Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2011 Jun 15.
Published in final edited form as: Cancer. 2010 Jun 15;116(12):2993–3000. doi: 10.1002/cncr.25114

Smoking, Alcohol Use, Obesity, and Overall Survival from Non-Hodgkin Lymphoma: A Population-Based Study

Susan M Geyer 1,2, Lindsay M Morton 3, Thomas M Habermann 1, Cristine Allmer 1, Scott Davis 4, Wendy Cozen 5, Richard K Severson 6, Charles F Lynch 7, Sophia S Wang 3, Matthew J Maurer 1, Patricia Hartge 3, James R Cerhan 1
PMCID: PMC2889918  NIHMSID: NIHMS182481  PMID: 20564404

Abstract

BACKGROUND

Smoking, alcohol use, and obesity appear to increase the risk of developing non-Hodgkin lymphoma (NHL), but few studies have assessed their impact on NHL prognosis.

METHODS

We evaluated the association of pre-diagnosis cigarette smoking, alcohol use, and body mass index (BMI) on overall survival in 1,286 patients enrolled through population-based registries in the United States from 1998–2000. Hazard Ratios (HR) and 95% confidence intervals (CI) were estimated using Cox regression, adjusting for clinical and demographic factors.

RESULTS

Through 2007, 442 patients died (34%), and the median follow-up on living patients was 7.7 years. Compared to never smokers, former (HR=1.59; 95% CI 1.12–2.26) and current (HR=1.50; 95% CI 0.97–2.29) smokers had poorer survival, and poorer survival was positively associated with smoking duration, number of cigarettes smoked per day, pack-years of smoking, and shorter time since quitting (all p-trend<0.01). Alcohol use was associated with poorer survival (p-trend=0.03); compared to non-users, those drinking more than 43.1 grams/week (median of intake among drinkers) had poorer survival (HR=1.55; 95% CI 1.06–2.27) while those drinkers consuming less than this amount showed no survival disadvantage (HR=1.13; 95% CI 0.75–1.71). Greater body mass index was associated with poorer survival (p-trend=0.046), but the survival disadvantage was only seen among obese individuals (HR=1.32 for BMI ≥30 versus 20–24.9 kg/m2; 95% CI 1.02–1.70). These results held for lymphoma-specific survival and were broadly similar for DLBCL and follicular lymphoma.

CONCLUSIONS

NHL patients who smoked, consumed alcohol or were obese prior to diagnosis had a poorer overall and lymphoma-specific survival.

Keywords: alcohol, non-Hodgkin lymphoma, obesity, smoking, survival

INTRODUCTION

In 2008, the American Cancer Society estimates that 66,120 persons will be diagnosed with non-Hodgkin lymphoma (NHL), and 19,160 will die from this disease.1 NHL is now the fifth most common cause of cancer among both men and women. NHL incidence rates have been increasing over much of the 20th century and have only recently begun to slow. Five-year relative survival for the time period of 1974–1995 was stable at approximately 50%, but has increased to 66% for the time period 1996–2004.2 Of the estimated 10.7 million cancer survivors in the US in 2004, approximately 431,000 had NHL,2 making this an important group from a clinical and public health perspective. Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma are the two most common NHL subtypes.

The strongest and most robust predictors of outcome in NHL are age, stage, number of nodal or extranodal sites, performance status, and certain biochemical measures (e.g., serum LDH and hemoglobin level), and these factors have been aggregated into various clinical prognostic indices including the International Prognostic Index (IPI) for DLBCL3 and the Follicular IPI (FLIPI) for follicular lymphoma.4 Multiple tumor biomarkers have also been widely evaluated as predictors of NHL prognosis, although few are used in routine clinical practice.5, 6 More recently, host genetic background has also been evaluated, including pharmacogenetic markers,7–9 genetic markers of general immune function,10, 11 or genetic markers of DNA repair function.12 In contrast, there is relatively little data on the impact of lifestyle factors on NHL prognosis. Results from large International Lymphoma Epidemiology Consortium (InterLymph) pooling projects have reported that cigarette smoking appears to slightly increase the risk of NHL overall and follicular lymphoma in particular;13 alcohol decreases the risk of NHL across most subtypes;14 and obesity increases the risk of DLBCL.15 However, only three studies have evaluated the role of these factors in NHL prognosis, and to date suggest that cigarette smoking,16, 17 alcohol use,16, 17 and obesity18 may be adverse prognostic factors. We therefore evaluated the impact of these factors on overall and lymphoma-specific survival in a series of cases who participated in a population-based case-control study in four regions of the United States.

MATERIALS AND METHODS

Study Population

This study of NHL survival has been previously described.10 Briefly, subjects with newly diagnosed, histologically-confirmed NHL were enrolled in a population-based case-control study from July 1998 through June 2000;19 the analyses presented here focus on the cases enrolled in that study. The cases were rapidly reported from four Surveillance, Epidemiology, and End Results (SEER) cancer registries in the Detroit metropolitan area, the state of Iowa, Los Angeles County, and northwestern Washington State. All consecutive NHL patients diagnosed in Iowa and the Seattle area were included in this study, while in Los Angeles and Detroit, all African American NHL patients were included, but only a random sample of non-African American patients. Eligible patients had a first primary diagnosis of NHL between the ages of 20 to 74 years old (inclusive), and were alive and competent to participate in the interview. Any patients known to be HIV-positive were excluded. Local IRB approval was obtained for each of the participating recruitment centers and consent was obtained for all participants.

