Unrestrained eating behavior and risk of digestive system cancers: a prospective cohort study

Yin Zhang; Mingyang Song; Andrew T Chan; Eva S Schernhammer; Brian M Wolpin; Meir J Stampfer; Jeffrey A Meyerhardt; Charles S Fuchs; Susan B Roberts; Walter C Willett; Frank B Hu; Edward L Giovannucci; Kimmie Ng

doi:10.1093/ajcn/nqab235

. 2021 Jul 22;114(5):1612–1624. doi: 10.1093/ajcn/nqab235

Unrestrained eating behavior and risk of digestive system cancers: a prospective cohort study

Yin Zhang ^1,^2,^3,^✉, Mingyang Song ^4,^5,^6,⁷, Andrew T Chan ^8,^9,^10,¹¹, Eva S Schernhammer ^12,^13,¹⁴, Brian M Wolpin ¹⁵, Meir J Stampfer ^16,^17,¹⁸, Jeffrey A Meyerhardt ¹⁹, Charles S Fuchs ^20,²¹, Susan B Roberts ²², Walter C Willett ^23,²⁴, Frank B Hu ^25,^26,²⁷, Edward L Giovannucci ^28,^29,³⁰, Kimmie Ng ³¹

¹Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

²Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

³Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

⁴Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

⁵Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

⁶Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

⁷Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

⁸Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

⁹Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

¹⁰Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

¹¹Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA

¹²Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

¹³Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

¹⁴Department of Epidemiology, Center of Public Health, Medical University of Vienna, Vienna, Austria

¹⁵Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

¹⁶Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

¹⁷Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

¹⁸Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

¹⁹Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

²⁰Department of Medical Oncology, Smilow Cancer Hospital and Yale Cancer Center, New Haven, CT, USA

²¹Department of Medicine, Yale School of Medicine, New Haven, CT, USA

²²USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA

²³Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

²⁴Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

²⁵Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

²⁶Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

²⁷Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

²⁸Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

²⁹Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

³⁰Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

³¹Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

^✉

Address correspondence to YZ (emails: yin.zhang@channing.harvard.edu and drzhangyin@163.com).

PMCID: PMC8588850 PMID: 34293086

ABSTRACT

Background

Unrestrained eating behavior, as a potential proxy for diet frequency, timing, and caloric intake, has been questioned as a plausible risk factor for digestive system cancers, but epidemiological evidence remains sparse.

Objectives

We investigated prospectively the associations between unrestrained eating behavior and digestive system cancer risk.

Methods

Participants in the Nurses’ Health Study who were free of cancer and reported dietary information in 1994 were followed for ≤18 y. Cox models were used to estimate HRs and 95% CIs for unrestrained eating (eating anything at any time, no concern with figure change, or both) and risk of digestive system cancers.

Results

During follow-up, 2064 digestive system cancer cases were documented among 70,450 eligible participants in analyses of eating anything at any time, In total, 2081 digestive system cancer cases were documented among 72,468 eligible participants in analyses of no concern with figure change. In fully adjusted analyses, women with the behavior of eating anything at any time had a higher risk of overall digestive system cancer (HR: 1.22; 95% CI: 1.10, 1.35), overall gastrointestinal tract cancer ((HR: 1.33; 95% CI: 1.18, 1.50), buccal cavity and pharynx cancer (HR: 1.50; 95% CI: 1.02, 2.21), esophageal cancer (HR: 1.62; 95% CI: 1.01, 2.62), small intestine cancer (HR: 1.92; 95% CI: 1.02,3. 59), and colorectal cancer (HR: 1.20; 95% CI: 1.04, 1.38), and a non–statistically significant increased risk of stomach cancer (HR: 1.54; 95% CI: 0.96,2.48), compared with women without this behavior. No statistically significant association was observed for pancreatic cancer and liver and gallbladder cancer. The combined effect of eating anything at any time and having no concern with figure change was associated with a significantly increased risk of overall digestive system cancer (HR: 1.27; 95% CI: 1.10, 1.46), overall gastrointestinal tract cancer (HR: 1.45; 95% CI: 1.23, 1.71), and colorectal cancer (HR: 1.34; 95% CI: 1.11, 1.63), compared with women exhibiting the opposite.

Conclusions

Unrestrained eating behavior was independently associated with increased risk of gastrointestinal tract cancers. The potential importance of unrestrained eating behavior modification in preventing gastrointestinal tract cancers should be noted.

Keywords: Unrestrained eating behavior, digestive system cancer, gastrointestinal tract cancer, colorectal cancer, buccal cavity and pharynx cancer, esophageal cancer, stomach cancer, small intestine cancer, pancreatic cancer, cohort study

Introduction

The global disease burden of digestive system cancer is substantial (1, 2). Major digestive system cancers accounted for 29.6% of newly diagnosed cancers and 39.0% of all cancer deaths worldwide (1).

Unrestrained eating behaviors have previously been thought to be proxies for diet frequency, caloric intake (with restriction or reckless abandon), and timing (at appropriate or inappropriate times, and subsequent potential alterations in circadian rhythms or diurnal preference). In addition to diet quality (3–5), unrestrained eating behaviors have been hypothesized to play an independent role in the carcinogenesis of digestive system cancers (6, 7) through insulin resistance, increased chance of injury to the mucus-lined barrier of the gastrointestinal tract, chronic inflammation, rhythmic disturbances, and potential synergistic effects with unhealthier dietary patterns and lifestyle factors among unrestrained eaters. However, epidemiological evidence comprehensively characterizing the effect of unrestrained eating or dietary restraint on risk of overall and individual digestive system cancers (e.g., cancers of buccal cavity, pharynx, esophagus, stomach, pancreas, colorectum, and liver and gallbladder) have not yet been reported.

The Nurses’ Health Study (NHS) (8–11) with detailed assessments of disease outcomes and validated data on a wide range of potential confounders (12–17) affords a rich resource to add to this topic. We prospectively examined the association between unrestrained eating behavior and risk of digestive system cancers within the NHS.

Subjects and methods

Study population

The details of the NHS have been previously described (8–11). In brief, the NHS is an ongoing large prospective cohort study that enrolled 121,700 US female nurses ages 30– 55 y starting in 1976. Demographics were collected at enrollment. Biennial questionnaires evaluating anthropometric data, lifestyles, medical history, family history, and menstrual and reproductive history were sent to participants, with diet and physical activity updated quadrennially. Cumulative response rates exceeding 90% of potential person-time have been achieved. We used 1994, the year the exposures were assessed, as the baseline timepoint. Participants who were alive, free of cancer, and had no missing information on the exposures of interest in 1994 were eligible for inclusion (Supplementary Figures 1 and 2). The study protocol was approved by the Institutional Review Board of the Brigham and Women's Hospital, and those of participating registries as required. Informed consent from participants was implied by the completion and return of the questionnaires. Written informed consent was required to retrieve medical records.

