Abstract
The epidemiologic evidence regarding the relationship between alcohol consumption and multiple myeloma (MM) risk remains limited and inconsistent, although recent studies suggest a potential protective effect. We prospectively investigated the risk of MM in relation to alcohol consumption frequency among 499,292 participants enrolled in the National Institutes of Health (NIH)-AARP Diet and Health Study in 1995–1996. A total of 1,312 MM cases were identified during follow-up through December 2011. Hazard ratios (HR) and 95% confidence intervals (CI) for categories of alcohol consumption relative to those defined as light drinkers (<1 drink/week) were estimated using multivariate Cox proportional hazard models. Overall, increasing frequency of alcohol consumption was inversely associated with MM (P-trend=0.01), with a statistically significant association among those who consumed 2 drinks per day (HR=0.70, 95% CI: 0.50, 0.98); similar but not statistically signifant associations were observed for greater frequency of alcohol consumption. Among women, risk of MM was reduced among those who consumed less that one drink per day (HR=0.73, 95% CI: 0.56, 0.97) and associations with greater frequency of alcohol consumption were inverse although not statistically significant. The findings of this large prospective investigation suggest that moderate alcohol consumption may be associated with reduced future risk of MM.
Keywords: multiple myeloma, alcohol, cohort study
Article category: Research article, Cancer Epidemiology
Introduction
Multiple myeloma (MM) is a B-cell malignancy characterized by accumulation of malignant plasma cells in the bone marrow. It is the second most common hematological malignancy, and an estimated 24,280 new cases were diagnosed in the US in 20161. Older age, male sex, African ancestry, family history of hematological malignancies, and obesity are established risk factors for MM2, 3. Among the potential modifiable risk factors, alcohol consumption has been inconsistently associated with MM. Statistically significant or at least marginal inverse associations between alcohol intake and MM risk have been reported in case-control studies4–7, including a more recent pooled analysis of six studies8; several of these investigations noted somewhat stronger inverse associations with MM for greater frequency of alcohol consumption6, 7. However, other case-control studies found no association9–13. Evidence from cohort studies is suggestive of an inverse association with MM risk; a prospective investigation in the UK Million women study found that greater frequency of alcohol consumption was associated with a statistically significant reduced risk of MM14, and another prospective study in Sweden observed a lower risk of MM among individuals with alcohol use disorders15. Other cohorts reported non-statistically significant inverse associations with frequency of alcohol consumption16–18, and one study found an increased risk of MM in light drinkers compared to non-drinkers, but noted an inverse trend with alcohol consumption frequency19. To better elucidate the association between alcohol consumption and MM, we prospectively investigated this relationship in the National Institutes of Health (NIH)-AARP Diet and Health Study, a cohort of approximately half a million adults in the United States.
METHODS
Study population
Details of the NIH-AARP Diet and Health Study design have been described20. Briefly, a baseline questionnaire was mailed to AARP members who were 50 to 71 years of age and resided in California, Florida, Louisiana, New Jersey, North Carolina, Pennsylvania or two metropolitan areas (Atlanta, Georgia, and Detroit, Michigan). A total of 567,169 satisfactorily completed questionnaires were returned between October 1995 and February 1997.
Records were excluded from our analysis if they met any of the following criteria: the questionnaire was completed in duplicate (n=179), the participant died or moved out of the study area before returning the questionnaire (n= 582), the participant withdrew from the study (n=10), the questionnaire was completed by a proxy (15,760), or the participant had history of cancer as determined by self-report or registry data (n= 51,346). After these exclusions, our final baseline analytical cohort included 499,292 participants (297,823 males and 201,469 females).
Case ascertainment
Incident cases of MM were identified through linkage to cancer registries in the eight states where recruitment took place, and in three additional states (Arizona, Nevada, and Texas) to capture cases among participants who moved to those areas during follow-up. Case ascertainment has been estimated to be approximately 90% complete21. Participants were followed from the date that each questionnaire was received until the first cancer was diagnosed, or until the participant moved out of the study area, died or the follow-up period ended on December 31, 2011. Vital status was determined through linkage to NDI plus. We defined MM cases as those that were assigned a histology code of 9732 according to the International Classification of Diseases for Oncology, Third Edition. A total of 1,312 cases of MM were identified during 5,910,841 person-years of follow-up.
