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Nutritional and genetic determinants of the one-carbon metabolism pathway have been related to risk of malignant lymphomas, but little is known about their associations with Hodgkin lymphoma risk specifically. The authors examined nutrient intake (folate, vitamin B2, vitamin B6, vitamin B12, methionine) and multivitamin use among 497 Hodgkin lymphoma patients and 638 population-based controls (Massachusetts and Connecticut, 1997–2000), and genetic variation (MTHFR 677C>T, MTHFR 1298A>C, MTR 2756A>G, SHMT1 1420C>T, TYMS 1494del6) and gene-diet interactions in a subset. Unconditional logistic regression was used to calculate multivariable odds ratios and 95% confidence intervals. Hodgkin lymphoma risk was not associated with total nutrient intake or intake from food alone (excluding supplements). Multivitamin use (odds ratio (OR) = 1.46, 95% CI: 1.09, 1.96), total vitamin B6 (ORquartile 4 vs. 1 = 1.62) (Ptrend = 0.03), and total vitamin B12 (ORquartile 4 vs. 1 = 1.75) (Ptrend = 0.02) intakes were positively associated with risk of Epstein-Barr virus-negative, but not -positive, disease. The 5 genetic variants were not significantly associated with Hodgkin lymphoma risk; no significant gene-diet interactions were observed after Bonferroni correction. Study findings do not support a strong role for nutrients and genetic variation in the one-carbon metabolism pathway in susceptibility to Hodgkin lymphoma. Associations between diet and risk of Epstein-Barr virus-negative disease require confirmation in other populations.
Folate, a water-soluble B vitamin, acts as a methyl-group donor in the one-carbon metabolism pathway and is critical for DNA synthesis, repair, and methylation (1). Vitamins B2, B6, and B12, which function as enzymatic cofactors, and methionine, which enters the pathway directly, are also required for efficient one-carbon metabolism. Because Hodgkin lymphoma tumors are chromosomally unstable (2) and epigenetic mechanisms have been observed in tumor cell lines (3–6), augmenting dietary intake of folate and related nutrients could offer a modifiable means of reducing Hodgkin lymphoma risk. Previous studies of diet and Hodgkin lymphoma risk have been limited in number and power (7–16), and none have investigated the nutrients involved in one-carbon metabolism.
Hodgkin lymphoma may also be influenced by genetic variation in the one-carbon metabolism pathway. Four prior case-control studies have investigated 2 nonsynonymous polymorphisms in the gene that codes for methylenetetrahydrofolate reductase (MTHFR), a key enzyme of the one-carbon metabolism pathway (17–20). One found an elevated risk of Hodgkin lymphoma associated with the CC versus CT/TT genotype of the MTHFR 677C>T polymorphism (18), whereas 3 other studies were null for MTHFR 677C>T (17, 19, 20) and MTHFR 1298A>C (20). Furthermore, a recent genome-wide association study of classical Hodgkin lymphoma (589 cases and 5,199 controls) identified 3 new susceptibility loci and confirmed the strong association of human leukocyte antigen class II variation with Hodgkin lymphoma risk, but no significant risk variants were found in one-carbon metabolism genes (21). These inconsistent findings may be due to a small number of cases or the inability to consider joint associations between genotype and folate status. Thus, larger studies of Hodgkin lymphoma, particularly with information on both genotype and diet, are needed.
In this population-based case-control study, we assessed the associations of diet (folate, vitamin B2, vitamin B6, vitamin B12, and methionine intake, as well as multivitamin use), polymorphisms in folate-metabolizing genes, and gene-diet interactions with Hodgkin lymphoma risk. Interactions of diet with sex, age, and alcohol intake were also considered. We previously showed that older age, male sex, smoking, and lower education were associated with risk of Epstein-Barr virus (EBV)-positive vs. -negative disease in this population (22). Therefore, we also examined dietary and genetic associations according to tumor EBV status. To our knowledge, this is the first study to examine how gene-diet interactions and nutrients involved in one-carbon metabolism are related to Hodgkin lymphoma risk.
From August 1, 1997, to December 31, 2000, incident Hodgkin lymphoma patients were recruited for a population-based case-control study in the greater Boston, Massachusetts, metropolitan area and in the state of Connecticut. Eligible patients were 15–79 years of age at diagnosis, living within the described geographic area, and without human immunodeficiency virus infection. Of 677 cases invited to participate, 567 (84%) consented.
