC, Baptiste MS, Schymura MJ, Nasca PC, Zdeb MS. Maternal and infant birth characteristics and hepatoblastoma. Am J Epidemiol. 2006;163:818–828. doi: 10.1093/aje/kwj104. [DOI] [PubMed] [Google Scholar]
  27. Muthen L, Muthen B. Mplus User's Guide. Los Angeles: Muthen & Muthen; 1998–2004. [Google Scholar]
  28. Okah FA, Cai J, Dew PC, Hoff GL. Are fewer women smoking during pregnancy? Am J Health Behav. 2005;29:456–461. doi: 10.5555/ajhb.2005.29.5.456. [DOI] [PubMed] [Google Scholar]
  29. Olivennes F, Mannaerts B, Struijs M, Bonduelle M, Devroey P. Perinatal outcome of pregnancy after GnRH antagonist (ganirelix) treatment during ovarian stimulation for conventional IVF or ICSI: a preliminary report. Hum Reprod. 2001;16:1588–1591. doi: 10.1093/humrep/16.8.1588. [DOI] [PubMed] [Google Scholar]
  30. Puumala SE, Ross JA, Olshan AF, Robison LL, Smith FO, Spector LG. Reproductive history, infertility treatment, and the risk of acute leukemia in children with down syndrome: a report from the Children's Oncology Group. Cancer. 2007;110:2067–2074. doi: 10.1002/cncr.23025. [DOI] [PubMed] [Google Scholar]
  31. Puumala SE, Spector LG, Robison LL, Bunin GR, Olshan AF, Linabery AM, Roesler MA, Blair CK, Ross JA. Comparability and representativeness of control groups in a case–control study of infant leukemia: a report from the Children's Oncology Group. Am J Epidemiol. 2009;170:379–387. doi: 10.1093/aje/kwp127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Reaman GH. Biology and treatment of acute leukemias in infants. In: Pui C, editor. Treatment of Acute Leukemias: New Directions for Clinical Research. Totowa, NJ: Humana Press; 2003. pp. 75–83. [Google Scholar]
  33. Robison LL, Daigle A. Control selection using random digit dialing for cases of childhood cancer. Am J Epidemiol. 1984;120:164–166. doi: 10.1093/oxfordjournals.aje.a113866. [DOI] [PubMed] [Google Scholar]
  34. Roman E, Ansell P, Bull D. Leukaemia and non-Hodgkin's lymphoma in children and young adults: are prenatal and neonatal factors important determinants of disease? Br J Cancer. 1997;76:406–415. doi: 10.1038/bjc.1997.399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ross JA, Xie Y, Davies SM, Shu XO, Pendergrass TW, Robison LL. Prescription medication use during pregnancy and risk of infant leukemia (United States) Cancer Causes Control. 2003;14:447–451. doi: 10.1023/a:1024953532355. [DOI] [PubMed] [Google Scholar]
  36. Savitz D. Interpreting Epidemiologic Evidence. Oxford, NY: Oxford University Press; 2003. Selection bias in case control studies; pp. 81–114. [Google Scholar]
  37. Schlesselman JJ, Stolley PD. Sources of bias. In: Schlesselman JJ, editor. Case–control Studies. Oxford, NY: Oxford University Press; 1982. pp. 124–143. [Google Scholar]
  38. Schuz J, Kaatsch P, Kaletsch U, Meinert R, Michaelis J. Association of childhood cancer with factors related to pregnancy and birth. Int J Epidemiol. 1999;28:631–639. doi: 10.1093/ije/28.4.631. [DOI] [PubMed] [Google Scholar]
  39. Shaw AK, Infante-Rivard C, Morrison HI. Use of medication during pregnancy and risk of childhood leukemia (Canada) Cancer Causes Control. 2004;15:931–937. doi: 10.1007/s10552-004-2230-6. [DOI] [PubMed] [Google Scholar]
  40. Shu XO, Han D, Severson RK, Chen Z, Neglia JP, Reaman GH, Buckley JD, Robison LL. Birth characteristics, maternal reproductive history, hormone use during pregnancy, and risk of childhood acute lymphocytic leukemia by immunophenotype (United States) Cancer Causes Control. 2002;13:15–25. doi: 10.1023/a:1013986809917. [DOI] [PubMed] [Google Scholar]
  41. Slattery ML, Edwards SL, Caan BJ, Kerber RA, Potter JD. Response rates among control subjects in case–control studies. Ann Epidemiol. 1995;5:245–249. doi: 10.1016/1047-2797(94)00113-8. [DOI] [PubMed] [Google Scholar]
  42. Stam RW, den Boer ML, Passier MM, Janka-Schaub GE, Sallan SE, Armstrong SA, Pieters R. Silencing of the tumor suppressor gene FHIT is highly characteristic for MLL gene rearranged infant acute lymphoblastic leukemia. Leukemia. 2006;20:264–271. doi: 10.1038/sj.leu.2404074. [DOI] [PubMed] [Google Scholar]
  43. Sutcliffe AG, Peters CJ, Bowdin S, Temple K, Reardon W, Wilson L, Clayton-Smith J, Brueton LA, Bannister W, Maher ER. Assisted reproductive therapies and imprinting disorders—a preliminary British survey. Hum Reprod. 2006;21:1009–1011. doi: 10.1093/humrep/dei405. [DOI] [PubMed] [Google Scholar]
  44. van Steensel-Moll HA, Valkenburg HA, Vandenbroucke JP, van Zanen GE. Are maternal fertility problems related to childhood leukaemia? Int J Epidemiol. 1985;14:555–559. doi: 10.1093/ije/14.4.555. [DOI] [PubMed] [Google Scholar]
  45. Wen W, Shu XO, Potter JD, Severson RK, Buckley JD, Reaman GH, Robison LL. Parental medication use and risk of childhood acute lymphoblastic leukemia. Cancer. 2002;95:1786–1794. doi: 10.1002/cncr.10859. [DOI] [PubMed] [Google Scholar]
  46. Werler MM, Pober BR, Nelson K, Holmes LB. Reporting accuracy among mothers of malformed and nonmalformed infants. Am J Epidemiol. 1989;129:415–421. doi: 10.1093/oxfordjournals.aje.a115145. [DOI] [PubMed] [Google Scholar]
  47. Zweidler-McKay PA, Hilden JM. The ABCs of infant leukemia. Curr Probl Pediatr Adolesc Health Care. 2008;38:78–94. doi: 10.1016/j.cppeds.2007.12.001. [DOI] [PubMed] [Google Scholar]

Articles from Human Reproduction (Oxford, England) are provided here courtesy of Oxford University Press

RESOURCES