Exposure assessment
Alcohol consumption in the past 12 months was assessed at baseline with the self-administered questionnaire. Information on the usual frequency and quantity of alcohol consumption was provided by each participant. Servings of alcoholic drinks per day were ascertained for total alcohol consumed and for each type of alcoholic beverage (i.e., beer, wine and liquor). One drink was defined based on the US Department of Agriculture MyPyramid Serving Equivalent Database with one alcoholic drink corresponding to 12 fluid ounces of beer (12.96 g of ethanol), 5 fluid ounces of wine (13.72 g of ethanol), and 1.5 fluid ounces of 80 proof distilled liquor (13.93 g of ethanol). MM risk was assessed according to the frequency of total alcohol consumption: none, <1 drink per week, 1 drink per week to <1 drink per day, 1–1.9 drinks per day, 2–2.9 drinks per day, 3–3.9 drinks per day, and ≥4 drinks per day. We also evaluated alcohol intake as a continuous variable (per 10 g/day increment) and using alternate category cut points based on g/day (none, <5 g/day; ≥5- <10 g/day; ≥10-<20 g/day; ≥20-<30 g/day and ≥30g/day).
Statistical analysis
Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression models with person-years as the underlying time metric. Consistent with previous studies including the UK Million Women Study14, 16, 22–24, for the referent group we selected participants who were light drinkers, defined here as those who consumed less than 1 drink per week. We used this approach to reduce the potential for confounding by unmeasured characteristics, reverse causation, and misclassification of exposure. The questionnaire inquired about alcohol consumption in the past 12 months; it is possible that some participants who abstained from alcohol consumption (non-drinkers) might have had underlying health issues that affected their drinking patterns, whereas others might maintain a generally healthier lifestyle. It is also possible that former drinkers who have recently stopped would be classified as non-drinkers based on their questionnaire responses, however we cannot rule out the possibility that our referent category may include some former heavy drinkers. All statistical models were adjusted for age at study entry, race (non-Hispanic white, non-Hispanic black, Hispanic, other and missing), sex (in the analysis with men and women combined), and BMI (<18.5 kg/m2, 18.5–24.9 kg/m2, 25–29.9 kg/m2, ≥30 kg/m2, unknown). Additional analyses were performed with further adjustment for smoking status, physical activity and level of education. Tests for linear trend were conducted by modeling alcohol intake as a continuous variable per 10 g/day increment; non-drinkers and high outlying values (defined as alcohol intake >196 g/day) were excluded from the analyses of trend. We also performed analyses stratified by sex and by BMI category; multiplicative interaction was assessed using Wald tests. Sensitivity analyses restricted to non-Hispanic whites, excluding ouliers (alcohol intake >196 g/day) and excluding cases and person-time during the first 2 years of follow-up were conducted. All the analyses were done using SAS, version 9.4 software (SAS Institute, Inc., Cary, North Carolina). All p-values were considered statistically significant if less than 0.05.
RESULTS
Of the 499,292 participants remaining after exclusions, approximately 91% were non-Hispanic whites, 60% were male, and the median age at baseline was 63 (interquartile range 58–67) years. Over 75% of participants reported consuming alcohol in the past 12 months. As shown in Table 1, non-Hispanic whites were more likely to consume alcohol compared with other racial/ethnic groups, men reported greater frequency of alcohol consumption than women, and the prevalence of overweight or obesity was somewhat lower among moderate drinkers (e.g., 60–61% of those consuming 1 or 2 drinks/day) compared with non-drinkers (67%) and heavy drinkers (68%) with non-missing BMI values.
Table 1.