Pathology material was reviewed by study pathologists (M. Borowitz, R. B. Mann, and E. G. Weir at Johns Hopkins University) to confirm the diagnosis of Hodgkin lymphoma (23). Of the 463 cases with information on pathologic subtype, 447 (97%) were classical Hodgkin lymphoma and 16 were nodular lymphocyte-predominant subtype. We excluded the latter from our analyses, because the nodular lymphocyte-predominant subtype is considered biologically and clinically separate from classical subtypes (24).
Tumor tissue was previously analyzed for the presence of EBV by using in situ hybridization for EBV-encoded RNA transcripts and/or by immunohistochemical assay for the viral latency membrane protein 1 in the Hodgkin and Reed-Sternberg cells (22, 25). A tumor was considered EBV positive if results were positive for either of the assays and EBV negative if both assays were negative or only a single assay was performed and its result was negative (26).
Population controls were frequency matched to the age (within 5 years), sex, and state of residency distribution of the cases. Eligible controls were living residents of the study area and without prior history of Hodgkin lymphoma.
In the Boston metropolitan area (132 cities and towns), controls were randomly selected from current “Town Books.” The Town Books are annual records that include the name, sex, street address, and birth year of all residents aged ≥17 years and are >90% complete (27). If a selected control could not be contacted or refused to participate, the next listed eligible person was selected as a replacement control. Of 720 invited controls, 367 (51%) consented.
Connecticut controls aged 18–65 years were identified by random digit dialing; approximately 98.9% of Connecticut residents had home telephone service at the time of the study (28). To avoid overlap of participant responses and clustering by social class, only one participant was recruited per household. To prevent geographic clustering, a maximum of 8 households were screened within a block of 100 telephone numbers. Of the 450 eligible controls arising from 5,632 telephone numbers attempted, 276 (61%) consented to participate in the study. Connecticut controls aged 66–79 years were randomly selected from the Health Care Financing Administration (Medicare) files; 36 (52%) of 69 eligible controls consented to participate.
Participants or their guardians, if minors, gave written informed consent at enrollment. This research protocol was approved by the institutional review boards of the Harvard School of Public Health, Yale University School of Medicine, and Johns Hopkins Medical School, as well as all participating hospitals, the Massachusetts Cancer Registry, and the Connecticut Tumor Registry in the Connecticut Department of Public Health.
Of the 551 cases and 679 controls who consented, 97% completed a structured telephone interview, and 3% completed an abbreviated mailed questionnaire assessing known and potential risk factors for Hodgkin lymphoma (23, 29). Participants were also invited to complete a validated, semiquantitative food frequency questionnaire containing 61 food and beverage items (including alcohol), plus vitamin and mineral supplements (30). Participants recorded how often (9 options ranging from “never” to “6+ per day”) they consumed each food item in the past year according to a commonly used unit or portion size; multivitamin dose options ranged from “2 or less” to “10 or more” tablets/week. Nutrient intake was calculated by multiplying the frequency response by the nutrient content for the chosen portion size of a particular item. All measures of nutrient intake were adjusted for total energy, separately for men and women, by using the residual method to account for the correlation of nutrient intake with energy intake (31). Of the 511 cases (93%) and 648 controls (95%) who completed the food frequency questionnaire, 24 were excluded for leaving ≥10 items blank or for reporting a sex-specific energy intake >3 standard deviations from the mean on the natural log scale (males: <524 or >4,953 kcal; females: <406 or >4,584 kcal). These exclusions left 497 cases and 638 controls for the dietary analyses.
Blood specimens were collected from 466 (85%) of 551 interviewed cases, and buccal cell specimens were collected by using a mailed kit with commercial mouthwash from 372 (55%) of 679 interviewed controls (32). DNA extraction and genotyping were performed at the Dana-Farber/Harvard Cancer Center High Throughput Polymorphism Detection Core. DNA was initially extracted from buccal cells by using the Puregene kit (Gentra Systems, Inc., Minneapolis, Minnesota) and, subsequently, DNA from both the buccal and buffy coat specimens was extracted by using the QIAamp DNA blood kit (Qiagen GmbH, Hilden, Germany).