Of 2,248 eligible cases, 320 (14%) died before we could conduct an interview, 127 (6%) could not be located, 16 (1%) had moved out of the area, and 57 (3%) had physician refusals. We attempted to contact the remaining 1,728, but 274 (16%) declined to be interviewed, and 133 (8%) never responded or were not interviewed because of illness, impairment or other reasons. This left 1,321 eligible cases in the study, for a participation rate of 76% of the cases we attempted to contact and an overall response rate of 59% of the living and deceased cases presumed to be eligible. Response rates were higher for patients with follicular lymphoma (67%) than DLBCL (51%). Approximately 60% of cases were interviewed within six months of their diagnosis, and 84% were interviewed within one year.

Lifestyle Data

In-home interviews were conducted using a computer-assisted personal interview (CAPI). To accommodate a large number of questions, we used a split-sample design, with a core set of questions given to all respondents and the remainder given to participants in either Group A (all African American and 50% of non-African American participants) or Group B (50% of non-African American participants). Prior to the in-person interview, participants were mailed a form for listing residential and job history, and either a family medical history questionnaire (Group A) or a diet and lifestyle questionnaire (Group B). During the interview, the interviewer administered a computer-assisted personal interview (CAPI) that included demographics, height and weight, occupational history, pesticide exposure and hair dye use. The Group A CAPI included an extended medical history and use of illicit drugs, while the Group B CAPI included an abbreviated medical history, cell phone use and sun exposure.

Self-reported height and weight one year before diagnosis was obtained from all participants as part of the CAPI. Alcohol and cigarette smoking (but not other tobacco use) was only collected on Group B participants. Usual intake of beer, white wine, red wine, and liquor one year before diagnosis (and excluding recent changes) were obtained as part of the self-administered modified Block 1995 Health Habits and History Questionnaire.20 Total grams of alcohol intake per week were estimated from the Block database. The smoking history included ever smoking, age start, age stop, and average number of cigarettes smoked per day.

Clinical and Outcome Data

Date of diagnosis, histology, stage, presence of B-symptoms, first course of therapy, date of last follow-up, and vital status were derived from linkage to registry databases at each study site. Histology was coded initially according to the International Classification of Diseases-Oncology (ICD-O), 2nd Edition,21 and this was later updated to the 3rd Edition by each registry.22 We grouped cases into NHL subtypes according to the InterLymph guidelines.23 Data on first course of therapy included use of single or multi-agent chemotherapy, radiation, other therapies exclusive of chemotherapy and/or radiation, and no therapy (presumed to be observation); information on individual agents and doses was not available. The SEER registries collect date and cause of death, but do not collect data on treatment response or disease recurrence or progression. In 2007, we conducted a second linkage to update survival information. Of the 1,321 cases in the original study, we had clinical and follow-up data available for 1286 cases for this analysis.

Statistical Analysis

Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared, and was categorized a priori according to WHO categories of <20, 20–24.9, 25.0–29.9, and 30+ kg/m2. Alcohol use was categorized as non-drinker, alcohol use ≤median of all alcohol users in this study (43.1 g/week) or alcohol use >median of alcohol users; in secondary analyses, we also modeled alcohol as a continuous variable. Smoking was categorized by status (never, former, current), duration (never smoker, <20 and 20+ years), intensity (never smoker, ≤20 (1 pack) and 21+ cigarettes/day), pack-years (never smoker, <17 and17+ pack-years), and time since quitting (never smoker, quit >20 years, quit 10–19 years, quit <10 years, current smokers).

Overall survival was defined as the time from diagnosis to the date of death or last follow-up; patients alive at last follow-up were censored at that time point. Lymphoma-specific survival was defined as death due to lymphoma; other deaths were censored at date of death. We used Cox proportional hazards regression24 to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) after adjusting for clinical and demographic variables (see Table 1 for variables and categories). These analyses were conducted for the entire cohort as well as the subset groups of DLBCL and follicular lymphoma.

Table 1.

Univariate Associations of Demographic and Clinical Factors, NCI-SEER NHL Survival Study, 1998–2007