Ascertainment of exposures, outcomes, and deaths

Information on unrestrained eating behavior was assessed once in the 1994 questionnaire in the NHS. Specifically, participants answered a pair of questions on whether “I eat anything I want, anytime I want." and “I pay a great deal of attention to changes in my figure” applied to them, with “yes” or “no” as response categories. These questions, using stepwise regression, had been identified to be the strongest predictors of the full dietary restraint score in a prior study among participants (selected to include persons who exhibited the spectrum from extreme lack of restraint to extreme dietary restraint) who completed a questionnaire measuring 3 dimensions of human eating behavior (7, 18). The question “I pay a great deal of attention to changes in my figure” is part of the “flexible control” subscale proposed for dietary restraint, which has been proved to have greater benefits for counterbalancing dietary disinhibition than other aspects of dietary restraint [flexible, but not rigid, control of dietary restraint attenuated the influence of habitual disinhibition on weight gain and BMI (in kg/m²)] (19). In our analyses, the first unrestrained eating exposure included participants who reported “having the behavior of eating anything at any time.” The second exposure included individuals who reported “not paying a great deal of attention to changes in their figure.” Our main analyses were then repeated using a third exposure that required both to measure their combined effect.

The outcomes included incident cases of overall digestive system cancer, overall gastrointestinal tract cancer, and cancers of the buccal cavity and pharynx, esophagus, stomach, pancreas, small intestine, colorectum, and liver and gallbladder. Physician-diagnosed incident cancers were reported biennially via questionnaires and confirmed through medical records and pathology reports or linkage to state cancer registries.

Deaths were confirmed through next-of-kin or postal authority reporting and the National Death Index, with an identifying rate of more than 98% (20, 21).

Ascertainment of covariates and effect modifiers

Covariates were selected a priori as potential confounders, including age, follow-up cycle, race, cumulative average BMI, BMI at age 18 y, smoking status, pack-years of smoking, alcohol intake, physical activity, Empirical Dietary Index for Hyperinsulinemia (EDIH), regular use of aspirin, regular use of non-aspirin nonsteroidal anti-inflammatory drugs (NSAIDs), multivitamin use, history of diabetes mellitus, menopausal status, postmenopausal hormone use, family history of colorectal cancer, screening colonoscopy or sigmoidoscopy, total calorie intake, red or processed meat intake, and intake of fiber, folate, calcium, and vitamin D. We considered different sets of the above-mentioned variables in multivariable analyses of various outcomes. In sensitivity analyses, we additionally considered the Alternate Healthy Eating Index (AHEI), Healthy Eating Index (HEI), history of depression, history of rotating night shiftwork, socioeconomic status, and waist circumference. The ascertainment of covariates was elaborated in the Supplementary Methods.

We considered age, BMI, smoking status, alcohol intake, physical activity, regular use of aspirin, EDIH, and Empirical Lifestyle Index for Hyperinsulinemia (ELIH) (3, 22) as potential effect modifiers. We were particularly interested in factors that relate to hyperinsulinemia because eating frequency may influence insulin exposure; and hyperinsulinemia (higher EDIH and ELIH) has been associated with increased risk of digestive system cancers (3).

Statistical analysis

Person-years of follow-up accrued from the return date of the baseline questionnaire (1994) until the date of death recorded, any cancer diagnosis reported, loss to follow-up, or follow-up completion (defined as 30 June, 2012), whichever was earliest.

We used Cox proportional hazards regression models to estimate HRs and 95% CIs of the associations of unrestrained eating with risk of digestive system cancers, with follow-up time since 1994 (continuous, in months) as the time scale. The proportionality assumption was verified using interactions between the exposures of interest and the (log-)time scale. We controlled for age and follow-up cycle in age-adjusted models. In multivariable analyses, we additionally adjusted a priori for potential confounding variables, the details of which have been described in table footnotes and Supplementary Methods.

In secondary analyses, we conducted 4- and 8-y latency analyses for the aggregated and site-specific endpoints to minimize the potential influence of reverse causation on our results. To examine the primary associations of interest at different levels of major potential effect modifiers, we performed stratified analyses. Tests for interaction were conducted by adding interaction terms to the models and using likelihood ratio tests to determine statistical significance. Joint analyses were further conducted to examine the potential interactions between unrestrained eating and BMI or physical activity in relation to the outcomes by using the collapsed categories of unrestrained eating and BMI or physical activity.

In sensitivity analyses, we estimated the predicted probability of reporting unrestrained eating using logistic regression models that included all the covariates (grouped into 3 categories: demographics, lifestyle, and diet) and then adjusted for the propensity score to further address the possibility of residual confounding.

Analyses were conducted using SAS software (version 9.4; SAS Institute), with 2-sided P values < 0.05 indicating statistical significance for all tests.

Results

Population characteristics

During ≤18 y of follow-up, 2064 digestive system cancer cases were documented among 70,450 eligible study participants in analyses of eating anything at any time, 2081 digestive system cancer cases were documented among 72,468 eligible study participants in analyses of having no concern with their figure change. When examining the combined effects, 44,382 eligible participants were included, with 1268 digestive system cancer cases documented. The sample size differs across these analyses due to the different missingness in exposures. Specifically in analyses of the combined effect, individuals who “eat anything at any time but cared about their figure change,” and those who “did not eat anything at any time but did not care about their figure change” were excluded in order to have a clear contrast. Variations across the exposures of interest were observed for a wide spectrum of characteristics (Table 1).

TABLE 1.

Characteristics of the study population in the NHS (1994) across self-reported unrestrained eating¹

