American Journal of Epidemiology
Copyright © 2002 by the Johns Hopkins Bloomberg School of Public Health
All rights reserved
Vol. 156, No. 7
Printed in U.S.A.
DOI: kwf084
Differential Association of Body Mass Index and Fat Distribution with Three Major
Histologic Types of Lung Cancer: Evidence from a Cohort of Older Women
J. E. Olson1, P. Yang1, K. Schmitz2, R. A. Vierkant1, J. R. Cerhan1, and T. A. Sellers1
1
2
Health Sciences Research, Mayo Foundation, Rochester, MN.
Division of Epidemiology, University of Minnesota, Minneapolis, MN.
Received for publication July 30, 2001; accepted for publication May 24, 2002.
The Iowa Women’s Health Study, a prospective cohort study of 41,836 Iowa women aged 55–69 years at
baseline in 1986, reported that lung cancer was inversely associated with body mass index (BMI) and waist/hip
ratio. Risk by histologic subtype was not examined. Through 1998, 596 cases of lung cancer were identified. After
adjustment for established risk factors, women in the upper BMI quintile were at decreased risk of all lung cancer
subtypes, especially squamous cell carcinoma; the highest versus the lowest quintile of BMI was associated with
a relative risk of 0.22 (p-trend = 0.005). Conversely, the highest quintile of waist circumference was positively
associated with small cell and squamous cell lung cancer (relative risks = 3.31 and 3.05, respectively). No
association of waist circumference with risk of adenocarcinoma of the lung was found. There were too few cases
of squamous cell and small cell carcinoma in never smokers to eliminate the possibility that these results are due
to the residual effects of smoking. Alternatively, these results may reflect increased activation of chemicals from
cigarette smoke among women with an increased waist circumference. Results suggest that waist circumference
may be differentially associated with histologic subtypes of lung cancer.
body mass index; cohort studies; fat body; histology; lung neoplasms; risk factors; women
Abbreviations: BMI, body mass index; ICD-O, International Classification of Diseases for Oncology; SEER, Surveillance,
Epidemiology, and End Results.
A previous report from the Iowa Women’s Health Study
examined the association of lung cancer with body mass
index (BMI) and central adiposity, as measured by waist/hip
ratio, through 6 years of follow-up (1). Although there was a
suggestion of decreased risk of lung cancer with higher BMI
and waist/hip ratio among women who never smoked cigarettes, the authors concluded that “the inverse association of
body mass index with lung cancer can be explained by
smoking status and that the positive association of waist/hip
ratio with lung cancer can be explained by pack-years of
smoking” (1, p. 600). Elevated risk of lung cancer associated
with lower levels of BMI has been reported in both casecontrol (2, 3) and cohort (4, 5) studies, although there are
exceptions (6). However, none of these five studies examined risk by histologic subtype of lung cancer.
The three main histologic subtypes of lung cancer include
squamous cell carcinoma, adenocarcinoma, and small cell
carcinomas (7), all of which arise from epithelial cells. Incidence rate patterns of the three subtypes differ over time and
support the concept of differing mechanisms of lung carcinogenesis for the various histologic types (8), although these
differences over time may be partly due to changes in diagnostic and pathologic methods (8). In the 1973–1977
Surveillance, Epidemiology, and End Results (SEER)
program report, squamous cell carcinomas were found to be
twice as common as adenocarcinomas in US males, while
adenocarcinomas were slightly more common in women (7).
By the mid-1980s, the excess rate of squamous cell carcinomas was only 40 percent in men, while adenocarcinomas
were 50 percent more common in women (7).
The goals of the present analysis were to update earlier
Iowa Women’s Health Study analyses to include 7 additional
years of follow-up and to determine whether anthropometric
factors were differentially associated with histologic types of
lung cancer. BMI and BMI at age 18 years were included to
investigate the association of overall body fat with lung
cancer incidence, waist/hip ratio and waist circumference
were included to examine the association of central body fat,
Reprint requests to Dr. Thomas A. Sellers, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 (e-mail: [email protected]).
606
Am J Epidemiol 2002;156:606–615
Obesity and Risk of Lung Cancer Histologic Type 607
and height was included as a “control” anthropometric factor
not hypothesized to influence lung cancer. Earlier studies
have suggested that adenocarcinoma of the lung is less
closely associated with smoking (7) than are squamous and
small-cell lung cancer and has the highest probability of
being influenced by non-tobacco-related causes, including
hormonal effects of body fat distribution. Thus, our a priori
hypothesis was that adenocarcinoma of the lung was the
most likely to be associated with high levels of abdominal
fat.
MATERIALS AND METHODS
Study population
The Iowa Women’s Health Study is a prospective cohort
study designed to identify risk factors for cancer and other
chronic diseases in postmenopausal women (9). In January
1986, a questionnaire was mailed to 99,826 women who had
been selected at random from a list of women aged 55–69
years holding a valid Iowa driver’s license in 1985. The
41,836 respondents (42.7 percent response rate) form the
cohort under study.
Rates of lung cancer among responders were somewhat
lower than those among nonresponders (10). Self-reported
questionnaire items included reproductive factors, height,
current weight, educational level, medication use, alcohol
consumption, smoking habits, and family history of cancer.
Environmental tobacco exposure was not ascertained. Also
included was a semiquantitative food frequency questionnaire with 127 food items and other questions related to
nutrient intake. Physical activity was ascertained through
three questions about participation in leisure exercise and, if
any, the frequency of moderate-intensity and high-intensity
activities. These responses were combined to create a threelevel activity score (low, medium, and high) that has been
shown to be predictive of coronary artery disease (11). Body
circumferences (waist and hips) were measured by using a
paper tape measure mailed with the questionnaire. Measurements obtained by this method have been shown to be both
accurate and reliable (12). Waist/hip ratio (waist circumference/hip circumference) and BMI (weight (kilograms)/
height (meters) squared) were calculated from these data.