All NHL
 DLBCL
 Follicular Lymphoma
Factor N %
Dead		 HR (95% CI) N %
Dead		 HR (95% CI) N %
Dead		 HR (95% CI)
Age (years)
    ≤60 714 25.4% 1.00 (reference) 247 23.5% 1.00 (reference) 192 21.4% 1.00 (reference)
    >60 572 45.6% 1.98 (1.63–2.39) 173 49.7% 2.41 (1.72–3.36) 136 38.2% 1.86 (1.23–2.80)
Sex
    Female 595 31.9% 1.00 (reference) 189 33.3% 1.00 (reference) 165 29.7% 1.00 (reference)
    Male 691 36.5% 1.17 (0.97–1.41) 231 35.1% 1.03 (0.74–1.43) 163 27.0% 0.93 (0.62–1.39)
Race
    White 1097 33.5% 1.00 (reference) 368 33.7% 1.00 (reference) 287 27.5% 1.00 (reference)
    Non-white 189 39.7% 1.24 (0.97–1.59) 52 38.5% 1.20 (0.75–1.92) 41 34.1% 1.33 (0.76–2.36)
Education Level (years)
    <12 127 49.6% 1.00 (reference) 45 53.3% 1.00 (reference) 25 32.0% 1.00 (reference)
    12 – 15 796 33.2% 0.58 (0.44–0.77) 252 30.2% 0.45 (0.29–0.72) 213 28.6% 0.89 (0.43–1.87)
    16+ 362 31.8% 0.57 (0.42–0.77) 122 36.1% 0.59 (0.36–0.96) 90 26.7% 0.83 (0.37–1.84)
B Symptoms
    No 496 29.6% 1.00 (reference) 146 28.8% 1.00 (reference) 160 25.6% 1.00 (reference)
    Yes 254 41.7% 1.62 ( 1.26–2.08) 118 39.0% 1.54 (1.01–2.34) 47 29.8% 1.24 (0.67–2.27)
    Unknown 536 35.3% 1.25 (1.01–1.55) 156 35.9% 1.34 (0.90–2.00) 121 36.4% 1.26 (0.81–1.96)
Stage of Disease
    Local/Regional 556 26.4% 1.00 (reference) 240 30.8% 1.00 (reference) 130 18.5% 1.00 (reference)
    Distant 663 42.2% 1.80 (1.48–2.20) 167 38.9% 1.39 (0.99–1.94) 181 35.4% 2.07 (1.29–3.30)
    Unknown 67 22.4% 0.81 (0.48–1.38) 13 38.5% 1.27 (0.52–3.15) 17 29.4% 1.51 (0.57–3.95)
Histologic Subtype
    DLBCL 420 34.3% 1.00 (reference)
    Follicular 328 28.4% 0.77 (0.59–1.00)
    Mantle Cell 54 63.0% 2.36 (1.62–3.43)
    Marginal Zone 115 25.2% 0.65 (0.43–0.96)
    Peripheral T-cell 42 40.5% 1.31 (0.79–2.17)
    CLL/SLL 128 50.8% 1.37 (1.03–1.82)
    Other Subtypes 199 30.2% 0.91 (0.66–1.24)
Open in a new tab

RESULTS

There were 1,286 patients with a median age of 58 years old (range: 20–74 years) at diagnosis from 1998–2000. A slight majority of subjects were male (54%). The most common histologies were DLBCL (33%), follicular (26%), CLL/SLL (10%), marginal zone (9%), mantle cell (4%) and peripheral T-cell (3%). As part of their initial therapy, 68% of patients received some type of chemotherapy, while 27% received radiation therapy (treatments not mutually exclusive).

Through 2007, 442 (34%) patients died, including 144 of 420 DLBCL (34%) and 93 of 328 follicular lymphoma (28%) patients. The median follow-up on living subjects was 7.7 years (range: 1.3–9.5). Age over 60 years, male sex, non-white race, lower education, presence of B-symptoms, distant stage, and NHL subtype (DLBCL, mantle cell, peripheral T-cell, CLL/SLL) were associated with poorer survival in univariate analysis for all NHL, although not all were statistically significant at p<0.05 (Table 1).

Data on smoking and alcohol use were only collected on the cases who were part of the Group B arm of the study (N=550). Of the 471 patients from this arm of the study with non-missing smoking data, 34% were former smokers and 19% were current smokers at the time of diagnosis. After adjustment for clinical and demographic factors in Table 1, former (HR=1.59; 95% CI 1.12–2.26) and current smokers (HR=1.50; 95% CI 0.97–2.29) had poorer survival compared to never smokers (Table 2). Poorer survival was positively associated with longer smoking duration (p<0.001), greater number of cigarettes smoked per day (p=0.009), greater pack-years of smoking (p=0.0005), and shorter time since quitting (p=0.002). Of note, compared to never smokers, patients who had quit >20 years before diagnosis did not have a higher risk of death (HR=1.14; 95% CI 0.70–1.86). All of these associations were somewhat weaker for DLBCL and stronger for follicular lymphoma (Table 2).

Table 2.

Multivariate Adjusted Hazard Ratios (HR) and 95% Confidence Intervals (CI)1 for the Association of Smoking, Alcohol Use and Body Mass Index with Overall Survival for all NHL and for DLBCL and Follicular Lymphoma, NCI-SEER NHL Survival Study, 1998–2007