Baseline characteristics of study participants by alcohol intake, NIH-AARP Diet and Health Study, 1995–2011 (n=499,292)
| Alcohol intakea | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristic | None | <1 drink/week | ≥1drink/week- | 1–1.9 drinks/day | 2–2.9 drinks/day | 3–3.9 drinks/day | ≥4 drinks/day | |||||||
| No. | (%) | No. | (%) | No. | (%) | No. | (%) | No. | (%) | No. | (%) | No. | (%) | |
| Age at baseline b | 62.4 | (5.3) | 61.9 | (5.4) | 61.7 | (5.4) | 62.4 | (5.3) | 61.9 | (5.4) | 62.1 | (5.3) | 62.0 | (5.30) |
| Sex | ||||||||||||||
| Male | 62286 | (51.0) | 61264 | (45.8) | 85703 | (66.5) | 41776 | (72.2) | 13732 | (75.9) | 10214 | (81.5) | 22848 | (87.7) |
| Female | 59770 | (49.0) | 72619 | (54.2) | 43107 | (33.5) | 16107 | (27.8) | 4369 | (24.1) | 2295 | (18.4) | 3202 | (12.3) |
| Race | ||||||||||||||
| Whitec | 106128 (87.0) | 120927 | (90.3) | 119267 | (92.6) | 54725 | (94.5) | 17110 | (94.5) | 11785 | (94.2) | 24571 | (94.3) | |
| Blackc | 7886 | (6.5) | 5416 | (4.1) | 3944 | (3.1) | 1186 | (2.1) | 396 | (2.2) | 355 | (2.8) | 678 | (2.6) |
| Hispanic | 2276 | (1.9) | 2956 | (2.2) | 2661 | (2.1) | 920 | (1.6) | 284 | (1.6) | 164 | (1.3) | 361 | (1.4) |
| Other | 3135 | (2.6) | 2701 | (2.0) | 1539 | (1.2) | 515 | (0.9) | 137 | (0.8) | 82 | (0.7) | 196 | (0.8) |
| Unknown | 2631 | (2.2) | 1883 | (1.4) | 1399 | (1.1) | 537 | (0.9) | 174 | (1.0) | 123 | (1.0) | 244 | (1.0) |
| BMI d | ||||||||||||||
| <18.5 | 1488 | (1.2) | 1341 | (1.0) | 1085 | (0.8) | 601 | (1.0) | 196 | (1.1) | 135 | (1.1) | 246 | (0.9) |
| ≥18.5 to <25 | 37201 | (30.5) | 44028 | (32.9) | 44777 | (34.8) | 22390 | (38.7) | 6653 | (36.8) | 4119 | (32.9) | 7917 | (30.4) |
| ≥25 to <30 | 46993 | (38.5) | 52652 | (39.3) | 56092 | (43.6) | 25357 | (43.8) | 8075 | (44.6) | 5818 | (46.5) | 12198 | (49.8) |
| ≥ 30 | 32131 | (26.3) | 32579 | (24.3) | 24368 | (18.9) | 8497 | (14.7) | 2845 | (15.7) | 2207 | (17.6) | 5151 | (19.8) |
| Unknown | 4243 | (3.5) | 3283 | (2.5) | 2488 | (1.9) | 1038 | (1.8) | 332 | (1.8) | 230 | (1.8) | 538 | (2.1) |
The approximate corresponding values for alcohol intake in grams/day are as follows: <1 drink/week = <2 g/day; ≥1 drink/week - <1 drink/day = ≥2 g/day - <14g/day; 1–1.9 drinks/day = ≥14 - <28 g/day; 2–2.9 drinks/day = ≥28 - <42 g/day; 3–3.9 drinks/day = ≥42 - <56 g/day; ≥4 drinks/day = ≥56 g/day.
mean (standard deviation)
non-Hispanic
Body mass index at baseline in kg/m2
Among men and women combined, greater frequency of alcohol consumption was inversely associated with MM risk, with an estimated 4% decrease in risk per 10g/day increase in alcohol intake (p-trend=0.01; Table 2). Compared with light drinkers, we observed a statistically significant reduced risk of MM among those who consumed 2 to 2.9 drinks per day (HR=0.70, 95% CI: 0.50, 0.98), but non-statistically significant reduced risks of MM in other categories of alcohol consumption. A similar pattern of association was observed when alcohol intake was defined in categories of g/day (Supplementary Table 1). Our findings were essentially unchanged after further adjusting for smoking status, level of education and level of physical activity (data not shown) and after exclusion of outliers (alcohol intake > 196 g/day). When we evaluated MM risk in separate analyses of beer, wine and liquor consumption, we found no association between any specific alcohol type and MM risk after adjusting for race, age, sex and other types of alcoholic beverages (data not shown).
Table 2.
Hazard ratios and 95% CI for the risk of MM in relation to alcohol consumption, NIH-AARP Diet and Health Study, 1995–2011 (n=499,292).
| Men and women combineda | Menb | Womenb | |||||
|---|---|---|---|---|---|---|---|
| Alcohol consumptionb | Nocases | HR (95% CI) | Nocases | HR (95% CI) | Nocases | HR (95% CI) | P interaction |
| None | 312 | 0.89 (0.76, 1.03) | 177 | 0.89 (0.73, 1.09) | 135 | 0.89 (0.71, 1.12) | |
| <1 drink/wk | 371 | 1.00 | 200 | 1.00 | 171 | 1.00 | |
| <1 drink/day | 346 | 0.91 (0.78, 1.05) | 275 | 0.99 (0.82, 1.19) | 71 | 0.73 (0.56, 0.97) | |
| 1–1.9 drinks/day | 156 | 0.88 (0.73, 1.07) | 129 | 0.93 (0.75, 1.16) | 27 | 0.77 (0.51, 1.16) | |
| 2–2.9 drinks/day | 38 | 0.70 (0.50, 0.98) | 32 | 0.73 (0.50, 1.06) | 6 | 0.66 (0.29, 1.50) | |
| 3–3.9 drinks/day | 27 | 0.72 (0.49, 1.07) | 23 | 0.73 (0.47, 1.12) | 4 | 0.80 (0.30, 2.16) | |
| >=4 drinks/day | 62 | 0.79 (0.60, 1.04) | 57 | 0.82 (0.61, 1.10) | 5 | 0.76 (0.31, 1.85) | |
| Per 10g/day increasec | 987 | 0.96 (0.93–0.99) | 703 | 0.96 (0.93–0.99) | 284 | 0.93 (0.85–1.03) | |
| P trend c | 0.01 | 0.02 | 0.17 | 0.52 | |||
Adjusted for race, age (continuous variable), sex, and BMI.