We measured the genetic variation of 4 nonsynonymous single nucleotide polymorphisms and 1 deletion mutation in 4 candidate genes: methylenetetrahydrofolate reductase gene (MTHFR) 677C>T, MTHFR 1298A>C, methionine synthase gene (MTR) 2756A>G, cytoplasmic serine hydroxymethyl transferase gene (SHMT1) 1420C>T, and thymidylate synthase gene (TYMS) 1494del6. These particular variants were chosen on the basis of impaired function of the gene and/or associations detected in prior studies of genetic variation in one-carbon metabolism and risk of lymphoma (33). DNA primer sets are listed by Skibola et al. (34). Genotyping was performed by using the TaqMan Gene Expression Assay and the ABI PRISM 7900HT Sequence Detection System (Applied Biosystems, Foster City, California). Quality control DNA samples (5–6 sets of duplicates per genotyping assay) yielded 100% concordance in each of the 5 assays.
Twenty-three samples were excluded because of genotyping failure for ≥2 of the 5 TaqMan assays, resulting in 454 cases and 361 controls for the genetic analyses. Information on both diet and genotype was available for 418 cases and 353 controls.
Two-sample t tests and chi-square tests were used to compare descriptive characteristics between cases and controls. Unconditional logistic regression was used to assess the associations of nutrient intake and genetic polymorphisms with risk of Hodgkin lymphoma. Quartiles of nutrient and alcohol intake were constructed according to sex-specific distributions among controls and modeled as indicator variables. The association of nutrient intake with Hodgkin lymphoma risk was estimated as the odds ratio and its 95% confidence interval, with the lowest quartile assigned as the referent group. We tested for linear trends by modeling the median values of the quartiles as a continuous variable and evaluated statistical significance using the Wald test.
The minimally adjusted model included total energy intake (continuous) and the matching factors: age (5-year age groups), sex (male, female), and state of residence (Massachusetts, Connecticut). The fully adjusted model included the following variables that were significantly associated with overall Hodgkin lymphoma risk when added to the minimally adjusted model: race/ethnicity (non-Hispanic white, nonwhite/missing (n = 2 missing)); history of smoking ≥10 lifetime packs of cigarettes (yes, no/missing (n = 1 missing)); aspirin use during the past 5 years (≥2, <2 tablets per week, missing (n = 46)); acetaminophen use during the past 5 years (≥2, <2 tablets per week, missing (n = 51)); education (less than high school, high school, college/missing (n = 2 missing), advanced degree); nursery school/daycare attendance for ≥1 year (yes, no, missing/don't know (n = 32)); and current alcohol intake (drinker, nondrinker).
We examined whether associations between diet and Hodgkin lymphoma differed by sex (male, female), age (<50, ≥50 years), or current alcohol intake (drinker, nondrinker) by constructing an interaction term between nutrient intake (median value of each quartile) or multivitamin use (yes/no) and the stratification variable. Interaction terms were evaluated by using likelihood ratio tests, adjusting for energy intake and the matching factors. We also examined the risk of EBV-positive and EBV-negative Hodgkin lymphoma compared with controls in separate multiple logistic regression models, adjusting for the matching factors, energy intake, smoking history, and education, on the basis of previously observed associations (22).
To assess whether genetic variation in MTHFR 677C>T, MTHFR 1298A>C, MTR 2756A>G, SHMT1 1420C > T, and TYMS 1494del6 was associated with Hodgkin lymphoma risk, we constructed indicator variables for genotype and assigned homozygous wildtype as the referent. Global P values were calculated by performing likelihood ratio tests (2 df) comparing the intercept-only model (adjusted for the matching factors and race/ethnicity) with the model including the indicator variables for genotype.
We used likelihood ratio tests to evaluate 30 gene-diet interactions between the 5 genetic variants and nutrient intake (total folate, total vitamin B2, total vitamin B6, total vitamin B12, methionine) or multivitamin use. Interaction terms were constructed by multiplying genotype (presence of at least 1 variant allele vs. homozygous wildtype) by the median values of the nutrient quartiles. Dominant inheritance models were used for the interaction terms because of sparse data among the homozygous variant genotypes.
Analyses were conducted by using SAS, version 9.1, software (SAS Institute, Inc., Cary, North Carolina). All statistical tests were 2 sided, and P < 0.05 was considered statistically significant, unless otherwise noted.