All NHL
 DLBCL
 Follicular Lymphoma
N %
Dead		 HR (95% CI) N %
Dead		 HR (95% CI) N %
Dead		 HR (95% CI)
Smoking and alcohol use for Group B subjects
  Smoking status
    Never smoker 220 30.0% 1.00 (reference) 86 32.6% 1.00 (reference) 58 22.4% 1.00 (reference)
    Former smoker 162 38.3% 1.59 (1.12–2.26) 54 40.7% 1.36 (0.77–2.40) 40 25.0% 1.98 (0.81–4.80)
    Current smoker 89 37.1% 1.50 (0.97–2.29) 30 33.3% 1.22 (0.58–2.58) 26 42.3% 2.95 (1.27–6.81)
  Smoking duration
    Non-smoker 220 30.0% 1.00 (reference) 86 32.6% 1.00 (reference) 58 22.4% 1.00 (reference)
    Smoker, <20 years 87 31.0% 1.09 (0.67–1.78) 29 31.0% 1.01 (0.47–2.17) 24 25.0% 2.04 (0.74–5.67)
    Smoker, 20+ years 159 44.0% 1.76 (1.25–2.47) 52 42.3% 1.53 (0.87–2.69) 41 34.1% 2.48 (1.11–5.52)
p-trend=0.0009 p-trend=0.16 p-trend=0.03
  Smoking Intensity (cigarettes/day)
    Non-smoker 220 30.0% 1.00 (reference) 86 32.6% 1.00 (reference) 58 22.4% 1.00 (reference)
    Smoker ≤1pack/day 151 35.1% 1.43 (1.00–2.06) 46 37.0% 1.17 (0.64–2.14) 42 28.6% 2.02 (0.88–4.61)
    Smoker >1 pack/day 99 40.4% 1.65 (1.11–2.45) 38 36.8% 1.39 (0.72–2.66) 22 31.8% 2.49 (0.93–6.64)
p-trend=0.009 p-trend=0.32 p-trend=0.04
  Smoking pack-years
    Non-smoker 220 30.0% 1.00 (reference) 86 32.6% 1.00 (reference) 58 22.4% 1.00 (reference)
    <17 pack-years 93 24.7% 0.96 (0.59–1.54) 29 27.6% 0.82 (0.37–1.82) 23 17.4% 1.29 (0.40–4.11)
    17+ pack-years 150 44.7% 1.88 (1.33–2.66) 50 44.0% 1.68 (0.95–2.95) 41 41.5% 2.61 (1.19–5.74)
p-trend=0.0005 p-trend=0.10 p-trend=0.02
  Time since quitting smoking (to diagnosis)
    Never smoker 220 30.0% 1.00 (reference) 86 32.6% 1.00 (reference) 58 22.4% 1.00 (reference)
    Quit >20 years 70 31.4% 1.14 (0.70–1.86) 21 42.9% 1.16 (0.54–2.49) 17 17.6% 1.19 (0.33–4.27)
    Quit 10–19 years 51 37.3% 1.41 (0.85–2.36) 21 42.9% 1.41 (0.66–3.00) 10 20.0% 1.79 (0.38–8.40)
    Quit <10 years 39 51.3% 3.56 (2.12–5.97) 11 36.4% 2.24 (0.76–6.62) 12 33.3% 3.73 (1.07–13.0)
    Current smoker 89 37.1% 1.51 (0.99–2.32) 30 33.3% 1.22 (0.58–2.59) 26 42.3% 3.01 (1.30–7.00)
p-trend=0.002 p-trend=0.312 p-trend=0.005
  Alcohol use
    None 234 33.3% 1.00 (reference) 83 37.3% 1.00 (reference) 66 28.8% 1.00 (reference)
    ≤43.1 g/week2 112 30.4% 1.13 (0.75–1.71) 45 33.3% 1.24 (0.65–2.35) 30 10.0% 0.35 (0.10–1.20)
    >43.1 g/week 112 39.3% 1.55 (1.06–2.27) 38 34.2% 1.48 (0.75–2.94) 26 42.3% 2.16 (0.98–4.77)
p-trend=0.03 p-trend=0.25 p-trend=0.16
BMI for Group A and B subjects combined
  BMI Continuous3 1189 33.9% 1.09 (1.00–1.20) 391 33.8% 1.07 (0.93–1.23) 299 31.1% 1.14 (0.92–1.41)
  BMI WHO Categories:
    Underweight (<20 kg/m2) 55 29.1% 1.00 (0.59–1.68) 17 23.5% 1.20 (0.42–3.42) 15 26.7% 0.76 (0.26–2.23)
    Normal (20–24.9 kg/m2) 368 31.3% 1.00 (reference) 108 30.6% 1.00 (reference) 107 23.4% 1.00 (reference)
    Overweight (25–29.9 kg/m2) 462 33.8% 1.03 (0.81–1.31) 143 32.2% 1.05 (0.67–1.64) 114 30.7% 1.23 (073–2.08)
    Obese (≥30 kg/m2) 304 38.2% 1.32 (1.02–1.70) 123 39.8% 1.37 (0.88–2.14) 63 30.2% 1.49 (0.81–2.72)
p-trend=0.046 p-trend=0.20 p-trend=0.11
BMI for Group B subjects only
  BMI Continuous3 459 33.1% 1.08 (0.93–1.25) 167 35.3% 1.05 (0.85–1.30) 122 26.2% 1.05 (0.77–1.44)
  BMI WHO Categories:
    Underweight (<20 kg/m2) 21 28.6% 1.01 (0.43–2.36) 5 40.0% 1.87 (0.42–8.22) 8 12.5% 0.38 (0.05–3.04)
    Normal (20–24.9 kg/m2) 158 32.3% 1.00 (reference) 52 32.7% 1.00 (reference) 48 27.1% 1.00 (reference)
    Overweight (25–29.9 kg/m2) 185 31.4% 0.88 (0.60–1.28) 64 34.4% 0.93 (0.49–1.77) 46 23.9% 1.01 (0.44–2.30)
    Obese (≥30 kg/m2) 95 38.9% 1.33 (0.86–2.03) 46 39.1% 1.24 (0.63–2.46) 20 35.0% 1.29 (0.51–3.27)
p-trend=0.29 p-trend=0.78 p-trend=0.33
Open in a new tab
1

Adjusted for age at diagnosis (≤60 vs. >60 years), gender, race, education, study site, stage (distant vs. not), chemotherapy, radiation, B symptoms, and histology (for the All NHL group only).