	Eating anything at any time		No concern with figure change		Eating anything at any time + no concern with figure change
Characteristic	No (n = 54,320)	Yes (n = 16,130)	No (n = 46,519)	Yes (n = 25,949)	No² (n = 36,036)	Yes (n = 8346)
Age, y	60.7 ± 7.1	60.3 ± 7.2	60.6 ± 7.1	60.5 ± 7.2	60.7 ± 7.1	60.1 ± 7.3
Race
White	97.7	96.9	97.3	98.0	97.4	97.2
Black	1.4	1.5	1.7	1.0	1.7	1.2
Other	0.8	1.6	1.0	1.1	0.9	1.6
BMI³	26.3 ± 4.9	27.1 ± 6.1	25.4 ± 4.3	28.1 ± 5.9	25.4 ± 4.24	28.0 ± 6.36
BMI at age 18 y	21.4 ± 2.9	21.3 ± 3.1	21.1 ± 2.8	21.7 ± 3.2	21.2 ± 2.79	21.5 ± 3.11
Waist circumference, cm	87.1 ± 11.8	88.5 ± 13.2	85.5 ± 11.3	90.1 ± 12.7	85.6 ± 11.2	90.1 ± 13.4
Smoking status
Never smoked	45.2	41.0	44.4	44.1	45.0	40.8
Past smoker	43.9	36.8	44.3	40.3	44.7	36.6
Current smoker, <25 cigs/d	9.2	17.4	9.7	12.4	8.9	17.4
Current smoker ≥25 cigs/d	1.8	4.8	1.7	3.3	1.5	5.2
Pack-y of smoking⁴	23.2 ± 20.0	29.8 ± 23.1	22.6 ± 19.7	27.0 ± 22.1	22.2 ± 19.4	30.5 ± 23.59
Alcohol intake, g/d	4.9 ± 8.8	5.1 ± 10.1	5.1 ± 8.8	4.8 ± 9.5	5.1 ± 8.8	5.1 ± 10.35
Physical activity,⁵ MET-h/wk	21.2 ± 24.6	17.8 ± 23.5	23.0 ± 26.02	17.0 ± 21.7	23.1 ± 25.9	16.5 ± 22.39
AHEI	49.49 ± 9.7	45.17 ± 9.7	50.0 ± 9.7	46.8 ± 9.7	50.3 ± 9.61	44.4 ± 9.44
EDIH, ≥median	50.5	65.1	47.0	62.5	46.1	68.8
ELIH, ≥median	48.4	56.6	39.8	64.5	40.0	63.3
Regular use of aspirin, >2 tablets/wk	29.6	28.4	29.2	29.7	29.3	28.8
Regular use of nonaspirin NSAIDs, >2 tablets/wk	25.5	24.6	25.0	25.9	25.0	24.9
Multivitamin use	44.1	38.5	45.5	39.5	45.6	36.5
Menopausal status
Premenopausal	9.8	10.6	9.7	10.7	9.6	10.9
Postmenopausal	90.0	89.2	90.2	89.1	90.2	88.9
Postmenopausal hormone use
Never	26.6	30.6	24.8	30.9	24.8	32.4
Past	18.0	18.2	18.0	18.0	17.9	18.1
Current	40.4	34.5	42.7	34.9	42.8	32.6
Family history of colorectal cancer	13.7	13.9	13.6	13.9	13.6	13.8
History of diabetes mellitus	5.0	2.6	3.4	6.2	3.6	3.1
Screening colonoscopy or sigmoidoscopy	17.3	14.2	17.5	15.3	17.8	13.6
Red or processed meat intake, servings/wk	2.8 ± 1.7	3.3 ± 1.9	2.7 ± 1.7	3.1 ± 1.8	2.6 ± 1.6	3.4 ± 2.0
Total calories intake, kcal/d	1711 ± 503	1824 ± 557	1699 ± 505	1783 ± 529	1691 ± 499	1848 ± 561
Total fiber intake, g/d	19.6 ± 6.0	16.7 ± 5.2	19.9 ± 6.2	17.9 ± 5.4	20.1 ± 6.14	16.4 ± 5.0
Total folate intake, ug/d	469 ± 235	400 ± 212	477 ± 239	426 ± 220	482 ± 240	390 ± 206
Total calcium intake, mg/d	1109 ± 548	939 ± 492	1132 ± 562	998 ± 503	1145 ± 564	920 ± 482
Total vitamin D intake, IU/d	410 ± 269	342 ± 240	419 ± 275	366 ± 248	423 ± 276	332 ± 233
History of depression	6.1	7.6	6.2	6.7	6.0	7.6
History of rotating night shiftwork	52.0	52.9	51.7	52.8	51.9	54.1
Husbands’ educational level
>High school	1.5	2.2	1.4	1.9	1.4	2.3
Some high school	3.2	3.9	3.0	3.8	3.0	4.4
High school graduate	31.4	32.9	30.7	32.9	30.9	33.9
College	23.9	21.4	23.8	22.8	24.1	21.4
Graduate school	20.0	16.9	20.2	18.4	20.3	16.0
Meal frequency⁶
1–2 times/d	3.8	5.9	4.3	4.0	4.1	5.5
3–4 times/d	65.8	62.0	65.5	64.4	65.9	62.2
≥5 times/d	30.4	32.2	30.1	31.6	30.0	32.3
Breakfast consumption⁶
0–2 times/wk	7.3	12.8	8.0	8.8	7.6	12.8
3–5 times/wk	9.2	12.8	9.4	10.7	9.1	13.4
6–7 times/wk	83.5	74.4	82.5	80.5	83.3	73.8

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Data are expressed as means ± SDs or percentages unless otherwise indicated. Participants who were diagnosed with any prior cancer at baseline, or participants who reported no information on their behavior of eating anything at any time were excluded. Percentages are of nonmissing values and may not sum to 100% after rounding. AHEI, alternate healthy eating index; cigs, cigarettes; EDIH, empirical dietary index for hyperinsulinemia; ELIH, empirical lifestyle index for hyperinsulinemia; IU, international unit; MET, metabolic equivalent task; NHS, Nurses’ Health Study; NSAIDs, nonsteroidal anti-inflammatory drugs

Defined as those exhibiting dietary restraint, i.e., those who reported both “not having the behavior of eating anything at any time” and “paying a great deal of attention to changes in their figure.”

Calculated as weight in kilograms divided by height in meters squared.

⁴

Cumulative among smokers.

⁵

Weekly energy expenditure in MET-h/wk from recreational and leisure time physical activity.

⁶

Assessed in 2002.

Unrestrained eating behavior and digestive system cancer risk (primary analyses)

In fully adjusted analyses, participants who reported having the behavior of eating anything at any time experienced an increased risk of overall digestive system cancer (HR: 1.22; 95% CI: 1.10, 1.35) compared with those not reporting this behavior, as well as overall gastrointestinal tract cancer (HR: 1.33; 95% CI: 1.18, 1.50), buccal cavity and pharynx cancer (HR: 1.50; 95% CI: 1.02, 2.21), esophageal cancer (HR: 1.62; 95% CI: 1.01, 2.62), small intestine cancer (HR: 1.92; 95% CI: 1.02, 3.59), colorectal cancer (HR: 1.20; 95% CI: 1.04, 1.38), and suggestively for stomach cancer (HR: 1.54; 95% CI: 0.96, 2.48). There was no statistically significant association with risk of pancreatic cancer or cancer of the liver and gallbladder. We observed no statistically significant association between no concern with their figure change and all of the aggregated and site-specific endpoints, though a non–statistically significant increased risk of overall digestive system cancer (HR: 1.09; 95% CI: 0.99, 1.19) and overall gastrointestinal tract cancer (HR: 1.08; 95% CI: 0.96, 1.20) was suggested (Table 2).

TABLE 2.

