- Email: [email protected]
Anthropometric factors and ovarian cancer risk in the Malmö Diet and Cancer Study
Cancer Epidemiology 35 (2011) 432–437
Contents lists available at ScienceDirect
Cancer Epidemiology The International Journal of Cancer Epidemiology, Detection, and Prevention journal homepage: www.cancerepidemiology.net
Anthropometric factors and ovarian cancer risk in the Malmo¨ Diet and Cancer Study Jenny Bra¨ndstedt a,b,*, Bjo¨rn Nodin a, Jonas Manjer b,c, Karin Jirstro¨m a a
Center for Molecular Pathology, Department of Laboratory Medicine, Lund University, Ska˚ne University Hospital, Malmo¨, Sweden Division of Surgery, Department of Clinical Sciences, Lund University, Ska˚ne University Hospital, Malmo¨, Sweden c The Malmo¨ Diet and Cancer Study, Lund University, Malmo¨, Sweden b
A R T I C L E I N F O
A B S T R A C T
Article history: Accepted 1 January 2011 Available online 1 February 2011
Objective: To examine the associations of measured anthropometric factors, including general and central adiposity, with epithelial ovarian cancer (EOC) risk in the Malmo¨ Diet and Cancer Study. Methods: In 93 incident EOC cases from a Swedish population-based prospective cohort study, seven anthropometric factors; height, weight, BMI, body fat percentage, waist- and hip circumference, and waist-hip ratio (WHR), were categorized by tertiles of baseline anthropometric measurements and relative risks were calculated using multivariate Cox regression models. Results: A high WHR (<0.77, 0.77 to <0.81, 0.81 cm/cm) was associated with a statistically significantly lower overall risk for EOC (RR 0.60; 0.36–1.00; p-trend = 0.04), particularly tumours of differentiation grades 1 and 2 (RR 0.27; 0.09–0.81; p-trend = 0.03) and clinical stages 1 and 2 (RR 0.32; 0.10–0.97; p-trend = 0.03) and these associations were stronger in postmenopausal women. Neither height, weight, BMI, body fat percentage, waist- or hip circumference were associated with overall risk, nor with risk for different subtypes, differentiation grade or stage. Conclusions: These results demonstrate that a high WHR is associated with a decreased risk of EOC. Other anthropometric factors were not associated with EOC risk. ß 2011 Elsevier Ltd. All rights reserved.
Keywords: Anthropometry Ovarian cancer risk Histological subtype Differentiation grade Clinical stage
1. Introduction Ovarian cancer is the leading cause of death from gynaecological malignancy and the fifth most common cause of cancerrelated death in women in Western Europe and North America [1]. The poor survival in ovarian cancer is related to the high percentage of cases that are diagnosed at an advanced stage [2]. Thus, it is important to identify potentially modifiable risk factors for prevention of ovarian cancer. Reproductive factors such as nulliparity and non-use of oral contraceptive (OC) are well established risk factors of ovarian cancer. Since central (abdominal) adiposity is associated with a series of hormonal and metabolic changes it is thought to potentially influence ovarian cancer risk [3–5]. The association between obesity and ovarian cancer risk has been investigated in several studies, but the results have been inconclusive [6]. Findings from a pooled analysis of 12 cohort studies indicated that obesity was associated with an increased risk of ovarian cancer in premenopausal women, but not
* Corresponding author at: Center for Molecular Pathology, Department of Laboratory Medicine, Lund University, Ska˚ne University Hospital, SE-205 02 Malmo¨, Sweden. Tel.: +46 40 331402; fax: +46 40 337322. E-mail address: [email protected] (J. Bra¨ndstedt). 1877-7821/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.canep.2011.01.003
in postmenopausal women [7,8]. Contrary to this, a recent large prospective cohort study showed a positive association between obesity and risk of ovarian cancer in postmenopausal women, but not in premenopausal women [9]. The contradictory results between studies might partly be due to the use of different definitions of overweight and obesity, usually studied as weight or body mass index (BMI). It is, however, also possible that other anthropometric factors such as height or central adiposity may be even more important in relation to cancer risk [7,10,11]. Given that different histological subgroups of EOC have distinct morphological features and clinical behaviour, it could be hypothesized that they may also have different aetiologies. Most epidemiologic studies have treated ovarian cancer as one single disease, including those confirming established risk factors such as nulliparity and non-use of OC [4,12,13]. However, only a few epidemiological studies have investigated the relationship between anthropometric factors and specific histological subtypes of EOC, and their findings have been inconclusive [14–16]. In addition, we are not aware of any studies investigating whether risk factors differ according to the degree of aggressiveness of the tumours, here studied as differention grade and tumour stage. Furthermore, little is known about anthropometric factors and EOC prognosis [17,18].
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
The primary aim of this study was to examine the relationship between anthropometric factors and risk of EOC in the Malmo¨ Diet and Cancer Study. The secondary aim was aim to explore a potential association with clinicopathological parameters, e.g. stage, histological grade and histological subtype, which, to our knowledge, has not been done before. 2. Materials and methods 2.1. The Malmo¨ Diet and Cancer Study The Malmo¨ Diet and Cancer Study (MDCS) is a populationbased prospective cohort study which enrolled 17,035 women during baseline examinations between 1991 and 1996. Data on lifestyle, health and socio-demographic characteristics were collected via standardized questionnaires that included menstrual and reproductive history; level of education, age at menarche, parity, exposure to OC, menopausal status, use of hormonal replacement therapy (HRT), and smoking habits. Menopausal status was defined using information from the questionnaire and a record linkage with the local hospital registry concerning previous gynaecological surgery. This has previously been described in detail [19]. Ethical permissions for the MDCS (LU 90-51), and the present study (LU 530/2008), were obtained from the Ethical Committee at Lund University. 2.2. Anthropometric measurements At baseline examination, weight (multiples of 0.1 kg) and height (to the nearest 0.005 m) were measured by a trained nurse, and body mass index (BMI) was calculated as kg/m2. Waist circumference was measured at the midpoint between the lower ribs and the iliac crest, and for hip circumference the level of greatest lateral extension was used. These measurements were estimated to the nearest 0.01 m. The waist and hip circumferences of each participant were used to calculate waist-hip ratio (WHR; cm/cm) as an additional measure of fat distribution. Body composition was estimated using a single frequency bioimpedance methodology (BIA 103, RLJ-systems, Detroit, MI, USA) with tetra polar electrode placement and subjects in a supine position. Lean body mass and fat mass were determined and served to calculate body fat percentage. The BIA method has previously been validated in Swedish middle-aged and elderly adults [20]. 2.3. Follow-up for cancer and cause-of-death Incident cases of ovarian cancer in the MDCS were identified through the Swedish Cancer Registry and vital status was determined by record linkage with the Swedish Cause-of-Death Registry. End of follow-up was 31 December 2007. Cause of death in EOC cases was retrieved from medical charts and the Swedish Cause-of-Death Registry. Women were followed from the date of enrolment until ovarian cancer diagnosis, death, emigration or end of follow-up. Median time from baseline until diagnosis was 7.3 (SD: 4.3) years and the median follow-up time in the entire cohort was 13.1 (SD: 2.5) years. 2.4. Study population Among the 17,035 women in the cohort, there were a total number of 168 cases of malignant ovarian cancer. Among these, 67 were diagnosed before baseline examination, i.e. prevalent cancers, and they were therefore excluded, resulting in a study population of 16,968 women. Prevalent cancers of other sites were not excluded from the study.