2

Median intake of users; approximately equal to 3.3 cans (355 mL) of beer, 4.6 glasses (118 mL) of wine, or 2.7 shots (44 mL) of liquor per week.

3

One SD (5 kg/m2) change.

Of the 458 patients with data on alcohol use from the Group B arm of the study, 49% consumed alcohol one year before diagnosis, and among alcohol users, the median intake was 43.1 grams/week. After multivariable adjustment for demographic and clinical factors, there was poorer survival for drinkers of more than 43.1 grams/week compared to never drinkers (HR=1.55; 95% CI 1.06–2.27), while there was no association for drinkers of ≤43.1 grams/week (HR=1.13; 95% CI 0.75–1.71) (Table 2). These trends were evident for DLBCL and follicular lymphoma (Table 2), although the point estimates and trend tests did not reach conventional levels of statistical significance at p<0.05. When we modeled alcohol as a continuous variable, the p-values were 0.038 for all NHL, 0.17 for DLBCL, and 0.008 for follicular lymphoma. When we simultaneously modeled wine, beer, and liquor use, no one specific type of alcohol was associated with NHL survival or DLBCL survival (data not shown); for follicular survival, however, there appeared to be a specific association with liquor use (HR=2.90; 95% CI 1.19–7.07). However, due to relatively small numbers, these results should be interpreted with caution.

All 1286 patients were asked to provide height and weight 1 year before diagnosis, and 1189 had useable data. Based on BMI, 5% of patients were classified as underweight, 31% normal weight, 39% overweight, and 26% as obese. As shown in Table 2, after adjustment for clinical and demographic factors, only obese patients showed a survival disadvantage (HR=1.32 compared to normal weight; 95% CI 1.02–1.70). These associations were similar for the patients only in the Group B arm (who had smoking and alcohol data), and were also similar for DLBCL and follicular lymphoma (Table 2). There were no associations with height or weight for NHL overall or by subtype (all p-trend >0.15; data not shown).

We next re-analyzed the associations in Table 2 based on death due to lymphoma (N=273), and the patterns of association were very similar, with the major exceptions that the associations for smoking and survival strengthened for follicular lymphoma and the associations for BMI with survival strengthened for all outcomes (data not shown).

When we included pack-years of smoking, alcohol use and BMI in the same model along with the clinical and demographic variables, the HR smoking remained unchanged for smoking (HR=1.87 for 17+ pack-years versus never smoker; 95% CI 1.29–2.72), nearly identical for BMI (HR=1.38 for BMI >30 versus 20–24.9 kg/m2; 95% CI 0.89–2.15), and slightly attenuated for alcohol use (HR=1.41 for >43.1 grams/week versus no alcohol; 95% CI 0.93–2.15). Similar results were observed for DLBCL and follicular lymphoma (data not shown). These results provide some evidence that these associations were independent of each other, although a much larger sample size would be required to provide a more definitive assessment with respect to statistical significance.

DISCUSSION

In this analysis of a large, population-based sample of NHL cases, we found that smoking cigarettes, drinking more than 43.1 grams of alcohol per week – approximately 3.3 cans (355 mL) of beer, 4.6 glasses (118 mL) of wine, or 2.7 shots (44 mL) of liquor per week – and being obese prior to diagnosis each had a negative impact on overall survival after accounting for clinical and demographic variables. Associations remained after simultaneous inclusion of all three variables in the same statistical model, although some of the associations were no longer statistically significant, and therefore the latter observation is only preliminary. Finally, these associations held for lymphoma-specific survival, and were broadly similar for both DLBCL and follicular lymphoma, with the exception that the associations with smoking were stronger for follicular lymphoma.

This is the first report to simultaneously assess these associations on survival, and to evaluate them in a North American population, and replication is needed. While based on observational data, there does not appear to be obvious explanations for these findings due to bias, confounding or chance, although there may be residual confounding that we have not been able to fully address with the clinical and demographic variables that were measured. Furthermore, there may also be unmeasured confounding by lifestyle and other factors that are closely correlated with smoking, alcohol use and obesity. Alternatively, these associations could have biological plausibility in this context, and could represent a causal association.

Smoking

Our results for smoking are broadly consistent with two prior studies from Italy.16, 17 In a population-based study of 1138 patients diagnosed from 1991–1993 and followed through 2002, Battaglioli and colleagues 16 reported that the number of years smoked (HR=1.26 for >39 years versus ≤25 years, 95% CI 0.94–1.70) and number of pack-years (HR=1.60 for >31 pack-years versus ≤14 pack-years, 95% CI 1.18–2.18) were associated with poorer survival after adjustment for gender, age, and education, although there was no association by smoking status (never, former, current) or years since quitting smoking. These associations were similar when the outcome was restricted to death due to lymphoma. In a second study of 268 NHL patients enrolled from 1983 to 2002, Talamini and colleagues17 reported that number of cigarettes per day (HR=1.80 for ≥20 versus never smoked, 95% CI 1.06–2.73) and to a lesser extent duration of smoking (HR=1.32 for ≥30 years versus never smoked, 95% CI 0.85–2.05) were associated with poorer survival after adjustment for age, sex, B-symptoms and the IPI. With respect to NHL subtypes, the first study reported stronger associations for follicular lymphoma16 while the second smaller study reported stronger associations for follicular lymphoma and CLL/SLL.17 These findings for follicular lymphoma are consistent with our results; we had too few CLL/SLL cases in our study to evaluate survival.