Self-reported unrestrained eating and digestive system cancer risk¹

	Eating anything at any time		No concern with figure change
	No	Yes	No	Yes
All digestive system cancers
Cases	1507	557	1273	808
Age-adjusted²	1 [Reference]	1.32 (1.20, 1.46)	1	1.18 (1.08, 1.29)
Multivariable (without BMI)^3,4	1	1.22 (1.10, 1.35)	1	1.11 (1.02, 1.22)
Multivariable (without EDIH)^3,4	1	1.24 (1.12, 1.37)	1	1.10 (1.00, 1.21)
Multivariable³	1	1.22 (1.10, 1.35)	1	1.09 (0.99, 1.19)
Gastrointestinal tract cancers
Cases	1018	409	897	558
Age-adjusted²	1	1.44 (1.28, 1.61)	1	1.15 (1.04, 1.28)
Multivariable (without BMI)^3,4	1	1.34 (1.19, 1.51)	1	1.10 (0.98, 1.22)
Multivariable (without EDIH)^3,4	1	1.35 (1.20, 1.52)	1	1.09 (0.97, 1.21)
Multivariable³	1	1.33 (1.18, 1.50)	1	1.08 (0.96, 1.20)
Buccal cavity and pharynx cancer
Cases	85	44	83	48
Age-adjusted²	1	1.82 (1.26, 2.62)	1	1.07 (0.75, 1.53)
Multivariable (without BMI)^3,4	1	1.54 (1.05, 2.26)	1	0.96 (0.66, 1.39)
Multivariable (without EDIH)^3,4	1	1.48 (1.01, 2.17)	1	0.97 (0.67, 1.40)
Multivariable³	1	1.50 (1.02, 2.21)	1	0.96 (0.66, 1.39)
Esophageal cancer
Cases	51	29	42	35
Age-adjusted²	1	2.05 (1.30, 3.24)	1	1.55 (0.99, 2.43)
Multivariable (without BMI)^3,4	1	1.61 (1.00, 2.60)	1	1.35 (0.85, 2.14)
Multivariable (without EDIH)^3,4	1	1.67 (1.04, 2.68)	1	1.35 (0.84, 2.16)
Multivariable³	1	1.62 (1.01, 2.62)	1	1.32 (0.82, 2.11)
Squamous cell esophageal carcinoma
Cases	13	10	13	9
Age-adjusted²	1	2.72 (1.19, 6.21)	1	1.28 (0.55, 2.99)
Multivariable (without BMI)^3,4	1	1.72 (0.70, 4.21)	1	0.92 (0.38, 2.26)
Multivariable (without EDIH)^3,4	1	1.87 (0.77, 4.55)	1	1.14 (0.47, 2.79)
Multivariable³	1	1.78 (0.73, 4.34)	1	1.06 (0.43, 2.64)
Esophageal adenocarcinoma
Cases	29	16	23	20
Age-adjusted²	1	1.99 (1.08, 3.68)	1	1.61 (0.88, 2.94)
Multivariable (without BMI)^3,4	1	1.74 (0.92, 3.27)	1	1.45 (0.78, 2.69)
Multivariable (without EDIH)^3,4	1	1.67 (0.89, 3.13)	1	1.28 (0.68, 2.40)
Multivariable³	1	1.70 (0.90, 3.21)	1	1.28 (0.68, 2.41)
Stomach cancer
Cases	59	27	51	27
Age-adjusted²	1	1.66 (1.05, 2.62)	1	1.02 (0.64, 1.63)
Multivariable (without BMI)^3,4	1	1.55 (0.96, 2.49)	1	0.89 (0.55, 1.45)
Multivariable (without EDIH)^3,4	1	1.60 (1.00, 2.57)	1	0.88 (0.54, 1.43)
Multivariable³	1	1.54 (0.96, 2.48)	1	0.83 (0.51, 1.35)
Pancreatic cancer
Cases	282	89	226	144
Age-adjusted²	1	1.16 (0.91, 1.47)	1	1.20 (0.97, 1.48)
Multivariable (without BMI)^4,5	1	1.05 (0.82, 1.35)	1	1.07 (0.86, 1.33)
Multivariable (without EDIH)^4,5	1	1.07 (0.84, 1.37)	1	1.07 (0.86, 1.34)
Multivariable⁵	1	1.04 (0.82, 1.34)	1	1.05 (0.84, 1.31)
Liver and gallbladder cancer
Cases	119	41	83	66
Age-adjusted²	1	1.20 (0.84, 1.72)	1	1.51 (1.09, 2.09)
Multivariable (without BMI)^4,5	1	1.13 (0.78, 1.63)	1	1.31 (0.94, 1.84)
Multivariable (without EDIH)^4,5	1	1.16 (0.81, 1.67)	1	1.29 (0.92, 1.81)
Multivariable⁵	1	1.12 (0.77, 1.61)	1	1.23 (0.87, 1.73)
Small intestine cancer	29	17	30	16
Age-adjusted²	1	2.06 (1.13, 3.77)	1	0.96 (0.52, 1.77)
Multivariable (without BMI)^3,4	1	1.88 (1.01, 3.51)	1	0.86 (0.46, 1.61)
Multivariable (without EDIH)^3,4	1	1.97 (1.06, 3.65)	1	0.89 (0.47, 1.68)
Multivariable³	1	1.92 (1.02, 3.59)	1	0.85 (0.45, 1.63)
Colorectal cancer
Cases	799	297	696	436
Age-adjusted²	1	1.33 (1.17, 1.52)	1	1.16 (1.03, 1.31)
Multivariable (without BMI)^4,6	1	1.19 (1.04, 1.37)	1	1.08 (0.95, 1.22)
Multivariable (without EDIH)^4,6	1	1.20 (1.04, 1.38)	1	1.06 (0.94, 1.21)
Multivariable⁶	1	1.20 (1.04, 1.38)	1	1.06 (0.94, 1.21)

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Data are expressed as HRs and 95% CIs or numbers of cases unless otherwise indicated. EDIH, the empirical dietary index for hyperinsulinemia; NHS, Nurses’ Health Study; NSAIDs, nonsteroidal anti-inflammatory drugs.

HRs and 95% CIs were generated by Cox proportional hazards analyses adjusted for age and follow-up cycle.

⁴

Models were adjusted for the same sets of covariates as denoted, except for cumulative average BMI or EDIH.

⁵

⁶

Models were adjusted for age, follow-up cycle, race, cumulative average BMI, BMI at age 18, smoking status, pack-years of smoking, alcohol intake, physical activity, EDIH, regular use of aspirin, regular use of non-aspirin NSAIDs, multivitamin use, history of diabetes mellitus, menopausal status, postmenopausal hormone use, family history of colorectal cancer, screening colonoscopy or sigmoidoscopy, total calories intake, red or processed meat intake, and intake of fiber, folate, calcium, and vitamin D.