433
2.5. Tumour characteristics Of the 101 incident tumours, 8 tumours were non-epithelial, resulting in 93 EOCs available for analysis. All tumours were reevaluated by a senior pathologist (KJ) regarding histological subtype and histological grade [21]. Of these cases, 54 (58.0%) were classified as serous, 22 (24.0%) endometroid, 7 (7.5%) mucinous, 5 (5.3%) clear-cell and 5 (5.2%) undifferentiated/ adenocarcinoma NOS. No borderline tumours were included in the study, as none had been registered or denoted upon reevaluation. Because of the small number of cases in some histological subgroups, only serous and endometroid tumours were included in the analysis of histological types. Three (3,2%) cases were classified as being of high differentiation grade (1), 28 (30.1%) of intermediate grade (2), and 62 (66.7%) of low differentiation grade (3). Information regarding clinical stage was obtained from the medical charts, following the standardized WHO classification of tumour staging [21], whereas 27 (29.0%) of the tumours were classified as stage 1 or 2, and 66 (71.0%) were classified as stage 3 or 4. 2.6. Statistical methods All incident, non-epithelial ovarian cancers contributed to person-years but were not counted as cases in the analysis. Distribution of established and potential risk factors for EOC was compared between EOC cases and the rest of the study cohort. Anthropometric measurements were divided into tertiles, except for BMI, which was categorized as categories of <25 (normal weight), 25 to <30 (overweight), and 30 (obese). A Cox proportional hazards analysis was used in order to compare risk of ovarian cancer between different categories of anthropometric factors. This yielded relative risks (RR) with a 95% confidence interval. Time on study was used as the underlying time scale, defined as time from baseline to diagnosis, death or end of follow-up 31 December 2007. The proportional hazards assumption was confirmed by a log, log plot [22]. In the multivariate Cox analysis potential confounders were included, i.e. level of education (0-level college/A-level college/university), age at menarche (12/>12 to <15/15), use of OC (never/ever), parity (nulliparous/parous), menopausal status (premenopausal/perimenopausal/postmenopausal), use of HRT (no/yes) and smoking status (never/current/former). A sensitivity analysis was performed after excluding cases that were diagnosed within 1 year after baseline. All analyses were stratified for menopausal status, but due to the small number of cases in the premenopausal group, it was only possible to perform the analyses in the postmenopausal group. In addition, an interaction analysis was performed for menopausal status. All statistical analyses were conducted using SPSS version 16 (SPSS Inc., Chicago, IL, USA). A two-tailed p-value less than 0.05 was regarded as statistically significant. 3. Results 3.1. Baseline characteristics Table 1 shows the distribution of covariates and mean values for the anthropometric measures for women who developed EOC during follow-up (cases), and for those who did not (rest of cohort). Mean for weight, body fat percentage, BMI, waist and hip measures were slightly lower in cases as compared to the rest of cohort. EOC cases were slightly older, had a higher age at menarche, were more frequently nulliparous and more often postmenopausal than women in the rest of the cohort.
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
434 Table 1 Distribution of risk factors in cases and rest of cohort. Factor (number of subjects with information)
Category
EOC cases (n = 93)
Rest of cohort (n = 16,875)
a
Percent (mean and SD in italics) Age at baseline (years) Education (16,831)
Age at menarche (16,750)
Ever use of oral contraceptives (16,858) Parity (16,589) Menopausal status (16,874)
Ever use of HRT (16,827) Smoking status (16,869)
Weight (16,848) Height (16,849) Body fat percentage (16,778) Body mass index (16,848) Waist (16,842) Hip (16,841) Waist-hip ratio (16,839) a
58.4 (7.4) 72.0 3.2 24.7 15.0 49.5 35.5 53.8 46.2 18.3 78.5 19.4 8.6 72.0 76.3 23.7 40.9 32.3 26.9 67.3 (10.6) 164.3 (5.9) 30.2 (4.9) 24.9 (3.6) 76.2 (9.0) 96.8 (8.9) 0.87 (0.05)
0-Level college A-level college University 12 >12 to <15 15 Never Ever Nulliparous Parous Premenopausal Perimenopausal Postmenopausal No Yes Never Current Former kg cm % kg/m2 cm cm cm/cm
57.4 (7.9) 69.6 7.0 23.2 21.9 53.0 24.3 50.8 49.1 12.8 85.5 26.3 7.0 66.7 81.8 18.0 44.2 28.2 27.7 68.0 (11.7) 163.6 (6.1) 30.8 (5.0) 25.4 (4.2) 77.9 (10.6) 97.9 (9.6) 0.79 (0.09)
Because of some missing values, the sum may not be 100%.
and hip circumference were not statistically significantly associated with EOC risk (Tables 2a and 2b).
3.2. Overall risk of EOC A statistically significant lower overall risk of EOC was noted for women in the highest WHR tertile as compared to the first (RR = 0.60; 0.36–1.00; p-trend = 0.04). In postmenopausal women, this association was even stronger (RR = 0.47; 0.25–0.87; ptrend = 0.01). Height, weight, BMI, body fat percentage, waist
3.3. Risk of different subgroups of EOC As compared to women in the lowest tertile, women in the highest tertile of WHR had a statistically significant lower
Table 2a Relative risk of EOC subgroups (type, grade, stage) in relation to height, weight, BMI and body fat percentage. Tumour characteristic (n)
Tertile
Height (<1.60/1.61 to <1.66/1.66 m)
Weight (<62/62 to <71/ 71 kg)
BMI (<25/25 to <30/ 30 kg/m2)
Body fat% (<29/29 to <33/33%)
(n)
RR
(n)
RR
(n)
RR
(n)
RR
All (93)
1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend
26 29 38
1.00 1.04(0.61–1.76) 1.15(0.69–1.91) 0.43 1.00 1.22(0.60–2.45) 1.29(0.65–2.56) 0.58 1.00 1.31(0.41–4.15) 1.55(0.52–4.68) 0.47 1.00 0.66(0.25–1.75) 1.14(0.50–2.63) 0.83 1.00 1.21(0.63–2.32) 1.16(0.16–2.21) 0.41 1.00 0.95(0.38–2.34) 0.64(0.25–1.65) 0.45 1.00 1.18(0.62–2.24) 1.45(0.78–2.66) 0.17
29 33 31
1.00 1.03(0.62–1.70) 0.96(0.57–1.59) 0.98 1.00 0.85(0.44–1.66) 0.94(0.49–1.81) 0.89 1.00 1.47(0.49–4.40) 1.27(0.41–3.91) 0.60 1.00 1.62(0.68–3.86) 1.02(0.39–2.67) 0.91 1.00 0.81(0.44–1.51) 0.90(0.48–1.64) 0.98 1.00 1.01(0.43–2.39) 0.66(0.25–1.73) 0.98 1.00 0.94(0.52–1.71) 1.01(0.56–1.82) 0.89
51 31 11
1.00 1.01(0.65–1.60) 0.90(0.47–1.75) 0.99 1.00 0.85(0.46–1.56) 0.92(0.40–2.13) 0.95 1.00 1.04(0.42–2.57) 0.63(0.14–2.90) 0.82 1.00 0.87(0.40–1.90) 0.43(0.10–1.89) 0.40 1.00 1.05(0.60–1.85) 1.19(0.56–2.54) 0.66 1.00 1.47(0.68–3.18) 0.32(0.04–2.44) 0.65 1.00 0.84(0.49–1.46) 0.94(0.45–1.97) 0.88
31 31 31
1.00 1.01(0.61–1.67) 0.83(0.50–1.40) 0.62 1.00 1.09(0.56–2.11) 0.84(0.42–1.67) 0.83 1.00 0.39(0.12–1.26) 0.63(0.24–1.69) 0.44 1.00 0.92(0.37–2.29) 1.01(0.42–2.43) 0.75 1.00 1.06(0.58–1.94) 0.72(0.37–1.38) 0.33 1.00 1.61(0.68–3.80) 0.66(0.23–1.90) 0.52 1.00 0.84(0.46–1.54) 0.81(0.45–1.46) 0.63
Serous (54)
Endometroid (22)
Grades 1 and 2 (31)
Grade 3 (62)
Stage 1 and 2 (27)
Stage 3 and 4 (66)
14 18 22 5 7 10 10 7 14 16 22 24 9 9 9 16 21 29
18 17 19 5 9 8 8 14 9
22 19 21 10 11 6 20 22 24
31 16 7 12 8 2 19 10 2 33 20 19 15 11 1 37 20 9
17 19 18 10 4 8 10 9 12 21 23 18 9 12 6 20 22 24
Adjusted for parity, age at menarche, smoking status, ever-use of oral contraceptives, education, use of hormone replacement therapy (HRT) and menopausal status.