Cigarette smoking could negatively influence NHL survival through multiple mechanisms,13, 25 including a direct carcinogen impact; activation of other carcinogens through induction of metabolizing enzymes; influence on immunologic function, including increasing pro-inflammatory cytokines and T-cell function; and impact on the prevalence of the t(14;18) translocation and subsequent overexpression of bcl-2, which can inhibit apoptosis. Smoking may also impact treatment efficacy, and smokers also tend to have more comorbidities that can also impact survival.25

Alcohol Use

Both of the Italian studies also reported positive associations with alcohol use. Compared to non-drinkers, drinkers of any alcohol (HR=1.41; 95% CI 1.10–1.81), drinking >32 versus ≤13 grams of alcohol per day (HR=1.41; 95% CI 1.05–1.90), and longer duration of drinking (HR=1.55 for >43 years versus ≤10; 95% CI 1.13–2.14) were all associated with poorer survival in one study,16 while a greater number of drinks per day (HR=1.69 for ≥4 versus <2; 95% CI 1.04–2.76) was associated with poorer survival in the other study.17 These associations were broadly elevated for the common subtypes. The poorer survival associated with any use of alcohol is consistent with our findings, although it must be noted that the Italian studies had a much higher level of alcohol use than our study, raising concerns about the nature of any dose-response biologic effect. With respect to risk of developing NHL, light to moderate alcohol use appears to be protective for NHL overall and for the major subtypes,14 which is the opposite of its association with outcome observed to date. The reasons for this are not clear, but if real, would suggest different biologic mechanisms for the two associations. Alcohol can act as both a direct and indirect carcinogen,26 and may also impact treatment efficacy.27 In contrast, alcohol has immunomodulatory effects28 that have been hypothesized to protect against the development of NHL at light to moderate levels of intake,29 complicating interpretation of these findings.

Obesity

Only a single study has evaluated the role of obesity in NHL prognosis. Tarella and colleagues18 reported on 121 lymphoma patients enrolled in Italy from 1990–1997 who received high-dose chemotherapy and autograft and were followed for a median of 3 years. Patients with a BMI ≥28 kg/m2 had a poorer event-free (HR=2.8; 95% CI 1.5–5.3) and overall survival (RR=2.9; 95% CI: 1.3–6.2) compared to patients with a BMI of <28 kg/m2 after adjustment for gender, IPI, bone marrow infiltration, and histology; histology-specific results were not provided.

In this study, when smoking, alcohol and obesity were included in the same model that also adjusted for clinical and demographic factors, the associations were largely unchanged, suggesting that these may be independent prognostic factors. Both Battaglioli et al.16 and Talamini et al.17 reported a similar result when they simultaneously adjusted for smoking and alcohol use; however, no other study has simultaneously assessed all three factors.

Obesity can affect cancer through multiple mechanisms, including an impact on estrogen, androgen, insulin and growth factor pathways.30 Obesity is associated with a pro-inflammatory state in general and the release of TNFα in particular, both of which have been suggested as NHL risk 31, 32 and prognostic factors.10, 11 There is also the potential for underdosing of chemotherapy in obese individuals.33 While all of these factors can negatively impact existing comorbidities in NHL patients and therefore decrease overall survival, our finding that these associations remain when assessing lymphoma-specific survival suggest a more direct impact of these factors on lymphoma and/or its treatment.

Strengths and Limitations

Strengths of this study include the relatively large sample size, population-based case ascertainment, and relatively long-term and nearly complete follow-up. Our results should be generalizable to community patients. There are also limitations. Data on smoking, alcohol and BMI were self-reported, but were reported prior to outcome. We also only collected smoking and alcohol data on a subset of cases, limiting sample size for multivariate models. Alcohol use was light, and we could not assess heavier use such at that seen in the Italian studies. We were unable to assess event-free survival, rarer NHL subtypes, or to adjust for standard prognostic factors such as the IPI or FLIPI, or detailed treatment. Nevertheless, these clinical and demographic factors have been shown to predict outcome similar to the IPI or FLIPI in this dataset.10, 11 Additional limitations include the lack of data on change in these factors after diagnosis; diagnosis and initial treatment of cases from 1998–2000, prior to the widespread use of rituximab as standard of therapy for B-cell lymphoma;34, 35 and a largely white study population. Future studies should address these limitations.

Conclusions

In conclusion, our findings for an adverse impact of smoking, alcohol use, and obesity are consistent with data from three smaller studies from Italy, and thus supports investigating whether smoking cessation, decrease in alcohol intake, or weight loss after diagnosis of NHL can improve outcomes.

Acknowledgments

We thank Peter Hui for data management support, and Sondra Buehler for secretarial support.

Footnotes

Conflict of Interest Disclosures

Supported by the National Cancer Institute (grants R01 CA96704 and P50 CA97274; NCI Intramural Program; SEER contracts N01-PC-67010, N01-PC-67008, N01-PC-67009, N01-PC-65064, N02-PC-71105).