Follow-up questionnaires were mailed in 1987, 1989, 1992,
and 1997 to update vital status and current address. Deaths
were ascertained by annual linkage to the Iowa death certificate database, supplemented by linkage to the National
Death Index.
Exclusions and cancer incidence
Women reporting, at baseline, previous cancers other than
skin cancer (n = 3,830) were excluded. The total cohort at
risk for incident lung cancer included 38,006 women. When
analyses included dietary variables, women were considered
to have given “improbable” responses and were therefore
excluded if 30 or more items on the food frequency questionnaire were left blank or if their responses resulted in extreme
energy intake values (<600 or ≥5,000 kcal/day) (n = 2,785).
Incident lung cancer cases occurring in 1986–1998 were
Am J Epidemiol 2002;156:606–615
identified through the State Health Registry of Iowa, part of
the National Cancer Institute’s SEER program (13). A
computer match was performed annually between the list of
cohort members and the records of Iowans for whom incident cancer was listed in the registry by using combinations
of first, last, and maiden names; zip code; birth date; and
Social Security number. Data regarding the diagnoses and
pathology were abstracted by registry personnel from
medical records and pathology reports according to SEER
protocol (13) and were coded by using the International
Classification of Diseases for Oncology (ICD-O), second
edition (14). The histologic subtypes recorded in the cancer
registry were grouped as follows: small cell lung cancer
(ICD-O codes 8041–8044), squamous cell lung cancer (ICDO codes 8050–8076), and adenocarcinoma of the lung (ICDO morphology codes 8140–8380 and 8480–8481). Those
lung cancer cases not considered to belong to these three
categories were not included in the histologic subtype
analyses.
Analysis
The length of follow-up for each woman in the study was
calculated as the time from completion of the baseline questionnaire to the date of lung cancer diagnosis, date of a move
from Iowa, or date of death. If none of these events applied,
follow-up continued through December 31, 1998. Relative
risks and 95 percent confidence intervals were calculated
with Cox proportional hazards regression models. We first
examined the risk of lung cancer by using a set of predetermined potential confounding variables. These variables
included smoking status, pack-years of smoking, physical
activity score, educational status, beer consumption, alcohol
consumption, dietary fat intake, saturated fat intake, total
energy intake, and intake of whole grains, fruit, and
vegetables.
All variables found to be significantly associated with lung
cancer were included as covariates in subsequent analyses.
We then assessed the overall association of lung cancer separately with each of the following anthropometric measures:
BMI, waist/hip ratio, BMI at age 18 years, waist circumference, and height. Each variable was categorized on the basis
of quintiles, and relative risks were calculated by using the
lowest category as the referent group. We calculated tests for
trend by ordering the categories from lowest to highest and
including this variable as a linear variable in a proportional
hazards regression model.
Next, we evaluated whether smoking modified the effect
of the anthropometric measures on lung cancer risk by
checking the statistical significance of the interaction
between these measures and smoking status. For ease of
interpretation, the original set of main effects and interactions was then multiplied by a contrast matrix that allowed
for direct comparison of anthropometrics within each level
of family history but that did not change the overall fit of the
model.
The main effects of anthropometrics were then evaluated
separately for the following three lung cancer cell types:
small cell carcinoma, squamous cell carcinoma, and adenocarcinoma. In these analyses, the outcome variable was inci-
608 Olson et al.
TABLE 1. Pearson correlations between anthropometric measures in the Iowa Women’s Health Study,
1986–1998
Variable
Body mass index
(kg/m2)
Waist/hip ratio
Waist circumference Body mass index at
(cm)
age 18 years
Waist/hip ratio
0.400
Waist circumference
0.829
0.719
Body mass index at age 18 years
0.394
0.097
0.266
–0.119
–0.032
0.080
Height (cm)
dent lung cancer of the specific cell type of interest, and all
other types of lung cancer were considered censored observations. We determined whether risk ratios for exposures of
interest differed according to histologic type by using a
competing risk form of Cox proportional hazards analysis
(15). This approach enabled us to specifically model and test
the unordered interaction between a given risk factor
(modeled as a covariate) and histologic type (included as a
stratum variable).
For all Cox proportional hazards analyses, survival was
modeled as a function of age, since age is a better predictor
of lung cancer risk than is length of follow-up time in this
study (16). Each model included as covariates the statistically significant, potentially confounding variables
mentioned earlier. To account for the effects of smoking as
completely as possible, analyses also included both packyears (modeled as a continuous spline function) and smoking
status (current, former, never) as covariates.
We were concerned that fitting separate models for each of
the five anthropometric measures would not adequately
account for the effects of the other anthropometric variables.
For instance, the association between lung cancer risk and
BMI at age 18 years could be confounded by current BMI.
Thus, we fit additional models, further adjusting for a subset
of the anthropometric measures. Many anthropometric variables were highly correlated with one another (table 1). As a
result, variables were individually selected for each model
after screening for colinearity by using Spearman rank correlations. When two or more highly correlated variables were
included in the same model, special care was taken to
examine goodness of model fit and precision of parameter
estimates. Specific adjustments for each anthropometric
exposure are given as footnotes to the tables in this paper.
All statistical tests were two sided, and all analyses were
carried out by using the SAS (SAS Institute, Inc., Cary,
North Carolina) and S-PLUS (Mathsoft, Inc., Seattle, Washington) software systems.