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
435
Table 2b Relative risk of EOC subgroups (type, grade, stage) in relation to waist, hip and waist-hip ratio (WHR). Tumour characteristic (n)
Tertile
All (93)
1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend
Serous (54)
Endometroid (22)
Grades 1 and 2 (31)
Grade 3 (62)
Stages 1 and 2 (27)
Stages 3 and 4 (66)
Waist (<72/72 to <80/ 80 cm)
Hip (<93/93 to <101/ 101 cm)
WHR (<0.77/0.77 to <0.81/ 0.81 cm/cm)
(n)
RR
(n)
RR
(n)
RR
35 30 28
1.00 0.79 (0.48–1.29) 0.67 (0.40–1.11) 0.11 1.00 0.58(0.30–1.14) 0.67(0.36–1.27) 0.20 1.00 0.44(1.15–1.35) 0.78(0.30–2.03) 0.19 1.00 0.81(0.34–1.92) 0.75(0.42–2.43) 0.41 1.00 0.60(0.32–1.13) 0.78(0.43–1.41) 0.13 1.00 0.70(0.28–1.76) 0.80(0.33–1.95) 0.35 1.00 0.79(0.44–1.40) 0.64(0.35–1.16) 0.16
32 34 27
1.00 0.90 (0.55–1.46) 0.77 (0.45–1.29) 0.87 1.00 0.76(0.39–1.47) 0.85(0.46–1.56) 0.64 1.00 0.41(0.14–1.22) 0.53(0.20–1.42) 0.13 1.00 0.84(0.36–1.95) 0.74(0.30–1.83) 0.43 1.00 0.74(0.38–1.42) 0.93(0.52–1.69) 0.51 1.00 0.72(0.27–1.94) 0.98(0.41–2.32) 0.58 1.00 0.86(0.49–1.54) 0.75(0.41–1.39) 0.42
40 30 23
1.00 0.85(0.53–1.37) 0.60(0.36–1.00) 0.04 1.00 0.75(0.40–1.41) 0.59(0.31–1.15) 0.15 1.00 0.80(0.31–2.11) 0.55(0.19–1.62) 0.28 1.00 0.89(0.42–1.91) 0.27(0.09–0.81) 0.03 1.00 0.78(0.42–1.45) 0.80(0.44–1.47) 0.31 1.00 0.82(0.37–1.86) 0.32(0.10–0.97) 0.03 1.00 0.92(0.52–1.62) 0.67(0.37–1.23) 0.19
22 14 18 9 8 5 11 10 10 24 21 17 11 9 7 24 22 20
19 17 18 10 5 7 11 11 9 21 24 17 10 11 6 22 24 20
24 16 14 10 7 5 15 12 4 25 18 19 14 9 4 26 22 18
Adjusted for parity, age at menarche, smoking status, ever-use of oral contraceptives, education, use of hormone replacement therapy (HRT) and menopausal status.
risk of grades 1 and 2 tumours, as well as tumours in lower clinical stages (1 and 2) (Table 2b). This was also seen in postmenopausal women for grades 1 and 2 tumours (RR = 0.23; 0.06–0.81; p-trend = 0.02), and for stages 1 and 2 (RR = 0.25; 0.05–1.17; p-trend = 0.05). These associations showed a dose–response relationship with significant trend over tertiles. Moreover, in postmenopausal women, a lower risk in the highest tertile of WHR was also seen in relation to stages 3 and 4 tumours (RR = 0.48; 0.23–1.00; p-trend = 0.03) and in the subgroup of serous tumours (RR = 0.35; 0.15–0.85; p-trend = 0.02). An interaction analysis of menopausal status reveals a significant interaction between WHR and risk for stages 3 and 4 tumours (p for interaction = 0.04). Notably, all these associations were based on very few EOC cases. The significance was not altered, neither related to overall risk nor to histopathological subgroups, after performing a sensitivity analysis where cases diagnosed less than 1 year after baseline were excluded (data not shown). 4. Discussion In this prospective cohort study of 17,035 women we found an association between a high waist-hip ratio and a lower overall risk for epithelial ovarian cancer, particularly tumours with differentiation grades 1 and 2, and clinical stages 1 and 2. This association was even stronger in postmenopausal women. The small number of EOC cases (n = 93) should however be taken into consideration when interpreting the results. Height is an anthropometric parameter that, in contrast to the others, is not modifiable, and does probably not interact with endogenous hormones in adulthood. Height is most likely mainly genetically determined. Our analysis did not show any association between height and risk of EOC, which corresponds well to results from some previous studies [9,24]. However, another study with
pooled data from 12 cohort studies found that height above 1.70 m significantly increased the risk of ovarian cancer [7]. It has been hypothesized that the biological mechanism mediating an association between adiposity and ovarian cancer risk is that adiposity confers increased levels of androgens and oestrogens, decreases in progesterone levels and modulation of the insulin-like growth factor (IGF) axis, resulting in proliferative and anti-apoptotic effects on ovarian epithelial tissue [3]. However, the epidemiological evidence in favour of this hypothesis is limited. Many studies indicate that obesity (BMI > 30) mediates an increased risk of ovarian cancer [6,7,9], but such an association could neither be found in our study, nor in several other studies [8,24,25]. There is less support for the hypothesis that central adiposity (measured as waist circumference or waist-hip ratio, WHR) is a risk marker for EOC. Two studies have reported significant positive associations between WHR and ovarian cancer risk [26,27], and two studies found no associations between waist circumference and risk [9,26], however in the same study a positive relationship between hip circumference and risk was reported [9]. In our study, waist- or hip circumferences were not associated with an overall risk of EOC. However a high WHR was associated with a low overall risk of EOC, and of tumours of high differentiation grade and low clinical stage. This association was more pronounced in postmenopausal women. In postmenopausal women, WHR was also associated with a slightly decreased risk of serous tumours and stages 3 and 4 tumours. The fact that a high WHR is associated with a reduced risk of EOC is in contrast to previous studies. However although no other studies have focused on anthropometry and ovarian cancer risk in relation to tumour characteristics such as differentiation grade and clinical stage, a cautionary note must be made regarding the low number of EOC cases in the analysis when interpreting these results. Since ovarian cancer is a largely heterogenous disease, it is relevant to examine potential risk factors in relation to different
436
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
histological subgroups. Our ability to examine associations for specific histological subtypes was limited because of few cases for some of the subtypes. Overall, previous findings by histological subtype have been inconsistent across individual studies [6]. A pooled analysis of 10 case–control studies from the United States found a non-significantly increased risk of endometroid and mucinous tumours associated with increasing BMI [16], conversely, the pooled cohort analysis found no association between BMI and risk of endometroid, mucinous or serous tumours [7]. Few studies have investigated clear-cell tumours separately, because they represent only 5% of malignant ovarian tumours. Relatively few studies have examined how reproductive and life style factors vary between pre- and postmenopausal ovarian cancer. This is probably due to a relatively small number of premenopausal cases in most studies, and many have not performed analyses stratified by menopausal status. However, several case–control and cohort studies suggest that certain hormone-related risk factors, including use of OC, number of pregnancies and body mass index, have stronger associations with premenopausal than postmenopausal ovarian cancer [23,28–30]. We have therefore in this analysis calculated relative risks according to menopausal status, although due to the small number of premenopausal cases, it was only meaningful to perform the analysis in the postmenopausal group. There are some methodological differences among studies focusing on anthropometric factors and ovarian cancer risk, and several types of body measurements have been used. Most commonly weight and BMI are analysed. Only a few studies have analysed waist-hip circumference and waist-hip ratio and ovarian cancer risk [9,23]. Due to the inconclusive results of most recent studies on different anthropometric variables, it is difficult to outline one of these parameters as the best predictor of ovarian cancer risk in general as well as in relation to different histopathological subgroups. Differences in results for different study designs might also be due to recall-bias in case–control studies, or whether self-reported or measured anthropometric values were used. Some categories included few cases, resulting in wide confidence intervals, relatively low statistical power and a risk of a type II error. This was a problem particularly when analysing RR in the subgroups of endometroid carcinomas, tumours with differentiation grades 1 and 2, and in clinical stages 1 and 2, and furthermore in the survival analysis that is limited by a very small number of cases in each category. It is possible that participation in the MDCS was associated with body constitution, which may have lead to potential selection bias. In a previous paper, Manjer et al. compared BMI in the MDCS in relation to the background population, and found an equal distribution of overweight/obesity [31]. Since the only symptom of EOC often consists of an enlarged abdomen due to ascites, it might also be possible with a potential detection bias due to later detection of the cancer in individuals with a high central obesity than thin people. However, our results do not indicate signs of detection bias since women with a large waist circumference did not show a positive relationship with a more advanced disease. It might be difficult to apply incidence rates from the MDCS to the background population as participants may to some degree have been selected in terms of socio-economic factors and risk of ovarian cancer. Nevertheless, we consider it possible to make internal comparisons comparing subjects with high versus low levels of the study measurements in order to obtain relative risks. Similar associations were seen when adjusting for potential confounders, i.e. parity, age at menarche, smoking habits, ever use of oral contraceptives, education, HRT use, and menopausal status.