Their combined effect was associated with a significantly higher risk of overall digestive system cancer (HR: 1.27; 95% CI: 1.10, 1.46), overall gastrointestinal tract cancer (HR: 1.45; 95% CI: 1.23, 1.71), and colorectal cancer (HR: 1.34; 95% CI: 1.11, 1.63), compared to those exhibiting dietary restraint (i.e., those who reported both “not having the behavior of eating anything at any time” and “paying a great deal of attention to changes in their figure). We observed no statistically significant association for pancreatic cancer or cancer of the liver and gallbladder (Table 3).

TABLE 3.

Self-reported unrestrained eating (combined effect) and digestive system cancer risk¹

	Eating anything at any time + no concern with figure change
	No²	Yes
All digestive system cancers
Cases	975	293
Age-adjusted³	1 [Reference]	1.41 (1.24, 1.61)
Multivariable (without BMI)⁴	1	1.29 (1.13, 1.48)
Multivariable (without EDIH)⁴	1	1.28 (1.12, 1.47)
Multivariable⁵	1	1.27 (1.10, 1.46)
Gastrointestinal tract cancers
Cases	675	222
Age-adjusted³	1	1.55 (1.33, 1.81)
Multivariable (without BMI)⁴	1	1.46 (1.25, 1.72)
Multivariable (without EDIH)⁴	1	1.45 (1.24, 1.71)
Multivariable⁵	1	1.45 (1.23, 1.71)
Pancreatic cancer
Cases	175	43
Age-adjusted³	1	1.18 (0.84, 1.65)
Multivariable (without BMI)⁴	1	1.00 (0.71, 1.43)
Multivariable (without EDIH)⁴	1	1.01 (0.71, 1.43)
Multivariable⁵	1	0.97 (0.68, 1.39)
Liver and gallbladder cancer
Cases	69	20
Age-adjusted³	1	1.37 (0.83, 2.26)
Multivariable (without BMI)⁴	1	1.15 (0.68, 1.95)
Multivariable (without EDIH)⁴	1	1.18 (0.70, 1.99)
Multivariable⁵	1	1.08 (0.63, 1.84)
Colorectal cancer
Cases	526	166
Age-adjusted³	1	1.50 (1.25, 1.78)
Multivariable (without BMI)⁴	1	1.36 (1.12, 1.64)
Multivariable (without EDIH)⁴	1	1.33 (1.10, 1.61)
Multivariable⁵	1	1.34 (1.11, 1.63)

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Data are expressed as HRs and 95% CIs or numbers of cases unless otherwise indicated. EDIH, the empirical dietary index for hyperinsulinemia; NHS, Nurses’ Health Study.

HRs and 95% CIs were generated by Cox proportional hazards analyses adjusted for age and follow-up cycle.

⁴

Models were adjusted for the same sets of covariates as denoted in Table 2, except for cumulative average BMI or EDIH.

⁵

Models were adjusted for the same sets of covariates as denoted in Table 2.

Unrestrained eating behavior and digestive system cancer risk (sensitivity analyses)

Additionally adjusting for history of depression, history of rotating night shiftwork, socioeconomic status, waist circumference, meal frequency and breakfast consumption, or replacing EDIH with AHEI (23) or HEI (24, 25), or additionally adjusting for propensity score did not materially change our results for all the overall and site-specific endpoints in all analyses.

We performed latency analyses by excluding the first 4 and 8 y of follow-up to assess the possibility of changes in eating behavior due to the presence of subclinical cancer; however, this did not materially attenuate the observed associations for most of the overall and site-specific endpoints, except for a suggestion of decreasing risk of buccal cavity and pharynx cancer, and small intestine cancer in analyses of eating anything at any time.

Detailed results of these sensitivity analyses are presented in Supplementary Tables 1–9.

Unrestrained eating behavior and digestive system cancer risk (stratified analyses)

When stratifying by different levels of major potential confounding factors in fully adjusted analyses, statistically significant or suggestive variations by physical activity, EDIH, and ELIH were observed for overall gastrointestinal tract cancer and colorectal cancer. Women with unrestrained eating behavior and who were physically inactive or had more hyperinsulinemic diets/lifestyles had increased risk of overall gastrointestinal tract cancer and colorectal cancer compared with those who did the opposite (Supplementary Tables 10–13).

Joint associations of unrestrained eating behavior and BMI with digestive system cancer risk

In fully adjusted analyses of joint associations, unrestrained eating and BMI had independent associations with risk of overall digestive system cancer, overall gastrointestinal tract cancer, and colorectal cancer, with no significant interaction of their effects detected. Participants who had this behavior and higher BMI experienced the highest risk of developing the above-mentioned endpoints, followed by each factor singly (Table 4).

TABLE 4.

Joint associations of self-reported unrestrained eating and BMI with digestive system cancer risk¹

	No and low BMI²	No and high BMI²	Yes and low BMI²	Yes and high BMI²	P-interaction³
Joint associations according to eating anything at any time and BMI
All digestive system cancers
Cases	694	731	221	296
Multivariable⁴	1 [Reference]	1.16 (1.04, 1.30)	1.25 (1.07, 1.46)	1.46 (1.26, 1.69)	0.96
Gastrointestinal tract cancers
Cases	477	483	167	211
Multivariable⁴	1	1.15 (1.00, 1.31)	1.39 (1.16, 1.67)	1.55 (1.30, 1.85)	0.82
Colorectal cancer
Cases	381	371	110	167
Multivariable⁴	1	1.12 (0.96, 1.30)	1.19 (0.95, 1.47)	1.56 (1.28, 1.90)	0.25
Joint associations according to no concern with figure change and BMI
All digestive system cancers
Cases	682	512	282	480
Multivariable⁴	1	1.16 (1.03, 1.31)	1.13 (0.98, 1.30)	1.21 (1.07, 1.38)	0.41
Gastrointestinal tract cancers
Cases	489	350	200	324
Multivariable⁴	1	1.15 (0.99, 1.32)	1.12 (0.95, 1.33)	1.18 (1.01, 1.37)	0.45
Colorectal cancer
Cases	378	269	157	255
Multivariable⁴	1	1.15 (0.98, 1.36)	1.16 (0.96, 1.40)	1.23 (1.03, 1.46)	0.54
Joint associations according to eating anything at any time + no concern with figure change and BMI
All digestive system cancers
Cases	511	407	106	167
Multivariable⁴	1	1.23 (1.07, 1.42)	1.38 (1.12, 1.72)	1.50 (1.24, 1.81)	0.36
Gastrointestinal tract cancers
Cases	358	277	83	124
Multivariable⁴	1	1.22 (1.03, 1.44)	1.62 (1.26, 2.07)	1.64 (1.31, 2.04)	0.27
Colorectal cancer
Cases	280	212	57	99
Multivariable⁴	1	1.22 (1.00, 1.47)	1.51 (1.13, 2.03)	1.76 (1.37, 2.26)	0.81

Open in a new tab

Data are expressed as HRs and 95% CIs or numbers of cases unless otherwise indicated. NHS, Nurses’ Health Study.