RESULTS
Of the 38,006 women in the Iowa Women’s Health Study
cohort at risk, 596 developed lung cancer between 1986 and
1998. Histologic subtypes included 123 small cell carcinomas, 115 squamous cell carcinomas, 234 adenocarcinomas, and 124 other types (primarily carcinomas not
otherwise specified (n = 60), large cell carcinomas (n = 35),
malignant neoplasms (n = 13), and adenosquamous carcinoma (n = 9)).
–0.187
In general, associations of factors with lung cancer that
were evident in previous Iowa Women’s Health Study
cohort analyses continued to be found in this analysis. For
example, smoking, pack-years of smoking, and beer
consumption were associated with increased lung cancer risk
(table 2). Increasing physical activity was associated with
decreased risk of subsequent lung cancer. Higher fruit intake
per week was associated with lower risk of lung cancer (ptrend = 0.03). No other dietary factors examined were associated with risk (data not shown). The associations with fruit
(17) and beer (18) are consistent with previous reports from
this cohort. Because of the method of statistical modeling
used, information on the association of age with lung cancer
risk is not presented in table 2. However, increasing age was
associated with increasing risk of lung cancer, as expected.
Compared with women aged 55–59 years, women aged 60–
64 or older than 65 years had a 10 percent or 30 percent
increased risk of lung cancer, respectively.
We examined the association of anthropometric factors of
interest with lung cancer of all histologies combined,
adjusting for important confounders from table 2. As
reported previously for this cohort of women (1), increasing
BMI was associated with decreased risk of lung cancer (table
3). The association became stronger after adjustment for
other anthropometric variables, especially waist circumference. No association was found between waist/hip ratio,
BMI at age 18 years, or height and lung cancer. Prior to
adjustment for BMI, increasing waist circumference was not
associated with risk of lung cancer. However, after adjustment for multiple factors, including other anthropometric
factors (primarily BMI), women in the highest quintile of
waist circumference (>99 cm) were at a 1.8-fold higher risk
of developing lung cancer than were women in the lowest
quintile (95 percent confidence interval: 1.1, 2.7). Adjustment for fruit intake only marginally influenced results but
could be conducted for only those women for whom dietary
data were complete (n = 35,221) (data not shown). To evaluate whether undiagnosed lung cancer may have affected the
anthropometric measures, analyses were performed in two
ways: 1) with all years of follow-up included and 2) with the
first 3 years of follow-up excluded. Risk estimates were
altered only slightly when the first 3 years of follow-up were
excluded (i.e., 0.86 vs. 0.92 for waist circumference, 0.43 vs.
0.50 for BMI); thus, this paper presents only those analyses
that included all years of follow-up.
Table 4 depicts the association of BMI and waist circumference with lung cancer after stratification by smoking
status. In contrast to the previously reported analysis that
Am J Epidemiol 2002;156:606–615
Obesity and Risk of Lung Cancer Histologic Type 609
TABLE 2. Age- and smoking-adjusted association of lifestyle, diet, and demographic factors in the Iowa Women’s Health Study,
1986–1998
Variable
Lung cancer cases
(no.)
Age-adjusted
Person-years
(no.)
RR†
1.00
95% CI†
Smoking-adjusted*
RR
95% CI
Smoking status at baseline
Never
81
297,944
Former
139
83,677
6.27
4.77, 8.25
Current
368
61,947
23.24
18.26, 29.57
p-trend < 0.001
Pack-years of smoking
None
81
297,944
1–19
49
59,211
1.00
3.17
2.23, 4.53
20–39
185
47,847
14.91
11.48, 19.37
≥40
258
34,954
27.90
21.73, 35.82
p-trend < 0.001
Physical activity score
Low
355
207,166
1.00
Moderate
131
122,717
0.62
0.50, 0.75
0.86
98
111,648
0.51
0.40, 0.63
0.74
High
1.00
p-trend < 0.001
0.70, 1.06
0.59, 0.93
p-trend = 0.007
Educational status
<High school
53
40,803
1.00
High school
353
234,742
1.21
0.90, 1.61
1.00
0.95
>High school
189
173,862
0.87
0.64, 1.18
0.79
p-trend = 0.02
0.70, 1.28
0.57, 1.08
p-trend = 0.04
Beer consumption
Never
400
345,065
1.00
Monthly
38
35,194
0.95
0.68, 1.33
1.00
0.83
≥Weekly
123
42,296
2.61
2.13, 3.20
1.36
p-trend < 0.001
0.59, 1.16
1.11, 1.68
p-trend = 0.01
Alcohol consumption (per day)
Never
270
255,133
1.00
≤4 g
104
106,769
0.94
0.75, 1.18
1.00
0.80
>4 g
222
88,721
2.45
2.05, 2.92
1.08
p-trend < 0.001
0.63, 1.00
0.90, 1.30
p-trend = 0.52
Servings of fruit (per week)
≤10.0
222
89,777
1.00
10.1–14.5
78
70,959
0.43
0.34, 0.56
1.00
0.67
0.51, 0.87
14.6–19.0
100
83,159
0.47
0.37, 0.60
0.88
0.69, 1.12
19.1–25.5
83
95,316
0.34
0.26, 0.44
0.68
0.52, 0.88
>25.5
70
79,078
0.34
0.26, 0.45
0.80
p-trend < 0.001
0.61, 1.06
p-trend = 0.03
* Adjusted for age, smoking status (never, former, current), and pack-years of smoking (continuous).