Hence, these factors ought not to have confounded the results. However, a limitation of the study is that we had no available information on other potential confounders, such as heredity, tubal ligation, related diseases such as polycystic ovarian syndrome (PCOS) and diabetes. Another limitation of the study is the lack of updated information during follow-up on anthropometric characteristics and other factors that may influence these associations, i.e. use of exogenous hormones. Premenopausal women comprised 26% of the cohort at baseline, and some of these women would have been peri- or postmenopausal at the end of follow-up period for this analysis. Thus, changes that might have occurred during the mean 13 years of follow-up could not be accounted for in the analysis, and may therefore have influenced the risk estimates since our results indicate a stronger association in postmenopausal women, In conclusion, the present study did not show that obesity in general, or central adiposity in particular, constitutes an increased risk of developing EOC, but a high WHR was associated, with a lower overall risk of EOC. Conflict of interest The authors declare that they have no competing interests. Acknowledgements This work was supported by grants from the Swedish Cancer Society, Gunnar Nilsson´s Cancer Foundation, the Crafoord Foundation and the Research funds of Ska˚ne University Hospital, Malmo¨. References [1] Bray F, Sankila R, Ferlay J, Parkin DM. Estimates of cancer incidence and mortality in Europe in 1995. Eur J Cancer 2002;38:99–166. [2] Cannistra SA. Cancer of the ovary. N Engl J Med 2004;351:2519–29. [3] Lukanova A, Kaaks R. Endogenous hormones and ovarian cancer: epidemiology and current hypotheses. Cancer Epidemiol Biomarkers Prev 2005;14:98–107. [4] Risch HA. Hormonal etiology of epithelial ovarian cancer, with a hypothesis concerning the role of androgens and progesterone. J Natl Cancer Inst 1998;90: 1774–86. [5] Olsen CM, Green AC, Nagle CM, Jordan SJ, Whiteman DC, Bain CJ, Webb PM. Epithelial ovarian cancer: testing the ‘androgens hypothesis’. Endocr Relat Cancer 2008;15:1061–8. [6] Olsen CM, Green AC, Whiteman DC, Sadeghi S, Kolahdooz F, Webb PM. Obesity and the risk of epithelial ovarian cancer: a systematic review and metaanalysis. Eur J Cancer 2007;43:690–709. [7] Schouten LJ, Rivera C, Hunter DJ, Spiegelman D, Adami HO, Arslan A, et al. Height, body mass index, and ovarian cancer: a pooled analysis of 12 cohort studies. Cancer Epidemiol Biomarkers Prev 2008;17:902–12. [8] Beehler GP, Sekhon M, Baker JA, Teter BE, McCann SE, Rodabaugh KJ, et al. Risk of ovarian cancer associated with BMI varies by menopausal status. J Nutr 2006;136:2881–6. [9] Lahmann PH, Cust AE, Friedenreich CM, Schulz, M, Lukanova A. Anthropometric measures and epithelial ovarian cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer;126:2404–15. [10] Pischon T, Lahmann PH, Boeing H, Friedenreich C, Norat T, Tjonneland A, et al. Body size and risk of colon and rectal cancer in the European Prospective Investigation Into Cancer and Nutrition (EPIC). J Natl Cancer Inst 2006;98: 920–31. [11] Lahmann PH, Hoffmann K, Allen N, van Gils GH, Khan KT, Teherd B, et al. Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer And Nutrition (EPIC). Int J Cancer 2004;111:762–71. [12] Riman T, Dickman PW, Nilsson S, Correia N, Nordlinder H, Magnusson CM, et al. Risk factors for invasive epithelial ovarian cancer: results from a Swedish case–control study. Am J Epidemiol 2002;156:363–73. [13] Chiaffarino F, Pelucchi C, Parazzini F, Negri E, Franceschi S, Talamini R, et al. Reproductive and hormonal factors and ovarian cancer. Ann Oncol 2001;12: 337–41. [14] Risch HA, Marrett LD, Jain M, Howe GR. Differences in risk factors for epithelial ovarian cancer by histologic type. Results of a case–control study. Am J Epidemiol 1996;144:363–72. [15] Modugno F, Ness RB, Wheeler JE. Reproductive risk factors for epithelial ovarian cancer according to histologic type and invasiveness. Ann Epidemiol 2001;11:568–74.
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437 [16] Kurian AW, Balise RR, McGuire V, Whittemore AS. Histologic types of epithelial ovarian cancer: have they different risk factors? Gynecol Oncol 2005;96:520–30. [17] Nagle CM, Bain CJ, Green AC, Webb PM. The influence of reproductive and hormonal factors on ovarian cancer survival. Int J Gynecol Cancer 2008;18: 407–13. [18] Yang L, Klint A, Lambe M, Bellocco R, Riman T, Bergfeldt K, et al. Predictors of ovarian cancer survival: a population-based prospective study in Sweden. Int J Cancer 2008;123:672–9. [19] Borgquist S, Anagnostaki L, Jirstrom K, Manjer J, Landberg G. Breast tumours following combined hormone replacement therapy express favourable prognostic factors. Int J Cancer 2007;120:2202–7. [20] Steen B, Bosaeus I, Elmstahl S, Galvard H, Isaksson B, Robertsson E. Body composition in the elderly estimated with an electrical impedance method. Compr Gerontol A 1987;1:102–5. [21] WHO classification of tumours—pathology and genetics of tumours of the breast and female organs. Lyon: IARC Press, 2003. [22] Katz MH, Hauck WW. Proportional hazards (Cox) regression. J Gen Intern Med 1993;8:702–11. [23] Kuper H, Cramer DW, Titus-Ernstoff L. Risk of ovarian cancer in the United States in relation to anthropometric measures: does the association depend on menopausal status? Cancer Causes Control 2002;13:455–63.
437
[24] Lacey Jr JV, Leitzmann M, Brinton LA, Lubin JH, Sherman ME, Schatzkin A, et al. Weight, height, and body mass index and risk for ovarian cancer in a cohort study. Ann Epidemiol 2006;16:869–76. [25] Fairfield KM, Willett WC, Rosner BA, et al. Obesity, weight gain, and ovarian cancer. Obstet Gynecol 2002;100:288–96. [26] Anderson JP, Ross JA, Folsom AR. Anthropometric variables, physical activity, and incidence of ovarian cancer: The Iowa Women’s Health Study. Cancer 2004;100:1515–21. [27] Lukanova A, Toniolo P, Lundin E, et al. Body mass index in relation to ovarian cancer: a multi-centre nested case–control study. Int J Cancer 2002;99:603–8. [28] Folsom AR, Kushi LH, Anderson KE, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women’s Health Study. Arch Intern Med 2000;160:2117–28. [29] Dal Maso L, Franceschi S, Negri E, et al. Body size indices at different ages and epithelial ovarian cancer risk. Eur J Cancer 2002;38:1769–74. [30] Tung KH, Wilkens LR, Wu AH, et al. Effect of anovulation factors on pre- and postmenopausal ovarian cancer risk: revisiting the incessant ovulation hypothesis. Am J Epidemiol 2005;161:321–9. [31] Manjer J, Carlsson S, Elmstahl S, et al. The Malmo Diet and Cancer Study: representativity, cancer incidence and mortality in participants and nonparticipants. Eur J Cancer Prev 2001;10:489–99.