The mean age at diagnosis among Hodgkin lymphoma cases was 38 years (standard deviation = 15 years); the mean age at study recruitment among controls was 40 years (standard deviation = 16 years). Compared with controls, cases were less likely to be regular aspirin users, to have attended nursery school for ≥1 years in childhood, and to possess a higher education degree, and they were more likely to be regular acetaminophen users (Table 1). This study population comprised approximately 53% males and 47% females, and the greater majority of participants (~90 %) reported non-Hispanic white race/ethnicity.
In the dietary analysis adjusting for the matching factors and energy intake, nutrient intakes (total and from food alone) and current multivitamin use were not associated with Hodgkin lymphoma risk (Table 2). Odds ratio estimates were similar between the fully and minimally adjusted models. Because there was no evidence of significant interactions between age groups, sex, or alcohol consumption and nutrient intake or multivitamin use on Hodgkin lymphoma risk (data not shown), all odds ratios are presented without stratification by these factors. Associations between nutrient intake and risk of Hodgkin lymphoma according to tumor EBV status are presented in Table 3. Total vitamin B2 intake was associated with a slightly increased risk (Ptrend = 0.04) of EBV-positive disease. Risk of EBV-negative disease was positively associated with total vitamin B6 (Ptrend = 0.03) and B12 (Ptrend = 0.02) intakes and multivitamin use (Ptrend = 0.01). The associations for total vitamin B6 and B12 intakes with risk of EBV-negative disease were attenuated and statistically nonsignificant after adjustment for multivitamin use in the same model (data not shown). A suggestion of a dose-response trend was seen for number of multivitamin tablets taken per week and risk of EBV-negative disease (odds ratio (OR)1–2vs.0 = 0.97, 95% confidence interval (CI): 0.52, 1.83; OR3–5vs.0 = 1.73, 95% CI: 1.08, 2.76; OR6–9vs.0 = 1.41, 95% CI: 0.98, 2.02; OR≥10vs.0 = 2.22, 95% CI: 1.06, 4.66) (Ptrend = 0.007). Nutrients from food alone were not differentially associated with Hodgkin lymphoma risk according to tumor EBV status.
The 5 genetic variants had genotyping success rates between 94% and 99%, and all were in Hardy-Weinberg equilibrium among the controls (Table 4). No significant associations were detected between any of the genotypes and risk of Hodgkin lymphoma in the co-dominant (P value range: 0.20–0.99; Table 4) or dominant (P value range: 0.15–0.87; data not shown) model, adjusting for the matching factors and race/ethnicity. We also did not detect any significant associations between genotype and risk of EBV-negative or -positive Hodgkin lymphoma (data not shown).
Five of the 30 gene-diet interactions were statistically significant (Table 5). Most notably, multivitamin use was positively associated with Hodgkin lymphoma risk (OR = 1.67, 95% CI: 1.09, 2.65) (P = 0.02) among participants homozygous for the MTHFR 1298 wildtype allele (AA), whereas no association was found among those with 1 or 2 copies of the variant allele (Pinteraction = 0.01). No significant interactions were observed for the MTHFR 677C>T, MTR 2756A>G, and SHMT1 1420C>T genotypes (data not shown). After applying a Bonferroni correction (α = 0.05/30 = 0.0017) to account for multiple comparisons, none of the 5 interactions shown in Table 5 remained statistically significant (35).
In this population-based case-control study, we found no association of folate, vitamin B2, vitamin B6, vitamin B12, or methionine intake with overall classical Hodgkin lymphoma risk. Although no prior studies of these factors in relation to Hodgkin lymphoma exist, 3 case-control (36–38) and 2 prospective (39, 40) studies have examined these nutrients in relation to non-Hodgkin lymphoma risk with mixed findings. Overall, the current evidence does not support a strong association between non-Hodgkin lymphoma and one-carbon nutrients, which is in agreement with our largely null findings for Hodgkin lymphoma risk.