† RR, relative risk; CI, confidence interval.
included only 6 years of follow-up (1), stratification by
smoking status did not eliminate the association between
BMI and lung cancer. Increasing BMI was associated with
Am J Epidemiol 2002;156:606–615
decreased risk of lung cancer, even among women who had
never smoked; relative risks across the quintiles were 1.0,
0.8, 0.5, 0.4, and 0.4 (p-trend = 0.008). The trend across
610 Olson et al.
TABLE 3. Multivariate-adjusted association of anthropometric factors with lung cancer in the Iowa Women’s Health Study, 1986–1998
Variable
Lung cancer
cases (no.)
Personyears (no.)
Age-adjusted
RR*
Multivariate-adjusted† (without
anthropometric covariates)
RR
95% CI*
Multivariate-adjusted† (with
anthropometric covariates)
RR
95% CI
Body mass index (kg/m2)‡
≤22.89
168
88,175
1.00
1.00
22.90–25.04
126
92,949
0.73
0.92
0.73, 1.16
0.86
0.66, 1.12
25.05–27.43
87
89,587
0.52
0.76
0.58, 0.98
0.66
0.47, 0.91
27.44–30.69
81
90,498
0.47
0.69
0.52, 0.90
0.53
0.36, 0.78
≥30.70
70
89,414
0.42
0.66
0.50, 0.89
0.43
0.27, 0.69
p-trend < 0.001
p-trend < 0.001
1.00
p-trend < 0.001
Waist/hip ratio§
≤0.76
100
100,867
1.00
1.00
0.77–0.80
97
81,715
1.14
1.01
0.76, 1.34
1.08
0.81, 1.43
0.81–0.85
117
101,692
1.19
0.95
0.73, 1.24
1.06
0.81, 1.40
0.86–0.90
98
80,022
1.22
0.97
0.73, 1.28
1.15
0.86, 1.55
117
84,415
1.38
1.00
0.76, 1.32
1.29
0.96, 1.75
p-trend < 0.001
p-trend = 0.92
>0.90
1.00
p-trend = 0.10
Body mass index at age
18 years (kg/m2)¶
≤18.60
99
87,584
1.00
1.00
18.61–19.96
101
91,714
0.97
0.97
0.73, 1.28
1.00
1.03
0.78, 1.36
19.97–21.16
123
90,230
1.21
1.19
0.91, 1.55
1.32
1.01, 1.73
21.17–22.90
101
90,359
1.07
0.92
0.70, 1.21
1.05
0.79, 1.40
>22.90
108
88,662
1.14
0.91
0.69, 1.19
1.08
0.80, 1.44
p-trend = 0.22
p-trend = 0.41
p-trend = 0.62
Waist circumference (cm)#
≤75.56
125
88,111
1.00
1.00
75.57–81.92
122
90,069
0.95
1.04
0.81, 1.34
1.18
0.90, 1.55
81.93–89.54
98
95,628
0.72
0.80
0.62, 1.05
1.06
0.77, 1.46
85.55–99.0
89
88,532
0.70
0.82
0.63, 1.09
1.31
0.90, 1.89
>99.0
95
86,634
0.78
0.91
0.69, 1.19
1.76
1.14, 2.73
p-trend = 0.004
p-trend = 0.15
1.00
p-trend = 0.04
Height (cm)**
≤157.5
157.6–160.0
158
121,212
1.00
1.00
66
64,914
0.84
0.84
0.63, 1.12
1.00
0.82
0.62, 1.10
160.1–162.6
82
76,166
0.83
0.93
0.71, 1.21
0.89
0.68, 1.17
162.7–167.6
125
114,929
0.87
0.90
0.71, 1.14
0.82
0.65, 1.04
>167.6
101
73,402
1.04
1.15
0.89, 1.48
1.05
0.81, 1.36
p-trend = 0.92
p-trend = 0.54
p-trend = 0.78
* RR, relative risk; CI, confidence interval.
† Adjusted for age, pack-years of smoking (continuous), smoking status (never, former, current), physical activity score, educational level,
and beer consumption.
‡ Also adjusted for height, body mass index at age 18 years, and waist circumference.
§ Also adjusted for height, body mass index, and body mass index at age 18 years.
¶ Also adjusted for height, body mass index, and waist circumference.
# Also adjusted for body mass index, height, and body mass index at age 18 years.
** Also adjusted for body mass index, waist circumference, and body mass index at age 18 years.
Am J Epidemiol 2002;156:606–615
Obesity and Risk of Lung Cancer Histologic Type 611
increasing waist circumference quintiles divided into
smoking status groups remained statistically significant for
current smokers only (p = 0.01). The lung cancer risk across
increasing waist circumference quintiles of nonsmokers
displayed no clear pattern: compared with women in the
lowest quintile, the relative risks were 1.1, 0.5, 0.5, and 1.4
for each succeeding quintile.
Table 5 displays the associations between various anthropometric factors and risk of specific histologic types of lung
cancer. After we adjusted for established risk factors, women
in the upper quintile of BMI were found to be at decreased
risk of all major lung cancer subtypes. Increasing levels of
BMI were generally associated with decreasing risk of all
three main histologic types of lung cancer, but the trend
across BMI quintiles was statistically significant for squamous cell carcinoma only (p-trend = 0.005). Waist circumference was positively associated with only small cell and
squamous cell lung cancer (relative risks = 3.3 and 3.1,
respectively, for the highest vs. the lowest quintile of waist
circumference). In contrast, there was no association of waist
circumference with risk of adenocarcinoma of the lung
(relative risk = 0.78 for the highest vs. the lowest quintile, ptrend = 0.69). No clear patterns were observed for waist/hip
ratio, BMI at age 18 years, or height with regard to any of the
three histologic subtypes. Although there were too few cases
to stratify the histologic subtypes by smoking status, there
were enough cases of adenocarcinoma in nonsmokers to
examine the association of BMI with adenocarcinoma. In
our data, BMI was inversely associated with risk of adenocarcinoma in nonsmokers (relative risk across the quintiles =
1.0, 0.74, 0.60, 0.48, and 0.64 (all 95 percent confidence
intervals included 1.0, p-trend = 0.23) (data not shown).