Contents lists available at ScienceDirect
Cancer Epidemiology The International Journal of Cancer Epidemiology, Detection, and Prevention journal homepage: www.cancerepidemiology.net
Anthropometric factors and ovarian cancer risk in the Malmo¨ Diet and Cancer Study Jenny Bra¨ndstedt a,b,*, Bjo¨rn Nodin a, Jonas Manjer b,c, Karin Jirstro¨m a a
Center for Molecular Pathology, Department of Laboratory Medicine, Lund University, Ska˚ne University Hospital, Malmo¨, Sweden Division of Surgery, Department of Clinical Sciences, Lund University, Ska˚ne University Hospital, Malmo¨, Sweden c The Malmo¨ Diet and Cancer Study, Lund University, Malmo¨, Sweden b
A R T I C L E I N F O
A B S T R A C T
Article history: Accepted 1 January 2011 Available online 1 February 2011
Objective: To examine the associations of measured anthropometric factors, including general and central adiposity, with epithelial ovarian cancer (EOC) risk in the Malmo¨ Diet and Cancer Study. Methods: In 93 incident EOC cases from a Swedish population-based prospective cohort study, seven anthropometric factors; height, weight, BMI, body fat percentage, waist- and hip circumference, and waist-hip ratio (WHR), were categorized by tertiles of baseline anthropometric measurements and relative risks were calculated using multivariate Cox regression models. Results: A high WHR (<0.77, 0.77 to <0.81, 0.81 cm/cm) was associated with a statistically significantly lower overall risk for EOC (RR 0.60; 0.36–1.00; p-trend = 0.04), particularly tumours of differentiation grades 1 and 2 (RR 0.27; 0.09–0.81; p-trend = 0.03) and clinical stages 1 and 2 (RR 0.32; 0.10–0.97; p-trend = 0.03) and these associations were stronger in postmenopausal women. Neither height, weight, BMI, body fat percentage, waist- or hip circumference were associated with overall risk, nor with risk for different subtypes, differentiation grade or stage. Conclusions: These results demonstrate that a high WHR is associated with a decreased risk of EOC. Other anthropometric factors were not associated with EOC risk. ß 2011 Elsevier Ltd. All rights reserved.
Keywords: Anthropometry Ovarian cancer risk Histological subtype Differentiation grade Clinical stage
1. Introduction Ovarian cancer is the leading cause of death from gynaecological malignancy and the fifth most common cause of cancerrelated death in women in Western Europe and North America [1]. The poor survival in ovarian cancer is related to the high percentage of cases that are diagnosed at an advanced stage [2]. Thus, it is important to identify potentially modifiable risk factors for prevention of ovarian cancer. Reproductive factors such as nulliparity and non-use of oral contraceptive (OC) are well established risk factors of ovarian cancer. Since central (abdominal) adiposity is associated with a series of hormonal and metabolic changes it is thought to potentially influence ovarian cancer risk [3–5]. The association between obesity and ovarian cancer risk has been investigated in several studies, but the results have been inconclusive [6]. Findings from a pooled analysis of 12 cohort studies indicated that obesity was associated with an increased risk of ovarian cancer in premenopausal women, but not
* Corresponding author at: Center for Molecular Pathology, Department of Laboratory Medicine, Lund University, Ska˚ne University Hospital, SE-205 02 Malmo¨, Sweden. Tel.: +46 40 331402; fax: +46 40 337322. E-mail address: [email protected] (J. Bra¨ndstedt). 1877-7821/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.canep.2011.01.003
in postmenopausal women [7,8]. Contrary to this, a recent large prospective cohort study showed a positive association between obesity and risk of ovarian cancer in postmenopausal women, but not in premenopausal women [9]. The contradictory results between studies might partly be due to the use of different definitions of overweight and obesity, usually studied as weight or body mass index (BMI). It is, however, also possible that other anthropometric factors such as height or central adiposity may be even more important in relation to cancer risk [7,10,11]. Given that different histological subgroups of EOC have distinct morphological features and clinical behaviour, it could be hypothesized that they may also have different aetiologies. Most epidemiologic studies have treated ovarian cancer as one single disease, including those confirming established risk factors such as nulliparity and non-use of OC [4,12,13]. However, only a few epidemiological studies have investigated the relationship between anthropometric factors and specific histological subtypes of EOC, and their findings have been inconclusive [14–16]. In addition, we are not aware of any studies investigating whether risk factors differ according to the degree of aggressiveness of the tumours, here studied as differention grade and tumour stage. Furthermore, little is known about anthropometric factors and EOC prognosis [17,18].
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
The primary aim of this study was to examine the relationship between anthropometric factors and risk of EOC in the Malmo¨ Diet and Cancer Study. The secondary aim was aim to explore a potential association with clinicopathological parameters, e.g. stage, histological grade and histological subtype, which, to our knowledge, has not been done before. 2. Materials and methods 2.1. The Malmo¨ Diet and Cancer Study The Malmo¨ Diet and Cancer Study (MDCS) is a populationbased prospective cohort study which enrolled 17,035 women during baseline examinations between 1991 and 1996. Data on lifestyle, health and socio-demographic characteristics were collected via standardized questionnaires that included menstrual and reproductive history; level of education, age at menarche, parity, exposure to OC, menopausal status, use of hormonal replacement therapy (HRT), and smoking habits. Menopausal status was defined using information from the questionnaire and a record linkage with the local hospital registry concerning previous gynaecological surgery. This has previously been described in detail [19]. Ethical permissions for the MDCS (LU 90-51), and the present study (LU 530/2008), were obtained from the Ethical Committee at Lund University. 2.2. Anthropometric measurements At baseline examination, weight (multiples of 0.1 kg) and height (to the nearest 0.005 m) were measured by a trained nurse, and body mass index (BMI) was calculated as kg/m2. Waist circumference was measured at the midpoint between the lower ribs and the iliac crest, and for hip circumference the level of greatest lateral extension was used. These measurements were estimated to the nearest 0.01 m. The waist and hip circumferences of each participant were used to calculate waist-hip ratio (WHR; cm/cm) as an additional measure of fat distribution. Body composition was estimated using a single frequency bioimpedance methodology (BIA 103, RLJ-systems, Detroit, MI, USA) with tetra polar electrode placement and subjects in a supine position. Lean body mass and fat mass were determined and served to calculate body fat percentage. The BIA method has previously been validated in Swedish middle-aged and elderly adults [20]. 2.3. Follow-up for cancer and cause-of-death Incident cases of ovarian cancer in the MDCS were identified through the Swedish Cancer Registry and vital status was determined by record linkage with the Swedish Cause-of-Death Registry. End of follow-up was 31 December 2007. Cause of death in EOC cases was retrieved from medical charts and the Swedish Cause-of-Death Registry. Women were followed from the date of enrolment until ovarian cancer diagnosis, death, emigration or end of follow-up. Median time from baseline until diagnosis was 7.3 (SD: 4.3) years and the median follow-up time in the entire cohort was 13.1 (SD: 2.5) years. 2.4. Study population Among the 17,035 women in the cohort, there were a total number of 168 cases of malignant ovarian cancer. Among these, 67 were diagnosed before baseline examination, i.e. prevalent cancers, and they were therefore excluded, resulting in a study population of 16,968 women. Prevalent cancers of other sites were not excluded from the study.