Self-reported behavior of unrestrained eating was dichotomized into “no” category compared with “yes” category, cumulative average BMI was dichotomized into a “low” category (20 to <25 kg/m²) compared with a “high” category (≥25 kg/m²), resulting in 4categories (no and low, no and high, yes and low, and yes and high, i.e., not having this behavior and with low BMI, not having this behavior and with high BMI, having this behavior and with low BMI, and having this behavior and with high BMI).

P value for interaction was calculated using the multiplication of status of having this behavior (0 for “no” and 1 for “yes”) and cumulative average BMI (0 for “low” and 1 for “high”).

⁴

HRs and 95% CIs were generated by Cox proportional hazards analyses adjusted for the same sets of covariates as denoted in Table 2.

Joint associations of unrestrained eating behavior and physical activity with digestive system cancer risk

Similar findings were observed when exploring joint associations of unrestrained eating and physical activity with risk of overall digestive system cancer, overall gastrointestinal tract cancer, and colorectal cancer. Participants who reported having this behavior and being physically inactive had the highest risk of developing all these endpoints, followed by each factor singly. Physical activity and this behavior had independent effects, without significant interactions (Table 5).

TABLE 5.

Joint associations of self-reported unrestrained eating and physical activity with digestive system cancer risk¹

	No and active²	No and Inactive²	Yes and Active²	Yes and Inactive²	P-interaction³
Joint associations according to eating anything at any time and physical activity
All digestive system cancers
Cases	529	978	131	426
Multivariable⁴	1 [Reference]	1.12 (1.00, 1.24)	1.09 (0.90, 1.32)	1.41 (1.23, 1.61)	0.20
Gastrointestinal tract cancers
Cases	361	657	98	311
Multivariable⁴	1	1.11 (0.97, 1.27)	1.20 (0.95, 1.50)	1.54 (1.31, 1.80)	0.27
Colorectal cancer
Cases	283	516	73	224
Multivariable⁴	1	1.11 (0.96, 1.29)	1.17 (0.90, 1.52)	1.44 (1.20, 1.74)	0.51
Joint associations according to no concern with figure change and physical activity
All digestive system cancers
Cases	511	762	195	613
Multivariable⁴	1	1.06 (0.95, 1.19)	0.97 (0.82, 1.15)	1.20 (1.06, 1.36)	0.14
Gastrointestinal tract cancers
Cases	362	535	134	424
Multivariable⁴	1	1.06 (0.93, 1.22)	0.95 (0.78, 1.16)	1.19 (1.03, 1.39)	0.17
Colorectal cancer
Cases	280	416	107	329
Multivariable⁴	1	1.08 (0.92, 1.26)	1.00 (0.80, 1.25)	1.23 (1.04, 1.46)	0.32
Joint associations according to eating anything at any time + no concern with figure change and physical activity
All digestive system cancers
Cases	391	584	61	232
Multivariable⁴	1	1.09 (0.95, 1.24)	1.09 (0.83, 1.44)	1.44 (1.20, 1.71)	0.23
Gastrointestinal tract cancers
Cases	277	398	50	172
Multivariable⁴	1	1.05 (0.90, 1.23)	1.30 (0.96, 1.77)	1.57 (1.28, 1.93)	0.44
Colorectal cancer
Cases	218	308	41	125
Multivariable⁴	1	1.04 (0.87, 1.24)	1.43 (1.01, 2.01)	1.51 (1.19, 1.91)	0.95

Open in a new tab

Data are expressed as HRs and 95% CIs or numbers of cases unless otherwise indicated. MET, metabolic equivalent task; NHS, Nurses’ Health Study.

Self-reported behavior of unrestrained eating was dichotomized into a “no” category compared with a “yes” category, physical activity was dichotomized into an “active” category (≥18 MET-h/wk) compared with an “inactive” category (<18 MET-h/wk), resulting in 4 categories (no and active, no and inactive, yes and active, and yes and inactive, i.e., not having this behavior and being physically active, not having this behavior and being physically inactive, having this behavior and being physically active, and having this behavior and being physically inactive).

P value for interaction was calculated using the multiplication of status of having this behavior (0 for “no” and 1 for “yes”) and physical activity (0 for “active” and 1 for “inactive”).

⁴

HRs and 95% CIs were generated by Cox proportional hazards analyses adjusted for the same sets of covariates as denoted in Table 2.

Discussion

In this large prospective cohort study, unrestrained eaters had increased risk of cancers of the gastrointestinal tract (buccal cavity and pharynx, esophagus, stomach, small intestine, and colorectum), instead of accessory organs. To our knowledge, this study is the first to comprehensively examine the association of unrestrained eating on risk of overall and major individual digestive system cancers.

Colorectal cancer is among the most frequently investigated cancers in relation to disordered eating behaviors (6). In 2012, an analysis from the NHS showed that eating anything at any time was associated with increased colorectal cancer risk (7). Our present findings, based on expanded scope and a larger number of cases, largely confirm these results (7). A meta-analysis summarizing 12 case–control studies and 3 cohort studies reported a significant association between eating frequency and risk of colorectal cancer among case–control studies, but not among cohort studies (6). Interestingly, a positive association specifically between snacking frequency and colorectal cancer risk has been demonstrated in several studies that observed null findings for frequency of meals (26, 27), but results for a null association also exist (28, 29).

Evidence for disordered eating behavior and incidence of overall and other site-specific digestive system cancers remains very sparse, though surrogate exposures have been investigated for some of these cancers in very few case–control studies (30–33). Our findings largely corroborate the prior evidence linking overeating (30–33) and irregular meals (31, 33) with increased risk of overall digestive system cancer (30, 31) and gastric cardia cancer (33), but conflict with the previously reported lack of association between irregular dinners and cancer of esophagus (32).

Of note, the prior studies may have been limited by their study design [predominantly retrospective studies (26, 28, 30–43), and relatively small sample size (27, 28, 31, 33, 39, 43), imprecise assessment of eating behaviors (7, 27, 29, 44), diagnostic challenges (30), inadequate control for confounding (i.e., missing information on certain critical covariates (26–28, 30–43), and/or missing updated information on covariates in cohort studies (27)], and absence of consideration for reverse causation (26–29, 31–37, 39–43), offering potential explanations of the discrepancy observed in their findings. The significant heterogeneity detected in the above-mentioned meta-analysis (6) further supports these concerns. Our study addresses most of the limitations in previous investigations.