DISCUSSION
The purpose of this study was twofold. First, we wanted to
update an earlier Iowa Women’s Health Study report of the
association between anthropometrics and lung cancer (1) to
include 7 additional years of follow-up. Second, we wanted
to extend the earlier analysis to determine whether anthropometric factors were differentially associated with histologic
lung cancer type. We had hypothesized that, of the three
main histologic subtypes, adenocarcinoma of the lung was
the most likely to be influenced by anthropometric factors.
This hypothesis was based on data indicating that the
etiology of adenocarcinoma was most likely to include
factors not related to smoking.
Unlike the previous analysis, which concluded that “the
inverse association of body mass index with lung cancer can
be explained by smoking status” (1, p. 600), we found that
BMI remained significantly inversely associated with lung
cancer after multivariate adjustment. In addition, because
cigarette smoking is associated with decreased body weight
(19), we examined whether the inverse BMI–lung cancer
association was explained by confounding from cigarette
smoking. This did not appear to be the case, because the
association between BMI and lung cancer was of the same
magnitude even for women who had never smoked cigarettes. In the earlier report (1), although women never
smokers in the highest tertile of BMI were 50 percent less
Am J Epidemiol 2002;156:606–615
likely to develop lung cancer compared with women in the
lowest tertile of BMI (relative risk = 0.68), the estimate did
not exclude 1.0 and the test for trend was not statistically
significant. In case some of the difference between the two
reports was caused by the use of different BMI category
cutpoints (tertiles instead of quintiles), we recalculated the
estimates by using the same category cutpoints. On the basis
of these recalculations, women never smokers in the lowest
BMI tertile were half as likely to have developed lung cancer
compared with women in the highest BMI tertile (relative
risk = 0.52, 95 percent confidence interval: 0.28, 0.95; ptrend = 0.03). Women in the middle tertile were at similarly
decreased risk of lung cancer (relative risk = 0.58, 95 percent
confidence interval: 0.33, 1.02). Other differences between
this report and the previous one include 363 additional cases
and 7 additional years of follow-up.
It is possible that the inverse association with high BMI
becomes gradually evident with time so that, although not
statistically significant earlier, after additional follow-up and
with the increased statistical power from additional cases,
the effect becomes apparent. A similar latency was seen in a
cohort of Finnish men (4); the protective effect of increasing
levels of BMI became more distinct after 10 years of followup.
As with any multivariate analysis, one must be careful to
understand the correlation between variables. In the current
study, the correlation of anthropometric indices ranged from
0.1 to 0.8, raising two major issues. First, when multicollinearity exists, sampling variability of parameter estimates
tends to be larger (20). In the initial multivariate models,
prior to adjustment for other anthropometric factors, standard errors for the waist circumference parameter estimates
ranged from 0.13 to 0.14. After adjustment for other anthropometric variables, the standard errors were larger than
before but still quite reasonable (standard errors ranged from
0.14 to 0.22). A second implication of multicollinearily is
that the individual parameter estimates cannot be interpreted
as estimating the effect of the exposure on the outcome risk
while all other covariates are held fixed. Because correlated
variables vary simultaneously, one must consider the variables at the same time. BMI should be included in the multivariate model because it fits the classic definition of a
confounder for the relation between waist circumference and
lung cancer risk, in that it is strongly associated with both the
exposure (waist circumference) and the outcome (lung
cancer). Indeed, the interpretation of the waist circumference
effect (made within the constraints of the second implication
defined above) is intriguing—that an association exists
between lung cancer risk and location of adiposity even after
accounting for the amount of adiposity.
An elevated risk of lung cancer associated with lower
levels of BMI has been reported in both case-control (2, 3)
and cohort (4, 5) studies. More recently, however, a casecontrol study among former (>10 years since cessation of
smoking) and never smokers found that persons in the upper
octile of BMI had a twofold greater risk of lung cancer (6).
These authors explained the results as due to the large
number of nonsmokers (n = 188) included in the analyses.
Although the present study included about only half that
number (n = 81), our estimates appear quite stable and
612 Olson et al.
TABLE 4. Multivariate associations of body mass index and waist circumference at baseline with lung
cancer risk, by smoking category, Iowa Women’s Health Study, 1986–1998
Variable
Lung cancer cases
(no.)
Person-years
(no.)