433
2.5. Tumour characteristics Of the 101 incident tumours, 8 tumours were non-epithelial, resulting in 93 EOCs available for analysis. All tumours were reevaluated by a senior pathologist (KJ) regarding histological subtype and histological grade [21]. Of these cases, 54 (58.0%) were classified as serous, 22 (24.0%) endometroid, 7 (7.5%) mucinous, 5 (5.3%) clear-cell and 5 (5.2%) undifferentiated/ adenocarcinoma NOS. No borderline tumours were included in the study, as none had been registered or denoted upon reevaluation. Because of the small number of cases in some histological subgroups, only serous and endometroid tumours were included in the analysis of histological types. Three (3,2%) cases were classified as being of high differentiation grade (1), 28 (30.1%) of intermediate grade (2), and 62 (66.7%) of low differentiation grade (3). Information regarding clinical stage was obtained from the medical charts, following the standardized WHO classification of tumour staging [21], whereas 27 (29.0%) of the tumours were classified as stage 1 or 2, and 66 (71.0%) were classified as stage 3 or 4. 2.6. Statistical methods All incident, non-epithelial ovarian cancers contributed to person-years but were not counted as cases in the analysis. Distribution of established and potential risk factors for EOC was compared between EOC cases and the rest of the study cohort. Anthropometric measurements were divided into tertiles, except for BMI, which was categorized as categories of <25 (normal weight), 25 to <30 (overweight), and 30 (obese). A Cox proportional hazards analysis was used in order to compare risk of ovarian cancer between different categories of anthropometric factors. This yielded relative risks (RR) with a 95% confidence interval. Time on study was used as the underlying time scale, defined as time from baseline to diagnosis, death or end of follow-up 31 December 2007. The proportional hazards assumption was confirmed by a log, log plot [22]. In the multivariate Cox analysis potential confounders were included, i.e. level of education (0-level college/A-level college/university), age at menarche (12/>12 to <15/15), use of OC (never/ever), parity (nulliparous/parous), menopausal status (premenopausal/perimenopausal/postmenopausal), use of HRT (no/yes) and smoking status (never/current/former). A sensitivity analysis was performed after excluding cases that were diagnosed within 1 year after baseline. All analyses were stratified for menopausal status, but due to the small number of cases in the premenopausal group, it was only possible to perform the analyses in the postmenopausal group. In addition, an interaction analysis was performed for menopausal status. All statistical analyses were conducted using SPSS version 16 (SPSS Inc., Chicago, IL, USA). A two-tailed p-value less than 0.05 was regarded as statistically significant. 3. Results 3.1. Baseline characteristics Table 1 shows the distribution of covariates and mean values for the anthropometric measures for women who developed EOC during follow-up (cases), and for those who did not (rest of cohort). Mean for weight, body fat percentage, BMI, waist and hip measures were slightly lower in cases as compared to the rest of cohort. EOC cases were slightly older, had a higher age at menarche, were more frequently nulliparous and more often postmenopausal than women in the rest of the cohort.
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
434 Table 1 Distribution of risk factors in cases and rest of cohort. Factor (number of subjects with information)
Category
EOC cases (n = 93)
Rest of cohort (n = 16,875)
a
Percent (mean and SD in italics) Age at baseline (years) Education (16,831)
Age at menarche (16,750)
Ever use of oral contraceptives (16,858) Parity (16,589) Menopausal status (16,874)
Ever use of HRT (16,827) Smoking status (16,869)
Weight (16,848) Height (16,849) Body fat percentage (16,778) Body mass index (16,848) Waist (16,842) Hip (16,841) Waist-hip ratio (16,839) a
58.4 (7.4) 72.0 3.2 24.7 15.0 49.5 35.5 53.8 46.2 18.3 78.5 19.4 8.6 72.0 76.3 23.7 40.9 32.3 26.9 67.3 (10.6) 164.3 (5.9) 30.2 (4.9) 24.9 (3.6) 76.2 (9.0) 96.8 (8.9) 0.87 (0.05)
0-Level college A-level college University 12 >12 to <15 15 Never Ever Nulliparous Parous Premenopausal Perimenopausal Postmenopausal No Yes Never Current Former kg cm % kg/m2 cm cm cm/cm
57.4 (7.9) 69.6 7.0 23.2 21.9 53.0 24.3 50.8 49.1 12.8 85.5 26.3 7.0 66.7 81.8 18.0 44.2 28.2 27.7 68.0 (11.7) 163.6 (6.1) 30.8 (5.0) 25.4 (4.2) 77.9 (10.6) 97.9 (9.6) 0.79 (0.09)
Because of some missing values, the sum may not be 100%.
and hip circumference were not statistically significantly associated with EOC risk (Tables 2a and 2b).
3.2. Overall risk of EOC A statistically significant lower overall risk of EOC was noted for women in the highest WHR tertile as compared to the first (RR = 0.60; 0.36–1.00; p-trend = 0.04). In postmenopausal women, this association was even stronger (RR = 0.47; 0.25–0.87; ptrend = 0.01). Height, weight, BMI, body fat percentage, waist
3.3. Risk of different subgroups of EOC As compared to women in the lowest tertile, women in the highest tertile of WHR had a statistically significant lower
Table 2a Relative risk of EOC subgroups (type, grade, stage) in relation to height, weight, BMI and body fat percentage. Tumour characteristic (n)
Tertile
Height (<1.60/1.61 to <1.66/1.66 m)
Weight (<62/62 to <71/ 71 kg)
BMI (<25/25 to <30/ 30 kg/m2)
Body fat% (<29/29 to <33/33%)
(n)
RR
(n)
RR
(n)
RR
(n)
RR
All (93)
1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend
26 29 38
1.00 1.04(0.61–1.76) 1.15(0.69–1.91) 0.43 1.00 1.22(0.60–2.45) 1.29(0.65–2.56) 0.58 1.00 1.31(0.41–4.15) 1.55(0.52–4.68) 0.47 1.00 0.66(0.25–1.75) 1.14(0.50–2.63) 0.83 1.00 1.21(0.63–2.32) 1.16(0.16–2.21) 0.41 1.00 0.95(0.38–2.34) 0.64(0.25–1.65) 0.45 1.00 1.18(0.62–2.24) 1.45(0.78–2.66) 0.17
29 33 31
1.00 1.03(0.62–1.70) 0.96(0.57–1.59) 0.98 1.00 0.85(0.44–1.66) 0.94(0.49–1.81) 0.89 1.00 1.47(0.49–4.40) 1.27(0.41–3.91) 0.60 1.00 1.62(0.68–3.86) 1.02(0.39–2.67) 0.91 1.00 0.81(0.44–1.51) 0.90(0.48–1.64) 0.98 1.00 1.01(0.43–2.39) 0.66(0.25–1.73) 0.98 1.00 0.94(0.52–1.71) 1.01(0.56–1.82) 0.89
51 31 11
1.00 1.01(0.65–1.60) 0.90(0.47–1.75) 0.99 1.00 0.85(0.46–1.56) 0.92(0.40–2.13) 0.95 1.00 1.04(0.42–2.57) 0.63(0.14–2.90) 0.82 1.00 0.87(0.40–1.90) 0.43(0.10–1.89) 0.40 1.00 1.05(0.60–1.85) 1.19(0.56–2.54) 0.66 1.00 1.47(0.68–3.18) 0.32(0.04–2.44) 0.65 1.00 0.84(0.49–1.46) 0.94(0.45–1.97) 0.88
31 31 31
1.00 1.01(0.61–1.67) 0.83(0.50–1.40) 0.62 1.00 1.09(0.56–2.11) 0.84(0.42–1.67) 0.83 1.00 0.39(0.12–1.26) 0.63(0.24–1.69) 0.44 1.00 0.92(0.37–2.29) 1.01(0.42–2.43) 0.75 1.00 1.06(0.58–1.94) 0.72(0.37–1.38) 0.33 1.00 1.61(0.68–3.80) 0.66(0.23–1.90) 0.52 1.00 0.84(0.46–1.54) 0.81(0.45–1.46) 0.63
Serous (54)
Endometroid (22)
Grades 1 and 2 (31)
Grade 3 (62)
Stage 1 and 2 (27)
Stage 3 and 4 (66)
14 18 22 5 7 10 10 7 14 16 22 24 9 9 9 16 21 29
18 17 19 5 9 8 8 14 9
22 19 21 10 11 6 20 22 24
31 16 7 12 8 2 19 10 2 33 20 19 15 11 1 37 20 9
17 19 18 10 4 8 10 9 12 21 23 18 9 12 6 20 22 24
Adjusted for parity, age at menarche, smoking status, ever-use of oral contraceptives, education, use of hormone replacement therapy (HRT) and menopausal status.