References

  • 1.Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA Cancer J Clin. 2008;58(2):71–96. doi: 10.3322/CA.2007.0010. [DOI] [PubMed] [Google Scholar]
  • 2.Ries LAG, Melbert D, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, et al. SEER Cancer Statistics Review, 1975–2005. Bethesda, MD: National Cancer Institute; 2008 http://seer.cancer.gov/csr/1975_2005/, based on November 2007 SEER data submission, posted to the SEER web site, 2008.
  • 3.Shipp MA, Harrington DP, Anderson JR, et al. A predictive model for aggressive non-Hodgkin's lymphoma. The International Non-Hodgkin's Lymphoma Prognostic Factors Project. N Engl J Med. 1993;329(14):987–994. doi: 10.1056/NEJM199309303291402. [DOI] [PubMed] [Google Scholar]
  • 4.Solal-Celigny P, Roy P, Colombat P, White J, Armitage JO, Arranz-Saez R, et al. Follicular lymphoma international prognostic index. Blood. 2004;104(5):1258–1265. doi: 10.1182/blood-2003-12-4434. [DOI] [PubMed] [Google Scholar]
  • 5.Gascoyne RD. Emerging prognostic factors in diffuse large B cell lymphoma. Curr Opin Oncol. 2004;16(5):436–441. doi: 10.1097/00001622-200409000-00004. [DOI] [PubMed] [Google Scholar]
  • 6.Lossos IS, Morgensztern D. Prognostic biomarkers in diffuse large B-cell lymphoma. J Clin Oncol. 2006;24(6):995–1007. doi: 10.1200/JCO.2005.02.4786. [DOI] [PubMed] [Google Scholar]
  • 7.Weng WK, Levy R. Two immunoglobulin G fragment C receptor polymorphisms independently predict response to rituximab in patients with follicular lymphoma. J Clin Oncol. 2003;21(21):3940–3947. doi: 10.1200/JCO.2003.05.013. [DOI] [PubMed] [Google Scholar]
  • 8.Carlotti E, Palumbo GA, Oldani E, Tibullo D, Salmoiraghi S, Rossi A, et al. FcγRIIIA and FcγRIIA polymorphisms do not predict clinical outcome of follicular non-Hodgkin's lymphoma patients treated with sequential CHOP and rituximab. Haematologica. 2007;92(8):1127–1130. doi: 10.3324/haematol.11288. [DOI] [PubMed] [Google Scholar]
  • 9.Hohaus S, Mansueto G, Massini G, D'Alo F, Giachelia M, Martini M, et al. Glutathione-S-transferase genotypes influence prognosis in follicular non-Hodgkin's Lymphoma. Leuk Lymphoma. 2007;48(3):564–569. doi: 10.1080/10428190601158647. [DOI] [PubMed] [Google Scholar]
  • 10.Cerhan JR, Wang S, Maurer MJ, Ansell SM, Geyer SM, Cozen W, et al. Prognostic significance of host immune gene polymorphisms in follicular lymphoma survival. Blood. 2007;109(12):5439–5446. doi: 10.1182/blood-2006-11-058040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Habermann TM, Wang SS, Maurer MJ, Morton LM, Lynch CF, Ansell SM, et al. Host immune gene polymorphisms in combination with clinical and demographic factors predict late survival in diffuse large B-cell lymphoma patients in the pre-rituximab era. Blood. 2008;112(7):2694–2702. doi: 10.1182/blood-2007-09-111658. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Wang SS, Maurer MJ, Morton LM, Habermann TM, Davis S, Cozen W, et al. Polymorphisms in DNA repair and one-carbon metabolism genes and overall survival in diffuse large B-cell lymphoma and follicular lymphoma. Leukemia. 2009;23(3):596–602. doi: 10.1038/leu.2008.240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Morton LM, Hartge P, Holford TR, Holly EA, Chiu BC, Vineis P, et al. Cigarette smoking and risk of non-Hodgkin lymphoma: a pooled analysis from the International Lymphoma Epidemiology Consortium (interlymph) Cancer Epidemiol Biomarkers Prev. 2005;14(4):925–933. doi: 10.1158/1055-9965.EPI-04-0693. [DOI] [PubMed] [Google Scholar]
  • 14.Morton LM, Zheng T, Holford TR, Holly EA, Chiu BC, Costantini AS, et al. Alcohol consumption and risk of non-Hodgkin lymphoma: a pooled analysis. Lancet Oncol. 2005;6(7):469–476. doi: 10.1016/S1470-2045(05)70214-X. [DOI] [PubMed] [Google Scholar]
  • 15.Willett EV, Morton LM, Hartge P, Becker N, Bernstein L, Boffetta P, et al. Non-Hodgkin lymphoma and obesity: a pooled analysis from the InterLymph Consortium. Int J Cancer. 2008;122(9):2062–2070. doi: 10.1002/ijc.23344. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Battaglioli T, Gorini G, Costantini AS, Crosignani P, Miligi L, Nanni O, et al. Cigarette smoking and alcohol consumption as determinants of survival in non-Hodgkin's lymphoma: a population-based study. Ann Oncol. 2006;17(8):1283–1289. doi: 10.1093/annonc/mdl096. [DOI] [PubMed] [Google Scholar]
  • 17.Talamini R, Polesel J, Spina M, Chimienti E, Serraino D, Zucchetto A, et al. The impact of tobacco smoking and alcohol drinking on survival of patients with non-Hodgkin lymphoma. Int J Cancer. 2008;122(7):1624–1629. doi: 10.1002/ijc.23205. [DOI] [PubMed] [Google Scholar]