Because the etiology and risk factor profile of Hodgkin lymphoma may differ by EBV status, we examined the risk of each subtype separately (22, 41). Total vitamin B6 and B12 intakes and multivitamin use were positively associated with EBV-negative disease, but not EBV-positive disease, in our study population. These results may, however, be explained through multivitamins, because there was no association for vitamin B6 or B12 from food sources alone, and adjustment of total vitamin B6 or B12 intake for multivitamin use attenuated the associations. We also observed a slight positive association between total vitamin B2 and risk of EBV-positive disease, although this finding is based on a small number of cases and, like other observed associations, may be due to chance.
The positive association between multivitamin use and EBV-negative disease may be biased, because diet was assessed postdiagnostically, and a proportion of cases may have initiated supplement use as a result of their diagnosis (42). However, a small number of prospective studies have also found positive associations between multivitamin use and cancer risk: breast cancer (43), prostate cancer (44), and non-Hodgkin lymphoma (45). Zhang et al. (45) reported a 48% increased risk of non-Hodgkin lymphoma among women who used multivitamins for >10 years but found no association for men. The lack of an association between multivitamins and EBV-positive disease in our study may be due to low power. Alternatively, the pathogenesis of EBV-positive Hodgkin lymphoma may be largely determined by the oncogenic properties of EBV (2), not by dietary factors. Overall, the role of multivitamins in lymphomagenesis merits further investigation.
We did not find a link between genetic variants in key folate-metabolizing genes, including the MTHFR 677C>T and MTHFR 1298A>C polymorphisms, and Hodgkin lymphoma risk. Our null findings for the MTHFR polymorphisms are consistent with results from 3 prior case-control studies of 25–50 cases (17, 19, 20). However, Deligezer et al. (18) reported an increased risk of Hodgkin lymphoma (n = 51 cases) associated with the MTHFR 677 CC versus the CT/TT genotype, which contradicts the hypothesis that reduced enzymatic activity increases Hodgkin lymphoma risk (46).
Inconsistencies across prior genetic studies may be due in part to inadequate statistical power or effect modification by nutrient status. In fact, we found suggestive evidence of gene-diet interactions in our study. In general, high nutrient intake or multivitamin use was associated with increased risk of Hodgkin lymphoma among carriers of the wildtype genotype, whereas the association was null or nonsignificantly inverse among variant carriers. Because the variant alleles in MTHFR and TYMS reduce enzymatic activity (46, 47), these results suggest that individuals with unimpaired enzyme activity and high intakes of certain one-carbon metabolism-related nutrients are at highest risk of Hodgkin lymphoma. This conclusion contradicts the hypothesized mechanism that a diet high in one-carbon metabolism-related nutrients would provide a more stable environment for DNA synthesis and methylation, thereby reducing cancer risk (1). An alternative explanation is that reduced MTHFR activity favors the supply of 5,10-methylenetetrahydrafolate toward DNA synthesis and repair, away from the methylation cycle of the pathway, thereby reducing the likelihood of aberrant hypermethylation of tumor suppressor genes. This pattern has been seen in colorectal cancer, where individuals carrying the MTHFR 677C>T variant allele and with adequate folate status had the lowest cancer risk (48). We cannot, however, rule out the possibility that the gene-diet interactions we observed are chance findings, especially because none remained significant after Bonferroni correction for multiple comparisons.
Although our study population of Hodgkin lymphoma cases was one of the largest to date, statistical power was limited, especially for analyses within subgroups and tests for interactions. Response rates among controls were rather low, which could have led to selection bias, particularly if participation was related to quality of nutrition. We attempted to limit this potential selection bias by replacing nonparticipating Massachusetts controls with individuals drawn from the same residential area, and Chang et al. (29) further showed that the income distribution across census tracts of our consented Massachusetts controls was representative of the source population. However, data to evaluate this potential source of bias were not available among Connecticut controls. Recall bias is another potential limitation, because the estimate of nutrient intake relied on the participant's ability to recall usual dietary habits in the previous year, and diet was assessed after diagnosis of Hodgkin lymphoma. Finally, our study mostly includes young adult cases and may not be representative of older-onset (>55 years) disease etiology.
Our study has several strengths. We are the first to examine the link between Hodgkin lymphoma and dietary intake of nutrients involved in one-carbon metabolism. The assessment of nutrient intake was of high quality, as measured by a validated food frequency questionnaire, and the extensive baseline questionnaire allowed us to evaluate several potential confounders. Another unique aspect of our study was the collection of both dietary and genetic information, allowing for exploratory assessment of gene-diet interactions with respect to Hodgkin lymphoma risk. Consideration of the joint role of folate-metabolizing genes and related nutrients is biologically relevant and can aid in understanding the etiology of lymphomagenesis.