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
435
Table 2b Relative risk of EOC subgroups (type, grade, stage) in relation to waist, hip and waist-hip ratio (WHR). Tumour characteristic (n)
Tertile
All (93)
1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend 1 2 3 p-Trend
Serous (54)
Endometroid (22)
Grades 1 and 2 (31)
Grade 3 (62)
Stages 1 and 2 (27)
Stages 3 and 4 (66)
Waist (<72/72 to <80/ 80 cm)
Hip (<93/93 to <101/ 101 cm)
WHR (<0.77/0.77 to <0.81/ 0.81 cm/cm)
(n)
RR
(n)
RR
(n)
RR
35 30 28
1.00 0.79 (0.48–1.29) 0.67 (0.40–1.11) 0.11 1.00 0.58(0.30–1.14) 0.67(0.36–1.27) 0.20 1.00 0.44(1.15–1.35) 0.78(0.30–2.03) 0.19 1.00 0.81(0.34–1.92) 0.75(0.42–2.43) 0.41 1.00 0.60(0.32–1.13) 0.78(0.43–1.41) 0.13 1.00 0.70(0.28–1.76) 0.80(0.33–1.95) 0.35 1.00 0.79(0.44–1.40) 0.64(0.35–1.16) 0.16
32 34 27
1.00 0.90 (0.55–1.46) 0.77 (0.45–1.29) 0.87 1.00 0.76(0.39–1.47) 0.85(0.46–1.56) 0.64 1.00 0.41(0.14–1.22) 0.53(0.20–1.42) 0.13 1.00 0.84(0.36–1.95) 0.74(0.30–1.83) 0.43 1.00 0.74(0.38–1.42) 0.93(0.52–1.69) 0.51 1.00 0.72(0.27–1.94) 0.98(0.41–2.32) 0.58 1.00 0.86(0.49–1.54) 0.75(0.41–1.39) 0.42
40 30 23
1.00 0.85(0.53–1.37) 0.60(0.36–1.00) 0.04 1.00 0.75(0.40–1.41) 0.59(0.31–1.15) 0.15 1.00 0.80(0.31–2.11) 0.55(0.19–1.62) 0.28 1.00 0.89(0.42–1.91) 0.27(0.09–0.81) 0.03 1.00 0.78(0.42–1.45) 0.80(0.44–1.47) 0.31 1.00 0.82(0.37–1.86) 0.32(0.10–0.97) 0.03 1.00 0.92(0.52–1.62) 0.67(0.37–1.23) 0.19
22 14 18 9 8 5 11 10 10 24 21 17 11 9 7 24 22 20
19 17 18 10 5 7 11 11 9 21 24 17 10 11 6 22 24 20
24 16 14 10 7 5 15 12 4 25 18 19 14 9 4 26 22 18
Adjusted for parity, age at menarche, smoking status, ever-use of oral contraceptives, education, use of hormone replacement therapy (HRT) and menopausal status.
risk of grades 1 and 2 tumours, as well as tumours in lower clinical stages (1 and 2) (Table 2b). This was also seen in postmenopausal women for grades 1 and 2 tumours (RR = 0.23; 0.06–0.81; p-trend = 0.02), and for stages 1 and 2 (RR = 0.25; 0.05–1.17; p-trend = 0.05). These associations showed a dose–response relationship with significant trend over tertiles. Moreover, in postmenopausal women, a lower risk in the highest tertile of WHR was also seen in relation to stages 3 and 4 tumours (RR = 0.48; 0.23–1.00; p-trend = 0.03) and in the subgroup of serous tumours (RR = 0.35; 0.15–0.85; p-trend = 0.02). An interaction analysis of menopausal status reveals a significant interaction between WHR and risk for stages 3 and 4 tumours (p for interaction = 0.04). Notably, all these associations were based on very few EOC cases. The significance was not altered, neither related to overall risk nor to histopathological subgroups, after performing a sensitivity analysis where cases diagnosed less than 1 year after baseline were excluded (data not shown). 4. Discussion In this prospective cohort study of 17,035 women we found an association between a high waist-hip ratio and a lower overall risk for epithelial ovarian cancer, particularly tumours with differentiation grades 1 and 2, and clinical stages 1 and 2. This association was even stronger in postmenopausal women. The small number of EOC cases (n = 93) should however be taken into consideration when interpreting the results. Height is an anthropometric parameter that, in contrast to the others, is not modifiable, and does probably not interact with endogenous hormones in adulthood. Height is most likely mainly genetically determined. Our analysis did not show any association between height and risk of EOC, which corresponds well to results from some previous studies [9,24]. However, another study with
pooled data from 12 cohort studies found that height above 1.70 m significantly increased the risk of ovarian cancer [7]. It has been hypothesized that the biological mechanism mediating an association between adiposity and ovarian cancer risk is that adiposity confers increased levels of androgens and oestrogens, decreases in progesterone levels and modulation of the insulin-like growth factor (IGF) axis, resulting in proliferative and anti-apoptotic effects on ovarian epithelial tissue [3]. However, the epidemiological evidence in favour of this hypothesis is limited. Many studies indicate that obesity (BMI > 30) mediates an increased risk of ovarian cancer [6,7,9], but such an association could neither be found in our study, nor in several other studies [8,24,25]. There is less support for the hypothesis that central adiposity (measured as waist circumference or waist-hip ratio, WHR) is a risk marker for EOC. Two studies have reported significant positive associations between WHR and ovarian cancer risk [26,27], and two studies found no associations between waist circumference and risk [9,26], however in the same study a positive relationship between hip circumference and risk was reported [9]. In our study, waist- or hip circumferences were not associated with an overall risk of EOC. However a high WHR was associated with a low overall risk of EOC, and of tumours of high differentiation grade and low clinical stage. This association was more pronounced in postmenopausal women. In postmenopausal women, WHR was also associated with a slightly decreased risk of serous tumours and stages 3 and 4 tumours. The fact that a high WHR is associated with a reduced risk of EOC is in contrast to previous studies. However although no other studies have focused on anthropometry and ovarian cancer risk in relation to tumour characteristics such as differentiation grade and clinical stage, a cautionary note must be made regarding the low number of EOC cases in the analysis when interpreting these results. Since ovarian cancer is a largely heterogenous disease, it is relevant to examine potential risk factors in relation to different
436
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437
histological subgroups. Our ability to examine associations for specific histological subtypes was limited because of few cases for some of the subtypes. Overall, previous findings by histological subtype have been inconsistent across individual studies [6]. A pooled analysis of 10 case–control studies from the United States found a non-significantly increased risk of endometroid and mucinous tumours associated with increasing BMI [16], conversely, the pooled cohort analysis found no association between BMI and risk of endometroid, mucinous or serous tumours [7]. Few studies have investigated clear-cell tumours separately, because they represent only 5% of malignant ovarian tumours. Relatively few studies have examined how reproductive and life style factors vary between pre- and postmenopausal ovarian cancer. This is probably due to a relatively small number of premenopausal cases in most studies, and many have not performed analyses stratified by menopausal status. However, several case–control and cohort studies suggest that certain hormone-related risk factors, including use of OC, number of pregnancies and body mass index, have stronger associations with premenopausal than postmenopausal ovarian cancer [23,28–30]. We have therefore in this analysis calculated relative risks according to menopausal status, although due to the small number of premenopausal cases, it was only meaningful to perform the analysis in the postmenopausal group. There are some methodological differences among studies focusing on anthropometric factors and ovarian cancer risk, and several types of body measurements have been used. Most commonly weight and BMI are analysed. Only a few studies have analysed waist-hip circumference and waist-hip ratio and ovarian cancer risk [9,23]. Due to the inconclusive results of most recent studies on different anthropometric variables, it is difficult to outline one of these parameters as the best predictor of ovarian cancer risk in general as well as in relation to different histopathological subgroups. Differences in results for different study designs might also be due to recall-bias in case–control studies, or whether self-reported or measured anthropometric values were used. Some categories included few cases, resulting in wide confidence intervals, relatively low statistical power and a risk of a type II error. This was a problem particularly when analysing RR in the subgroups of endometroid carcinomas, tumours with differentiation grades 1 and 2, and in clinical stages 1 and 2, and furthermore in the survival analysis that is limited by a very small number of cases in each category. It is possible that participation in the MDCS was associated with body constitution, which may have lead to potential selection bias. In a previous paper, Manjer et al. compared BMI in the MDCS in relation to the background population, and found an equal distribution of overweight/obesity [31]. Since the only symptom of EOC often consists of an enlarged abdomen due to ascites, it might also be possible with a potential detection bias due to later detection of the cancer in individuals with a high central obesity than thin people. However, our results do not indicate signs of detection bias since women with a large waist circumference did not show a positive relationship with a more advanced disease. It might be difficult to apply incidence rates from the MDCS to the background population as participants may to some degree have been selected in terms of socio-economic factors and risk of ovarian cancer. Nevertheless, we consider it possible to make internal comparisons comparing subjects with high versus low levels of the study measurements in order to obtain relative risks. Similar associations were seen when adjusting for potential confounders, i.e. parity, age at menarche, smoking habits, ever use of oral contraceptives, education, HRT use, and menopausal status.