We observed no statistically significant association specifically between no concern with figure change and all the aggregated and site-specific endpoints. Possible explanations include the fact that paying attention to figure change may reflect flexible, but not rigid, control of dietary restraint, which has greater benefits for counterbalancing dietary disinhibition (19).

Dietary restraint is defined as the self-imposed practice of consciously attempting to restrict energy intake to prevent weight gain or to promote weight loss (45, 46). The benefits of dietary restraint have been suggested for a wide spectrum of health conditions (including cancers), the mechanisms of which involve metabolic switching and cellular stress resistance (47). A higher risk of cancers of the gastrointestinal tract among women who reported unrestrained eating is biologically plausible.

Major individual digestive cancers share several common mechanisms. For example, obesity is a risk factor for most of the major digestive system cancers, including esophageal adenocarcinoma, gastric cardia cancer, and cancers of the colorectum, pancreas, liver, and gallbladder (48–51). Multiple interrelated carcinogenic pathways have been suggested to be implicated in the putative mechanisms linking obesity with gastrointestinal and hepatobiliary carcinogenesis (48–54). Another critical common risk factor for major digestive system cancers is dietary quality (3). Unrestrained eating has been associated with unhealthier dietary patterns generally (55). However, our results remained fairly robust after controlling for BMI and various measures of diet, indicating that some of the associations with unrestrained eating may be independent of these factors.

On the other hand, digestive system cancers are etiologically heterogeneous (22, 23, 56, 57). Mechanisms underlying why the observed association between unrestrained eating seems to be particularly driven by cancers of the gastrointestinal tracts, instead of accessory organs, remain to be fully elucidated. In general, the gastrointestinal tract organs share relatively similar structure, and there are common mechanisms specifically for the whole gastrointestinal tract during eating and digestion (e.g., being active or quiescent synchronously, etc.), although there are still many differences (e.g., in microbiome distribution, etc.). Interestingly, in stratified analyses and joint association analyses of our study, we reported that among unrestrained eaters, those who were physically inactive or had higher EDIH/ELIH (which might give prolonged hyperinsulinemia) (58) had even higher risk of overall gastrointestinal tract cancer and colorectal cancer, compared with those who were physically active or had lower EDIH/ELIH. The observed interactions suggest that the global effect of unrestrained eating on developing gastrointestinal tract cancer may act through the insulin resistance pathway.

Moreover, individuals reporting unrestrained eating may be more likely to exhibit other altered lifestyle behaviors, some of which have also been linked to increased risk of certain specific gastrointestinal cancers. For example, unrestrained eaters may be more likely to also indulge in late-night eating and eating large meals, as well as consumption of certain specific foods and beverages (fatty or fried foods, alcohol, coffee, etc.), which may lead to gastroesophageal reflux disease and subsequent Barrett's esophagus, the most common precancerous condition of the esophagus (23). Similarly, tobacco and alcohol use, which correlate with unrestrained eating behavior, are established risk factors for cancers of the buccal cavity, pharynx, and esophagus (23, 56). Further, frequent and untimed eating behavior, in conjunction with the above-mentioned factors, may increase the chance of injury to the mucus-lined barrier of the gastrointestinal tract, resulting in chronic inflammation and elevated risk of malignant transformation, offering another explanation of the observed significant associations that were restricted specifically to the gastrointestinal tract rather than accessory organs.

Last, human (59, 60) and animal (61, 62) evidence demonstrates that food intake rhythms and light exposure are important components regulating the circadian clock. Circadian misalignment induced by mistimed food intake that is out of phase with the endogenous circadian clock, in conjunction or not with mistimed sleep, may synergistically cause substantial plasma proteome changes, especially for proteins associated with pathways involved in multiple glucose homeostasis regulation, energy metabolism, immune function, and cancer (59, 63, 64). In contrast, shifting food intake to the normal activity phase may contribute to circadian reinforcement and therefore halt metabolic and rhythmic disturbances (61) and cancer progression (62), underlining the potential benefit of dietary modifications targeting circadian clocks (59–64). Among work schedules, rotating nightshifts are most disruptive to the circadian clock (65–67) and have previously been associated with higher risk of colorectal cancer (68–70). On the other hand, depression has been associated with disordered eating behavior (71, 72) and has previously been investigated as possibly linked with cancer risk, though there is no solid supporting evidence (73–76). However, in sensitivity analyses, additionally adjusting for history of rotating nightshift work and depression in our cohort with a high prevalence of abnormal working hours did not alter our findings for all endpoints (chronotype information was not available).

Our study has several major strengths. First, ours is the largest prospective study to investigate this aspect of diet. Second, validated information on a wide spectrum of covariates enabled rigorous confounding control (12–14, 16, 17, 20, 21, 77). Third, the prospective cohort study design and high follow-up rates minimize recall bias and selection bias. Fourth, we were able to examine heterogeneity across major digestive system cancer types. Last, we performed latency analyses to explore and minimize the potential influence of reverse causation.

Limitations of this study deserve comment. First, our population comprised predominantly white female healthcare professionals, which may limit the generalizability of our findings. However, the homogeneity of our study participants minimized socioeconomic confounding, further ensuring data quality and internal validity. Second, despite comprehensive confounding control, residual or uncontrolled confounding remains possible. Especially, our exposure (not only simply a behavior, but also an attitude) may still be different from pure behavior in nature, given initial clues suggested in Table 1 (unrestrained eaters generally have fewer health-conscious behaviors). On the other hand, the possibility of bias in dietary reporting by restrained compared with unrestrained eaters (45) cannot be completely ruled out. However, the fact that extensive control for all available confounding only had mild to moderate influence on the results minimizes the potential of residual confounding. In addition, we conducted the propensity score analyses, and the results remained essentially unchanged. Third, assessment of exposure was not updated during follow-up, presenting an important limitation; however, the effect estimates observed for most of the endpoints were not attenuated in various latency analyses. Further, the fact that exposures were measured once at baseline could have attenuated the effect estimates toward the null (underestimate the magnitude of the associations we observed). Although a validation study of unrestrained eating assessments has not been performed, we have similar confidence in their reliability given that a wide range of diet and lifestyle measurements have been demonstrated to be highly valid in our cohort of healthcare professionals. Nevertheless, it is critical to consider more refined exposure assessments in future investigation.

In conclusion, this prospective investigation among a large sample size of US women suggests that unrestrained eating may be independently associated with increased risk of overall digestive system cancer, particularly for several specific subtypes (buccal cavity and pharynx cancer, esophageal cancer, stomach cancer, small intestine cancer, and colorectal cancer). The potential importance of unrestrained eating behavior modification in preventing gastrointestinal tract cancers should be noted.