  • 18.Tarella C, Caracciolo D, Gavarotti P, Argentino C, Zallio F, Corradini P, et al. Overweight as an adverse prognostic factor for non-Hodgkin's lymphoma patients receiving high-dose chemotherapy and autograft. Bone Marrow Transplant. 2000;26(11):1185–1191. doi: 10.1038/sj.bmt.1702692. [DOI] [PubMed] [Google Scholar]
  • 19.Chatterjee N, Hartge P, Cerhan JR, Cozen W, Davis S, Ishibe N, et al. Risk of non-Hodgkin's lymphoma and family history of lymphatic, hematologic, and other cancers. Cancer Epidemiology, Biomarkers & Prevention. 2004;13(9):1415–1421. [PubMed] [Google Scholar]
  • 20.Block G, Coyle LM, Hartman AM, Scoppa SM. Revision of dietary analysis software for the Health Habits and History Questionnaire. American Journal of Epidemiology. 1994;139(12):1190–1196. doi: 10.1093/oxfordjournals.aje.a116965. [DOI] [PubMed] [Google Scholar]
  • 21.Percy C, Van Holten V, Muir C. Geneva: World Health Organization; International classification of diseases for oncology. (second edition) 1990
  • 22.Fritz A, Percy C, Jack A, Shanmugaratnam K, Sobin L, Parkin DM, et al. Geneva: World Health Organization; International Classification of Diseases for Oncology. (Third Edition) 2000
  • 23.Morton LM, Turner JJ, Cerhan JR, Linet MS, Treseler PA, Clarke CA, et al. Proposed classification of lymphoid neoplasms for epidemiologic research from the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) Blood. 2007;110(2):695–708. doi: 10.1182/blood-2006-11-051672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Cox DR. Regression models and life tables (with discussion) Journal of the Royal Statistical Society B. 1972;34:187–220. [Google Scholar]
  • 25.Gritz ER, Lam CY, Vidrine DJ, Cororve Fingeret M. Cancer Prevention: Tobacco dependence and its treatment. In: DeVita V, Lawrence T, Rosenberg S, editors. Cancer: Principles and Practice of Oncology. ed. 8. Philadelphia, PA: Lippincott, Williams and Wilkins; 2008. pp. 593–608. [Google Scholar]
  • 26.Seitz HK, Stickel F. Molecular mechanisms of alcohol-mediated carcinogenesis. Nat Rev Cancer. 2007;7(8):599–612. doi: 10.1038/nrc2191. [DOI] [PubMed] [Google Scholar]
  • 27.Gritz ER, Demark-Wahnefried W. Health behaviors influence cancer survival. J Clin Oncol. 2009;27(12):1930–1932. doi: 10.1200/JCO.2008.21.3769. [DOI] [PubMed] [Google Scholar]
  • 28.Diaz LE, Montero A, Gonzalez-Gross M, Vallejo AI, Romeo J, Marcos A. Influence of alcohol consumption on immunological status: a review. Eur J Clin Nutr. 2002;56 Suppl 3:S50–S53. doi: 10.1038/sj.ejcn.1601486. [DOI] [PubMed] [Google Scholar]
  • 29.Chiu BC, Cerhan JR, Gapstur SM, Sellers TA, Zheng W, Lutz CT, et al. Alcohol consumption and non-Hodgkin lymphoma in a cohort of older women. British Journal of Cancer. 1999;80(9):1476–1482. doi: 10.1038/sj.bjc.6690547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nature Reviews Cancer. 2004;4(8):579–591. doi: 10.1038/nrc1408. [DOI] [PubMed] [Google Scholar]
  • 31.Cerhan JR, Liu-Mares W, Fredericksen ZS, Novak AJ, Cunningham JM, Kay NE, et al. Genetic variation in tumor necrosis factor and the nuclear factor-{kappa}B canonical pathway and risk of non-Hodgkin's Lymphoma. Cancer Epidemiol Biomarkers Prev. 2008;17(11):3161–3169. doi: 10.1158/1055-9965.EPI-08-0536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Rothman N, Skibola CF, Wang SS, Morgan G, Lan Q, Smith MT, et al. Genetic variation in TNF and IL10 and risk of non-Hodgkin lymphoma: a report from the InterLymph Consortium. Lancet Oncol. 2006;7(1):27–38. doi: 10.1016/S1470-2045(05)70434-4. [DOI] [PubMed] [Google Scholar]
  • 33.Griggs JJ, Sabel MS. Obesity and cancer treatment: weighing the evidence. J Clin Oncol. 2008;26(25):4060–4062. doi: 10.1200/JCO.2008.17.4250. [DOI] [PubMed] [Google Scholar]
  • 34.Hochster H, Weller E, Gascoyne RD, Habermann TM, Gordon LI, Ryan T, et al. Maintenance rituximab after cyclophosphamide, vincristine, and prednisone prolongs progression-free survival in advanced indolent lymphoma: results of the randomized phase III ECOG1496 Study. J Clin Oncol. 2009;27(10):1607–1614. doi: 10.1200/JCO.2008.17.1561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Coiffier B, Lepage E, Briere J, Herbrecht R, Tilly H, Bouabdallah R, et al. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. N Engl J Med. 2002;346(4):235–242. doi: 10.1056/NEJMoa011795. [DOI] [PubMed] [Google Scholar]

RESOURCES