Hence, these factors ought not to have confounded the results. However, a limitation of the study is that we had no available information on other potential confounders, such as heredity, tubal ligation, related diseases such as polycystic ovarian syndrome (PCOS) and diabetes. Another limitation of the study is the lack of updated information during follow-up on anthropometric characteristics and other factors that may influence these associations, i.e. use of exogenous hormones. Premenopausal women comprised 26% of the cohort at baseline, and some of these women would have been peri- or postmenopausal at the end of follow-up period for this analysis. Thus, changes that might have occurred during the mean 13 years of follow-up could not be accounted for in the analysis, and may therefore have influenced the risk estimates since our results indicate a stronger association in postmenopausal women, In conclusion, the present study did not show that obesity in general, or central adiposity in particular, constitutes an increased risk of developing EOC, but a high WHR was associated, with a lower overall risk of EOC. Conflict of interest The authors declare that they have no competing interests. Acknowledgements This work was supported by grants from the Swedish Cancer Society, Gunnar Nilsson´s Cancer Foundation, the Crafoord Foundation and the Research funds of Ska˚ne University Hospital, Malmo¨. References [1] Bray F, Sankila R, Ferlay J, Parkin DM. Estimates of cancer incidence and mortality in Europe in 1995. Eur J Cancer 2002;38:99–166. [2] Cannistra SA. Cancer of the ovary. N Engl J Med 2004;351:2519–29. [3] Lukanova A, Kaaks R. Endogenous hormones and ovarian cancer: epidemiology and current hypotheses. Cancer Epidemiol Biomarkers Prev 2005;14:98–107. [4] Risch HA. Hormonal etiology of epithelial ovarian cancer, with a hypothesis concerning the role of androgens and progesterone. J Natl Cancer Inst 1998;90: 1774–86. [5] Olsen CM, Green AC, Nagle CM, Jordan SJ, Whiteman DC, Bain CJ, Webb PM. Epithelial ovarian cancer: testing the ‘androgens hypothesis’. Endocr Relat Cancer 2008;15:1061–8. [6] Olsen CM, Green AC, Whiteman DC, Sadeghi S, Kolahdooz F, Webb PM. Obesity and the risk of epithelial ovarian cancer: a systematic review and metaanalysis. Eur J Cancer 2007;43:690–709. [7] Schouten LJ, Rivera C, Hunter DJ, Spiegelman D, Adami HO, Arslan A, et al. Height, body mass index, and ovarian cancer: a pooled analysis of 12 cohort studies. Cancer Epidemiol Biomarkers Prev 2008;17:902–12. [8] Beehler GP, Sekhon M, Baker JA, Teter BE, McCann SE, Rodabaugh KJ, et al. Risk of ovarian cancer associated with BMI varies by menopausal status. J Nutr 2006;136:2881–6. [9] Lahmann PH, Cust AE, Friedenreich CM, Schulz, M, Lukanova A. Anthropometric measures and epithelial ovarian cancer risk in the European Prospective Investigation into Cancer and Nutrition. Int J Cancer;126:2404–15. [10] Pischon T, Lahmann PH, Boeing H, Friedenreich C, Norat T, Tjonneland A, et al. Body size and risk of colon and rectal cancer in the European Prospective Investigation Into Cancer and Nutrition (EPIC). J Natl Cancer Inst 2006;98: 920–31. [11] Lahmann PH, Hoffmann K, Allen N, van Gils GH, Khan KT, Teherd B, et al. Body size and breast cancer risk: findings from the European Prospective Investigation into Cancer And Nutrition (EPIC). Int J Cancer 2004;111:762–71. [12] Riman T, Dickman PW, Nilsson S, Correia N, Nordlinder H, Magnusson CM, et al. Risk factors for invasive epithelial ovarian cancer: results from a Swedish case–control study. Am J Epidemiol 2002;156:363–73. [13] Chiaffarino F, Pelucchi C, Parazzini F, Negri E, Franceschi S, Talamini R, et al. Reproductive and hormonal factors and ovarian cancer. Ann Oncol 2001;12: 337–41. [14] Risch HA, Marrett LD, Jain M, Howe GR. Differences in risk factors for epithelial ovarian cancer by histologic type. Results of a case–control study. Am J Epidemiol 1996;144:363–72. [15] Modugno F, Ness RB, Wheeler JE. Reproductive risk factors for epithelial ovarian cancer according to histologic type and invasiveness. Ann Epidemiol 2001;11:568–74.
J. Bra¨ndstedt et al. / Cancer Epidemiology 35 (2011) 432–437 [16] Kurian AW, Balise RR, McGuire V, Whittemore AS. Histologic types of epithelial ovarian cancer: have they different risk factors? Gynecol Oncol 2005;96:520–30. [17] Nagle CM, Bain CJ, Green AC, Webb PM. The influence of reproductive and hormonal factors on ovarian cancer survival. Int J Gynecol Cancer 2008;18: 407–13. [18] Yang L, Klint A, Lambe M, Bellocco R, Riman T, Bergfeldt K, et al. Predictors of ovarian cancer survival: a population-based prospective study in Sweden. Int J Cancer 2008;123:672–9. [19] Borgquist S, Anagnostaki L, Jirstrom K, Manjer J, Landberg G. Breast tumours following combined hormone replacement therapy express favourable prognostic factors. Int J Cancer 2007;120:2202–7. [20] Steen B, Bosaeus I, Elmstahl S, Galvard H, Isaksson B, Robertsson E. Body composition in the elderly estimated with an electrical impedance method. Compr Gerontol A 1987;1:102–5. [21] WHO classification of tumours—pathology and genetics of tumours of the breast and female organs. Lyon: IARC Press, 2003. [22] Katz MH, Hauck WW. Proportional hazards (Cox) regression. J Gen Intern Med 1993;8:702–11. [23] Kuper H, Cramer DW, Titus-Ernstoff L. Risk of ovarian cancer in the United States in relation to anthropometric measures: does the association depend on menopausal status? Cancer Causes Control 2002;13:455–63.
437
[24] Lacey Jr JV, Leitzmann M, Brinton LA, Lubin JH, Sherman ME, Schatzkin A, et al. Weight, height, and body mass index and risk for ovarian cancer in a cohort study. Ann Epidemiol 2006;16:869–76. [25] Fairfield KM, Willett WC, Rosner BA, et al. Obesity, weight gain, and ovarian cancer. Obstet Gynecol 2002;100:288–96. [26] Anderson JP, Ross JA, Folsom AR. Anthropometric variables, physical activity, and incidence of ovarian cancer: The Iowa Women’s Health Study. Cancer 2004;100:1515–21. [27] Lukanova A, Toniolo P, Lundin E, et al. Body mass index in relation to ovarian cancer: a multi-centre nested case–control study. Int J Cancer 2002;99:603–8. [28] Folsom AR, Kushi LH, Anderson KE, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women’s Health Study. Arch Intern Med 2000;160:2117–28. [29] Dal Maso L, Franceschi S, Negri E, et al. Body size indices at different ages and epithelial ovarian cancer risk. Eur J Cancer 2002;38:1769–74. [30] Tung KH, Wilkens LR, Wu AH, et al. Effect of anovulation factors on pre- and postmenopausal ovarian cancer risk: revisiting the incessant ovulation hypothesis. Am J Epidemiol 2005;161:321–9. [31] Manjer J, Carlsson S, Elmstahl S, et al. The Malmo Diet and Cancer Study: representativity, cancer incidence and mortality in participants and nonparticipants. Eur J Cancer Prev 2001;10:489–99.