Supplementary Material

nqab235_Supplemental_File

Click here for additional data file.^{(478.7KB, docx)}

Acknowledgments

We thank all participants and staff of the Nurses' Health Studies for their great contributions to this research. We are grateful for help from the cancer registries of the following states: AL, AZ, AR, CA, CO, CT, DE, FL, GA, IA, ID, IL, IN, KY, LA, MA, MD, ME, MI, NC, ND, NE, NH, NJ, NY, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. The authors also thank Eric B Rimm at the Harvard T. H. Chan School of Public Health and Harvard Medical School and Chen Yuan at Dana-Farber Cancer Institute for their insightful comments and suggestions of this paper.

The authors’ responsibilities were as follows—YZ, ELG, KN: concept and design; YZ: acquisition, analysis, or interpretation of data,; drafting of the manuscript, and statistical analysis; all authors: critical revision of the manuscript for important intellectual content; KN, ELG, CSF, BMW, ESS, ATC, MS: obtained funding; KN, ELG, FBH, SBR, WCW, CSF, JAM, MJS, BMW, ESS, ATC, MS: administrative, technical, or material support; . ELG, KN: supervision; YZ, ELG, KN: had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; and all authors: read and approved the final manuscript. Author disclosures: BMW declares research funding from Celgene and Eli Lilly and Company and consulting for BioLineRx, Celgene, G1 Therapeutics, and GRAIL, outside the submitted work. ATC declares research funding from Bayer and consulting for Bayer and Pfizer, outside the submitted work. JAM declares institutional research funding from Boston Biomedical and consulting for Ignyta, Taiho Pharmaceutical, and Cota, outside the submitted work. CSF declares consulting for Agios, Bain Capital, Bayer, Celgene, Dicerna Pharmaceuticals, Eli Lilly and Company, Entrinsic Health Solutions, Five Prime Therapeutics, Genentech, Gilead Sciences, KEW, Merck & Co., Merrimack Pharmaceuticals, Pfizer, Sanofi, Taiho Pharmaceutical, and Unum Therapeutics, outside the submitted work. He also serves as a Director for CytomX Therapeutics and owns unexercised stock options for CytomX Therapeutics and Entrinsic Health Solutions, outside the submitted work. KN declares institutional research funding from Revolution Medicines, Celgene, Genentech, Gilead Sciences, Pharmavite, Tarrex Biopharma, and Trovagene, and consulting/advisory board fees from Array Biopharma, Bayer, Eli Lilly, Genentech, Seattle Genetics, and Tarrex Biopharma, outside the submitted work. All other authors report no conflicts of interest.

Notes

The Nurses’ Health Study was supported by grants UM1 CA186107 and P01 CA87969 from the National Institutes of Health (NIH). MS was supported by the American Cancer Society Mentored Research Scholar Grant MRSG-17-220-01-NEC and the NIH grant R00 CA215314. ATC was supported by NIH grants R01 CA137178, R35 CA253185, K24 DK098311 and the Damon Runyon Cancer Research Foundation (Chan). ESS was supported by NIH grant R01 OH009803. BMW was supported by the Lustgarten Foundation and Dana-Farber Cancer Institute Hale Family Center for Pancreatic Cancer Research, NIH grant U01 CA210171, the Pancreatic Cancer Action Network, Stand Up to Cancer, Noble Effort Fund, Wexler Family Fund, and Promises for Purple. CSF was supported by NIH grant P50 CA127003. ELG was supported by a grant from the World Cancer Research Fund. KN was supported by NIH grant R01 CA205406, Department of Defense grant CA160344, Project P Fund, Broman Family Fund, Pussycat Foundation Helen Gurley Brown Presidential Initiative, and the Entertainment Industry Foundation's National Colorectal Cancer Research Alliance (NCCRA). This research was also supported by Stand Up To Cancer–Lustgarten Foundation Pancreatic Cancer Interception Translational Cancer Research grant SU2C-AACR-DT25-17. The funding sources played no role in the study design, data collection, data analysis, and interpretation of results, or the decisions made in preparation and submission of the article.

ELG and KN are co–senior authors.

Supplemental Figures 1 and 2 and Supplemental Tables 1–13 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/.

Abbreviations used: AHEI, Alternate Healthy Eating Index; EDIH, Empirical Dietary Index for Hyperinsulinemia; ELIH, Empirical Lifestyle Index for Hyperinsulinemia; HEI, Healthy Eating Index; MET, metabolic equivalent task; NHS, Nurses’ Health Study; NSAID, nonsteroidal anti-inflammatory drug.

Contributor Information

Yin Zhang, Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Mingyang Song, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Andrew T Chan, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Eva S Schernhammer, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Center of Public Health, Medical University of Vienna, Vienna, Austria.

Brian M Wolpin, Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.

Meir J Stampfer, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Jeffrey A Meyerhardt, Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.

Charles S Fuchs, Department of Medical Oncology, Smilow Cancer Hospital and Yale Cancer Center, New Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA.

Susan B Roberts, USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA.

Walter C Willett, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Frank B Hu, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Edward L Giovannucci, Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Kimmie Ng, Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.

Data Availability

Data described in the manuscript, code book, and analytic code may be made available upon application to and approval by the Channing Division of Network Medicine at Brigham and Women's Hospital and Harvard Medical School. Further information including the procedures to obtain and access data from the NHS is described at https://www.nurseshealthstudy.org/researchers (email: nhsaccess@channing.harvard.edu).

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PERMALINK

Unrestrained eating behavior and risk of digestive system cancers: a prospective cohort study

Yin Zhang

Mingyang Song

Andrew T Chan

Eva S Schernhammer

Brian M Wolpin

Meir J Stampfer

Jeffrey A Meyerhardt

Charles S Fuchs

Susan B Roberts

Walter C Willett

Frank B Hu

Edward L Giovannucci

Kimmie Ng

ABSTRACT

Background

Objectives

Methods

Results

Conclusions

Introduction

Subjects and methods

Study population

Ascertainment of exposures, outcomes, and deaths

Ascertainment of covariates and effect modifiers

Statistical analysis

Results

Population characteristics

TABLE 1.

Unrestrained eating behavior and digestive system cancer risk (primary analyses)

TABLE 2.

TABLE 3.

Unrestrained eating behavior and digestive system cancer risk (sensitivity analyses)

Unrestrained eating behavior and digestive system cancer risk (stratified analyses)

Joint associations of unrestrained eating behavior and BMI with digestive system cancer risk

TABLE 4.

Joint associations of unrestrained eating behavior and physical activity with digestive system cancer risk

TABLE 5.

Discussion

Supplementary Material

Acknowledgments

Notes

Contributor Information

Data Availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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