Overall, our study does not support a strong link between Hodgkin lymphoma risk and dietary intake of nutrients involved in one-carbon metabolism or genetic variation in this pathway. The potential increased risk of EBV-negative Hodgkin lymphoma among multivitamin users should be explored in other studies, ideally with prospective data. Additionally, larger studies with sufficient power to detect moderate gene-diet interactions are warranted to clarify the role of one-carbon metabolism in Hodgkin lymphoma risk.
Author affiliations: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts (Julie L. Kasperzyk, Peter Kraft, Nancy E. Mueller); Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts (Julie L. Kasperzyk, Brenda M. Birmann); Cancer Prevention Institute of California, Fremont, California (Ellen T. Chang); Division of Epidemiology, Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California (Ellen T. Chang); and School of Public Health, Yale University, New Haven, Connecticut (Tongzhang Zheng).
This work was supported by National Cancer Institute grants P01 CA069266-01A1 (N. E. M.), T32 CA-009001-35 (J. L. K.), and K07 CA115687 (B. M. B.) and by the American Institute for Cancer Research (J. L. K.).
The authors thank Edward Weir, Michael Borowitz, and Risa Mann (Johns Hopkins Medical Institute) for conducting the pathology review; Kathryn Trainor, Patricia Morey, and Karen Pawlish (Harvard School of Public Health) for project and data management; Mary Fronk (Harvard School of Public Health) for administrative support; and Richard Ambinder, Stacey Morin, Linda Post (Johns Hopkins Medical Institute), Judith Fine, Rajni Mehta, Patricia Owens (Yale University Rapid Case Ascertainment and School of Medicine), and Dan Friedman (Massachusetts Cancer Registry) for technical assistance at their study centers. Additional thanks to Patrice Soule and Hardeep Ranu of the Dana-Farber/Harvard Cancer Center High Throughput Polymorphism Detection Core and Carolyn Guo for assistance with the genetic database.
The authors also thank the participating staff members at the following hospitals in Massachusetts: AtlantiCare Medical Center, Beth Israel Deaconess Medical Center, Beverly Hospital, Boston Medical Center, Brigham and Women's Hospital, Brockton Hospital, Brockton VA/West Roxbury Hospital, Cambridge Hospital, Caritas Southwood Hospital, Carney Hospital, Children's Hospital Boston, Dana-Farber Cancer Institute, Deaconess Glover Memorial Hospital, Deaconess Waltham Hospital, Emerson Hospital, Faulkner Hospital, Good Samaritan Medical Center, Harvard Vanguard, Holy Family Hospital and Medical Center, Jordan Hospital, Lahey Hitchcock Medical Center, Lawrence General Hospital, Lawrence Memorial Hospital of Medford, Lowell General Hospital, Massachusetts Eye and Ear Infirmary, Massachusetts General Hospital, Melrose-Wakefield Hospital, MetroWest Medical Center, Morton Hospital, Mount Auburn Hospital, New England Baptist Hospital, New England Medical Center, Newton Wellesley Hospital, North Shore Medical Center, Norwood Hospital, Quincy Hospital, Saint's Memorial Hospital, South Shore Hospital, St. Elizabeth's Hospital, Sturdy Memorial Hospital, University of Massachusetts Medical Center, and Winchester Hospital; and in Connecticut: Bridgeport Hospital, Bristol Hospital, Charlotte Hungerford Hospital, Danbury Hospital, Day-Kimball Hospital, Greenwich Hospital, Griffin Hospital, Hartford Hospital, Johnson Memorial Hospital, Lawrence and Memorial Hospital, Manchester Memorial Hospital, MidState Medical Center, Middlesex Memorial Hospital, Milford Hospital, New Britain General Hospital, New Milford Hospital, Norwalk Hospital, Rockville General Hospital, Sharon Hospital, St. Francis Hospital and Medical Center, St. Mary's Hospital, St. Raphael's Hospital, St. Vincent's Medical Center, Stamford Hospital, WW Backus Hospital, Waterbury Hospital, Windham Hospital, and Yale-New Haven Hospital.
Conflict of interest: none declared.