Adjusted for race, age (continuous variable) and BMI.
Among drinkers, excluding high outlying values (>196 g/day), adjusted for race, age (continuous variable), sex, and BMI
The approximate corresponding values for alcohol intake in grams/day are as follows: <1 drink/week = <2 g/day; ≥1 drink/week - <1 drink/day = ≥2 g/day - <14g/day; 1–1.9 drinks/day = ≥14 - <28 g/day; 2–2.9 drinks/day = ≥28 - <42 g/day; 3–3.9 drinks/day = ≥42 - <56 g/day; ≥4 drinks/day = ≥56 g/day.
The significant associations between alcohol use and MM risk remained in lagged analyses excluding 139 cases diagnosed during the first two years of follow-up; we observed stronger inverse associations with MM risk for consumption of 2 to 2.9 drinks per day (HR=0.67, 95% CI: 0.46, 0.96) or 3 to 3.9 drinks per day (HR=0.63, 95% CI: 0.41, 0.99), with a statistically significant dose-response trend (P-trend=0.016). In sensitivity analyses restricted to non-Hispanic whites, the patterns of association with MM were similar although no longer statistically significant (e.g., 2–2.9 drinks/day vs. <1 drink/week, HR=0.73, 95% CI: 0.52, 1.03; data not shown).
In sex-specific analyses, we observed a statistically significant reduced risk of MM with increasing alcohol consumption among men (HR per 10g/day increase = 0.96; p-trend=0.02). Among women, the dose-response trend per 10g/day increase in alcohol intake was not statistically significant; however, we observed a reduced risk of MM among those who consumed less than 1 drink per day (HR=0.73, 95% CI: 0.56, 0.97) and associations with more frequent alcohol consumption were inverse although not statistically significant. A test of interaction between alcohol consumption and sex was not statistically significant (P-interaction=0.52). When alcohol intake was modeled in categories of g/day, we found that alcohol intake of ≥30 g/day was associated with a reduced risk of MM among men (HR=0.79, 95% CI: 0.63, 0.98), whereas intake of 5-<10 g/day was associated with reduced MM risk among women (HR=0.53, 95% CI: 0.32, 0.87; Supplementary Table 1). In analyses stratified by BMI, the inverse associations with MM risk for moderate alcohol consumption were more apparent among participants who were overweight (BMI ≥25-<30 kg/m2: 2–2.9 drinks per day, HR=0.45, 95% CI: 0.25, 0.81; Supplementary Table 2). We noted a similar pattern of association with moderate alcohol consumption among obese individuals, although findings in this group did not achieve statistical significance. A test of interaction between alcohol consumption and BMI was not statistically significant (p-interaction=0.10).
Discussion
In this large prospective investigation in the NIH-AARP Diet and Health Study, we found that moderate frequency of alcohol consumption was associated with a reduced risk of MM development. Our findings are consistent with those from an investigation in the UK Million Women Study noting reduced MM risk with moderate alcohol consumption14 and evidence from other cohort studies is generally suggestive of inverse associations with MM for increasing frequency of alcohol consumption15–18. A recent meta-analysis of results from 10 cohort studies and 16 case-control studies reported a statistically significant inverse association with moderate alcohol consumption (12.5–50 grams of ethanol per day) (pooled RR = 0.87, 95% CI: 0.77, 0.99), whereas there was limited evidence of an association among light drinkers (≤ 12.5 grams per ethanol per day) or heavy drinkers (≥ 50 grams of ethanol per day)25. Although our definition of moderate drinking (2–2.9 drinks per day, or approximately 28 to 42 grams of ethanol per day) is narrower than the one in the meta-analysis, our results are consistent with their findings.
Notably, we also observed a statistically significant reduced risk of MM among women who consumed less than 1 drink per day. A similar association with light drinking (3-<7 drinks per week) was observed in the UK Million Women Study14, and in the recent meta-analysis the protective effect of alcohol consumption on MM risk was more apparent among women compared with men25. Taken together, these findings may reflect gender differences in alcohol metabolism that could potentially lead to differences by sex in the relationship between alcohol consumption frequency and MM26.