RR*
Multivariate-adjusted
95% CI*
Body mass index (kg/m2)†
Current smokers
≤22.89
115
20,395
1.00
22.90 – 25.04
77
13,839
0.91
0.67, 1.25
25.05 – 27.43
54
10,644
0.79
0.54, 1.16
27.44 – 30.69
49
9,885
0.66
0.43, 1.02
≥30.70
30
7,184
0.47
0.27, 0.81
p-trend = 0.006
Former smokers
≤22.89
34
15,908
1.00
22.90–25.04
30
17,567
0.68
0.41, 1.13
25.05–27.43
19
16,166
0.45
0.25, 0.81
27.44–30.69
22
16,808
0.38
0.21, 0.70
≥30.70
24
17,229
0.32
0.17, 0.61
p-trend < 0.001
Never smokers
≤22.89
18
50,628
1.00
22.90–25.04
19
60,129
0.82
0.43, 1.57
25.05–27.43
13
61,438
0.51
0.24, 1.06
27.44–30.69
10
62,269
0.35
0.15, 0.79
≥30.70
16
63,480
0.44
0.21, 0.95
p-trend = 0.008
Waist circumference (cm)‡
Current smokers
≤75.56
82
16,557
1.00
75.57–81.92
71
13,813
1.13
0.84, 1.63
81.93–89.54
68
12,469
1.31
0.91, 1.89
85.55–99.0
57
10,333
1.56
1.02, 2.38
>99.0
47
8,567
1.83
1.11, 3.01
p-trend = 0.01
Former smokers
≤75.56
23
15,870
1.00
75.57–81.92
31
16,813
1.30
0.75, 2.26
81.93–89.54
20
17,976
0.85
0.45, 1.59
85.55–99.0
24
15,591
1.29
0.69, 2.42
>99.0
31
17,150
1.62
0.85, 3.09
p-trend = 0.25
Never smokers
≤75.56
20
54,463
1.00
75.57–81.92
20
57,867
1.06
0.57, 1.99
81.93–89.54
10
63,988
0.55
0.25, 1.20
85.55–99.0
8
61,204
0.53
0.23, 1.26
18
59,338
1.43
0.69, 2.97
>99.0
p-trend = 0.99
* RR, relative risk; CI, confidence interval.
† Adjusted for age, pack-years of smoking (continuous), physical activity score, educational level, beer
consumption, height, waist circumference, and body mass index at age 18 years.
‡ Adjusted for age, pack-years of smoking (continuous), physical activity score, educational level, beer
consumption, height, body mass index, and body mass index at age 18 years.
Am J Epidemiol 2002;156:606–615
Obesity and Risk of Lung Cancer Histologic Type 613
TABLE 5. Multivariate*-adjusted association of anthropometric factors and lung cancer, by cell type, Iowa Women’s Health Study,
1986–1998
Small cell carcinoma
Squamous cell carcinoma
Adenocarcinoma
Variable
p value†
Cases (no.)
RR‡
95% CI‡
Cases (no.)
RR
95% CI
Cases (no.)
RR
95% CI
Body mass index (kg/
m2)§
0.89
≤22.89
27
1.00
37
1.00
64
1.00
22.90–25.04
23
1.00
0.53, 1.88
28
0.90
0.52, 1.58
49
0.91
0.60, 1.38
25.05–27.43
19
0.87
0.41, 1.88
13
0.50
0.23, 1.08
39
0.83
0.50, 1.38
27.44–30.69
21
0.72
0.31, 1.71
16
0.42
0.18, 1.00
33
0.70
0.38, 1.27
≥30.70
19
0.60
0.22, 1.61
12
0.22
0.08, 0.64
25
0.62
0.30, 1.32
p-trend = 0.28
p-trend = 0.005
p-trend = 0.18
Waist/hip ratio¶
0.64
≤0.76
16
1.00
0.77–0.80
17
1.04
0.81–0.85
21
1.01
0.86–0.90
26
>0.90
29
23
1.00
0.52, 2.06
15
0.72
0.52, 1.98
21
0.81
1.54
0.79, 2.99
21
1.46
0.73, 2.89
25
p-trend = 0.14
48
1.00
0.38, 1.39
42
1.03
0.67, 1.56
0.44, 1.48
51
1.03
0.68, 1.55
1.04
0.56, 1.94
31
0.82
0.51, 1.32
1.22
0.64, 2.32
38
0.98
0.60, 1.58
p-trend = 0.36
p-trend = 0.65
Body mass index at
age 18 years#
≤18.60
15
1.00
26
1.00
38
1.00
18.61–19.96
19
1.20
0.61, 2.38
18
0.70
0.38, 1.27
42
1.11
0.71, 1.72
19.97–21.16
30
1.87
1.00, 3.51
19
0.77
0.42, 1.41
45
1.26
0.81, 1.95
21.17–22.90
24
1.35
0.70, 2.60
13
0.53
0.27, 1.05
44
1.22
0.78, 1.91
>22.90
21
1.02
0.51, 2.04
30
1.17
0.67, 2.05
41
1.19
0.74, 1.90
p-trend = 0.95
p-trend = 0.80
0.17
p-trend = 0.40
Waist circumference
(cm)
0.14
≤75.56
20
1.00
26
1.00
55
1.00
75.57–81.92
22
1.31
0.67, 2.53
26
1.21
0.67, 2.18
47
0.94
0.61, 1.44
81.93–89.54
16
1.00
0.45, 2.20
19
1.08
0.54, 2.18
41
0.86
0.52, 1.41
85.55–99.0
21
1.71
0.72, 4.03
12
1.04
0.44, 2.50
40
1.03
0.58, 1.83
>99.0
30
3.31
1.27, 8.60
23
3.05
1.19, 7.80
27
0.78
0.38, 1.61
p-trend = 0.02
p-trend = 0.11
p-trend = 0.69
Height (cm)**
0.88
≤157.5
29
1.00
157.6–160.0
17
1.13
160.1–162.6
16
0.93
162.7–167.6
25
>167.6
22
35
1.00
0.62, 2.07
11
0.64
0.50, 1.71
11
0.56
0.90
0.52, 1.56
28
1.16
0.65, 2.08
21
p-trend = 0.94
59
1.00
0.32, 1.26
25
0.83
0.52, 1.32
0.28, 1.11
33
0.96
0.62, 1.47
0.83
0.50, 1.38
49
0.89
0.61, 1.31
0.92
0.52, 1.63
44
1.29
0.86, 1.93
p-trend = 0.74
p-trend = 0.43
* All variables were adjusted for age, pack-years of smoking (continuous), smoking status (never, former, current), physical activity score,
educational level, and beer consumption.
† p value for the test of interaction between each risk factor and histologic cell type.
‡ RR, relative risk; CI, confidence interval.
§ Also adjusted for height, body mass index at age 18 years, and waist circumference.
¶ Also adjusted for height, body mass index, and body mass index at age 18 years.
# Also adjusted for height, body mass index, and waist circumference.
** Also adjusted for body mass index, waist circumference, and body mass index at age 18 years.
Am J Epidemiol 2002;156:606–615
614 Olson et al.
support a protective role of high BMI against lung cancer.
Unfortunately, we could not examine the impact of environmental tobacco exposure on this association.
Another important finding of our study was that the
highest level of waist circumference was positively associated with risk of lung cancer, but only after adjustment for
BMI. Conversely, waist/hip ratio was not associated with
lung cancer before or after adjustment for BMI. This difference may be explained by the fact that waist circumference
is more strongly associated with BMI than is waist/hip ratio;
therefore, one would expect the association with waist
circumference to show the largest increase after adjustment
for BMI. Other than the previous report from the Iowa
Women’s Health Study that used waist/hip ratio as an indicator for centralized versus peripheral fat (1), we know of no
other group that has examined risk of lung cancer associated
with fat distribution. This is the first known report of a positive association between waist circumference and risk of
lung cancer. However, it is quite likely that this association
is at least partly due to residual effects of cigarette smoking;
the association was strongest among current smokers, and
cigarette smoking has been shown to be associated with
increased levels of abdominal fat (19). It is also possible that
a higher waist circumference in a smoker is a marker for
greater levels of smoking and other poor lifestyle habits that
might contribute to carcinogenicity. Examples of such poor
habits might include poor levels of physical activity, poor
diet, and infrequent preventive health care visits.
One of the primary goals of this study was to examine the
association of anthropometric factors stratified by histologic
type. As far as we are aware, this is the first report of such an
analysis. Quintiles of BMI were inversely related to risk of
all histologic subtypes of lung cancer. Women in the highest
(vs. lowest) quintile of BMI were 40 percent less likely to
develop small cell carcinoma or adenocarcinoma of the lung
and more than 75 percent less likely to develop squamous
cell carcinoma. The inverse association of BMI was still
evident for adenocarcinoma of the lung, even among never
smokers. We were unable to stratify the analysis by cigarette
smoking for all histologic subtypes because there were too
few cases of squamous and small cell histologic types among
nonsmokers to permit such an analysis. Thus, we were not
able to completely eliminate the possibility that the
decreased risk was due to the residual effect of cigarette
smoking on BMI. However, on the basis of the data for all
lung cancers, this possibility seems unlikely.
The effect of large waist circumference was also examined
across specific histologic subtypes. Earlier studies had
suggested that adenocarcinoma was least closely associated
with smoking (7) and would have the highest probability of
being influenced by non-tobacco-related causes, including
hormonal effects of body fat distribution. Thus, we expected
a priori that adenocarcinoma of the lung was most likely to
be associated with a high level of abdominal fat. However,
we found that waist circumference was associated with small
cell and squamous cell carcinoma but not with risk of adenocarcinoma of the lung.
We must acknowledge that multiple factors were examined and that these results may reflect a chance association.
However, the finding is not without biologic plausibility.
The physiologic effects of high levels of abdominal fat may
exacerbate the toxic pathways leading to squamous and
small cell carcinoma of the lung. It has been hypothesized
that all lung cancers arise from similar cells but that the
specific promoters present at the time in the surrounding
cellular environment determine which histologic subtype
actually develops (8). Cigarette smoke is known to increase
abdominal fat (21), and there are numerous known physiologic effects of abdominal adiposity (22). For example, it has
been linked to increased levels of free fatty acids, insulin,
and unbound androgens and estrogens. It is unclear whether
any of these effects or some other physiologic effect of
abdominal adipose might be important in promoting cells to
develop into squamous or small cell carcinoma rather than
adenocarcinoma. However, it is interesting that squamous
and small cell carcinomas both tend to develop centrally in
the lung, whereas adenocarcinomas tend to be located in the
peripheral lung tissue. It may be that some factor linked to
high levels of abdominal fat is found primarily in the central
portion of the lung and promotes carcinogenesis in initiated
cells toward either small cell or squamous cell carcinoma.
Both waist circumference and waist/hip ratio are indices of
central adiposity. However, in this analysis, only waist
circumference was associated with risk of lung cancer
overall or for a particular histologic subtype of lung cancer.
This finding was somewhat surprising, since waist/hip ratio
was associated with risk of lung cancer in this cohort in the
earlier analysis (1). One possible explanation may be that
waist circumference has been shown to be a more accurate
estimator of central adiposity than waist/hip ratio (23, 24).
The other anthropometric factors examined, height and BMI
at age 18 years, were not associated with risk of lung cancer
in general or with histologic subtypes of lung cancer.
For this study, cases of lung cancer were collected by the
State Health Registry of Iowa, part of the National Cancer
Institute’s SEER program (13). Although desirable, no
centralized pathology review was feasible. However, one of
the SEER registries conducted a pathology review of a
subset of lung cancer cases from 1970 to 1972 and from
1980 to 1981 (25) and found that, for the cell types discussed
in this paper, there was generally good agreement between
the initial pathology reading and the review reading. A
strength of this study is the homogeneity of the at-risk population included. Nearly all were postmenopausal women,
which is an advantage when examining factors such as body
weight and fat distribution that change around and after
menopause. We did not collect information on exposure to
secondhand smoke and were unable to control for it in these
analyses.