The potential biological mechanisms by which alcohol might protect against MM development are not fully understood. It has been shown that high alcohol consumption impairs the immune system and, in turn, increases cancer risk. On the other hand, light and moderate alcohol consumption are hypothesized to improve humoral response27. Also, low alcohol consumption improves insulin sensitivity which, in turn, decreases risk of diabetes and other obesity-related disorders28, 29 and thus, indirectly, may decrease risk of MM. The effect of low alcohol consumption on insulin sensitivity might explain the observed inverse association among overweight individuals. It is possible that overweight individuals have dysregulated hormone levels which induce insulin resistance, making them more sensitive to potential protective effects of moderate alcohol consumption.
Exposure to low doses of ethanol has been associated with inhibition of the mammalian target of rapamycin (mTOR) signaling pathway in in vitro and in vivo non-Hodgkin lymphoma models30. Polyphenols and resveratrol, abundant in red wines, inhibit nuclear factor-κB (NF-κB) in mononuclear cells and resveratrol induces apoptosis by inhibiting signal transducer and activator of transcription 3 (STAT3) in human MM cells31–33. Synergistic cytotoxicity has been shown when resveratrol was combined with bortezomib in vitro32, providing the rationale for clinical trials in humans. A phase 2 clinical trial using SRT501, a resveratrol oral formulation, has demonstrated a high toxicity profile and minimal efficacy34. Therefore, in vitro molecular studies are needed to better define possible mechanisms of action in MM models.
The strengths of this study include its large sample size, the prospective design, and the ability to control for potential confounding factors and conduct stratified analyses in population subgroups. A limitation of this investigation is that information on alcohol consumption was based on self-report for the 12-month period prior to study enrollment. To account for the possibility that some participants’ alcohol consumption habits may have changed as a result of preclinical disease or as yet undiagnosed MM at baseline, we conducted sensitivity analyses excluding the first 2 years of follow-up, but our results did not change.
In conclusion, the findings of this prospective study – one of the largest of its kind – support a protective effect of alcohol consumption in MM development. Prospective studies with information on long-term alcohol use may help to further elucidate how the timing and amount of alcohol consumption may influence MM risk. Mechanistic studies are also warranted to better understand the potential underlying biological mechanisms, which may help to identify novel molecular targets or therapeutic approaches.
Supplementary Material
What’s new.
While alcohol consumption is a risk factor for multiple cancer types, epidemiological studies examining specific associations with multiple myeloma have yielded inconsistent results. Here, the authors investigated relationships between alcohol consumption and multiple myeloma risk using data for nearly half-a-million adults enrolled in the National Institutes of Health-AARP Diet and Health Study. Analyses reveal a significant association between reduced multiple myeloma risk and moderate drinking, defined as 2 to 2.9 drinks per day. Further investigation is needed to better understand how alcohol consumption influences multiple myeloma risk.
Acknowledgment
This research was supported by the Intramural Research Program of the NIH, National Cancer Institute. Cancer incidence data from the Atlanta metropolitan area were collected by the Georgia Center for Cancer Statistics, Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia. Cancer incidence data from California were collected by the California Cancer Registry, California Department of Public Health’s Cancer Surveillance and Research Branch, Sacramento, California. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration, Lansing, Michigan. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System (Miami, Florida) under contract with the Florida Department of Health, Tallahassee, Florida. The views expressed herein are solely those of the authors and do not necessarily reflect those of the FCDC or FDOH. Cancer incidence data from Louisiana were collected by the Louisiana Tumor Registry, Louisiana State University Health Sciences Center School of Public Health, New Orleans, Louisiana. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, The Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry, Raleigh, North Carolina. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg, Pennsylvania. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions. Cancer incidence data from Arizona were collected by the Arizona Cancer Registry, Division of Public Health Services, Arizona Department of Health Services, Phoenix, Arizona. Cancer incidence data from Texas were collected by the Texas Cancer Registry, Cancer Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas. Cancer incidence data from Nevada were collected by the Nevada Central Cancer Registry, Division of Public and Behavioral Health, State of Nevada Department of Health and Human Services, Carson City, Nevada. We are indebted to the participants in the NIH-AARP Diet and Health Study for their outstanding cooperation. We also thank Sigurd Hermansen and Kerry Grace Morrissey from Westat for study outcomes ascertainment and management and Leslie Carroll at Information Management Services for data support and analysis.
Footnotes
Conflict of interest: The authors state no conflict of interest
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