In conclusion, this study raises the possibility that waist
circumference may be differentially associated with histologic subtypes of lung cancer. There were too few cases
among nonsmokers to eliminate the possibility that these
results were due to the residual effects of smoking. However,
these results may reflect increased activation of chemicals
from cigarette smoke in those who have an increased waist
circumference.
Am J Epidemiol 2002;156:606–615
Obesity and Risk of Lung Cancer Histologic Type 615
ACKNOWLEDGMENTS
This work was supported by a grant from the National
Cancer Institute (R01 CA39742).
REFERENCES
1. Drinkard CR, Sellers TA, Potter JD, et al. Association of body
mass index and body fat distribution with risk of lung cancer in
older women. Am J Epidemiol 1995;142:600–7.
2. Kabat GC, Wynder EL. Body mass index and lung cancer risk.
Am J Epidemiol 1992;135:769–74.
3. Goodman MT, Wilkens LR. Relation of body size and the risk
of lung cancer. Nutr Cancer 1993;20:179–86.
4. Knekt P, Heliovaara M, Rissanen A, et al. Leanness and lungcancer risk. Int J Cancer 1991;49:208–13.
5. Chyou PH, Nomura AM, Stemmermann GN. A prospective
study of weight, body mass index and other anthropometric
measurements in relation to site-specific cancers. Int J Cancer
1994;57:313–17.
6. Rauscher GH, Mayne ST, Janerich DT. Relation between body
mass index and lung cancer risk in men and women never and
former smokers. Am J Epidemiol 2000;152:506–13.
7. Blot WJ, Fraumeni JF Jr. Cancers of the lung and pleura. In:
Schottenfeld D, Fraumeni JF Jr, eds. Cancer epidemiology and
prevention. New York, NY: Oxford University Press, 1996:
637–65.
8. Devesa SS, Shaw GL, Blot WJ. Changing patterns of lung cancer incidence by histological type. Cancer Epidemiol Biomarkers Prev 1991;1:29–34.
9. Folsom AR, Kaye SA, Prineas RJ, et al. Increased incidence of
carcinoma of the breast associated with abdominal adiposity in
postmenopausal women. Am J Epidemiol 1990;131:794–803.
10. Bisgard KM, Folsom AR, Hong CP, et al. Mortality and cancer
rates in nonrespondents to a prospective study of older women:
5-year follow-up. Am J Epidemiol 1994;139:990–1000.
11. Folsom AR, Arnett DK, Hutchinson RG, et al. Physical activity
and incidence of coronary heart disease in middle-aged women
and men. Med Sci Sports Exerc 1997;29:901–9.
12. Kushi LH, Kaye SA, Folsom AR, et al. Accuracy and reliability
of self-measurement of body girths. Am J Epidemiol 1988;128:
740–8.
Am J Epidemiol 2002;156:606–615
13. Ries LA, Eisner MP, Kosary CL, et al, eds. SEER cancer statistics review, 1973–1997. Bethesda, MD: National Cancer Institute, 2000. (National Institutes of Health publication no. 00–
2786).
14. World Health Organization. International classification of diseases for oncology. 2nd ed. Geneva, Switzerland: World Health
Organization, 1990.
15. Lunn M, McNeil D. Applying Cox regression to competing
risks. Biometrics 1995;51:524–32.
16. Korn EL, Graubard BI, Midthune D. Time-to-event analysis of
longitudinal follow-up of a survey: choice of the time-scale.
Am J Epidemiol 1997;145:72–80.
17. Steinmetz KA, Potter JD, Folsom AR. Vegetables, fruit, and
lung cancer in the Iowa Women’s Health Study. Cancer Res
1993;53:536–43.
18. Potter JD, Sellers TA, Folsom AR, et al. Alcohol, beer, and
lung cancer in postmenopausal women. The Iowa Women’s
Health Study. Ann Epidemiol 1992;2:587–95.
19. Baron JA, Rohan TE. Tobacco. In: Schottenfeld D, Fraumeni
JF Jr, eds. Cancer epidemiology and prevention. New York,
NY: Oxford University Press, 1996:269–89.
20. Neter J, Wasserman W, Kutner MH. Applied linear regression
models. 2nd ed. Homewood, IL: Richard D Irwin, Inc, 1989:
300–1.
21. Baron JA, Newcomb PA, Longnecker MD, et al. Cigarette
smoking and breast cancer. Cancer Epidemiol Biomarkers Prev
1996;3:399–403.
22. Campagnoli C, Biglia N, Belforte P, et al. Post-menopausal
breast cancer risk: oral estrogen treatment and abdominal obesity induce opposite changes in possibly important biological
variables. Eur J Gynaecol Oncol 1992;13:139–54.
23. Taylor RW, Keil D, Gold EJ, et al. Body mass index, waist
girth, and waist-to-hip ratio as indexes of total and regional adiposity in women: evaluation using receiver operating characteristic curves. Am J Clin Nutr 1998;67:44–9.
24. Hill JO, Sidney S, Lewis CE, et al. Racial differences in
amounts of visceral adipose tissue in young adults: the CARDIA (Coronary Artery Risk Development in Young Adults)
study. Am J Clin Nutr 1999;69:381–7.
25. Butler C, Samet JM, Humble CG, et al. Histopathology of lung
cancer in New Mexico, 1970–72 and 1980–81. J Natl Cancer
Inst 1987;78:85–90.