Alcohol consumption, cigarette smoking, and endometrial cancer risk: results from the Netherlands Cohort Study

Objective To examine the association between alcohol consumption, cigarette smoking, and endometrial cancer.
Introduction
Endometrial cancer is the most frequently diagnosed gynecologic cancer in Europe [1]. The development of endometrial cancer has been related to exposure to estrogens unopposed by progestagens [2]. Many studies have shown a positive association between alcohol ingestion and estrogen levels in postmenopausal women. For instance, cross-sectional data from the European Prospective Investigation into Cancer and Nutrition (EPIC) suggested that elevated blood levels of estrone are observed with increasing alcohol consumption in postmenopausal women [3]. Thus, alcohol could be expected to increase endometrial cancer risk by elevating estrogen levels. An important determinant of estrogen levels in women is use of unopposed hormone replacement therapy (HRT). Accordingly, use of unopposed HRT is consistently associated with an increased risk of endometrial cancer [4–6]. Furthermore, it has been suggested that alcohol consumption increases estradiol levels in particular in postmenopausal women who are on HRT [7, 8]. However, previous studies have indicated that alcohol consumption is either weakly or not associated with a reduced risk of endometrial cancer and no significant interaction with use of HRT has been found [9]. Although several studies have analyzed the association between alcohol consumption and endometrial cancer risk [10–22], only few studies have examined the risk associated with various measures of alcohol consumption (e.g., amount and type of alcohol) including only one comprehensive prospective cohort study [18].
In contrast to alcohol, smoking has been hypothesized to exert anti-estrogenic effects [23] and to lower the risk of endometrial cancer in this way. Also, an effect modification by use of HRT seems reasonable [24]. Furthermore, it has been suggested that body mass index (BMI) and age at menopause might mediate part of the inverse association between smoking and endometrial cancer [24]. Earlier prospective studies [22, 25–29] have generally suggested that smoking is associated with a slight to moderate protection against endometrial cancer. However, to date, only the largest and most recent prospective cohort study has reported a significantly reduced risk in both current and past smokers [29]. Moreover, this large study explored the relationship between HRT and smoking and found no effect-modification, and also found that the association between smoking and endometrial cancer was not confounded by alcohol use [29]. The evidence from other epidemiological studies with regard to an effect-modification by use of HRT is ambiguous [24].
As many case–control [24], but only a few cohort studies have reported on the association between smoking and endometrial cancer [22, 25–29], the International Agency for Research on Cancer concludes that prospective cohort studies, in which selection and recall bias are minimized, are scarce [30]. Only two cohort studies have explored the association between smoking and endometrial cancer comprehensively by examining the risk associated with all common quantitative smoking measures (e.g., smoking duration, time since cessation) [28, 29].
Since only few prospective cohort studies have investigated the association between alcohol consumption, cigarette smoking, and endometrial cancer comprehensively, important features of this relationship are under-explored. Hence, we aim to provide additional evidence based on prospective data. Moreover, we intend to elucidate the hormonal mechanisms underlying endometrial carcinogenesis by investigating, first, whether BMI and age at menopause might act as intermediary variables in the association between smoking and endometrial cancer and by examining, second, whether there is evidence regarding a potential effect modification by HRT use.
Materials and methods
The Netherlands Cohort Study (NLCS) started in September 1986 when 62,573 women aged 55–69 years were enrolled in the cohort. Ethical approval was obtained from the ethics committee of the University Hospital Maastricht. All women were presumed to be postmenopausal. At baseline, data on dietary habits and other risk factors (such as alcohol consumption, smoking history, reproductive history, and anthropometry) were collected by means of a self-administered questionnaire. Data analysis was conducted according to the case–cohort approach. In this approach, cases are derived from the cohort (providing numerator information for the incidence rates), while the accumulated person-years at risk of the cohort are estimated from a random sample from the cohort, i.e., the subcohort (providing denominator information for the incidence rates). Following this approach, a subcohort of 2,589 women was sampled after the baseline exposure measurement. The subcohort has been followed up biennially by mail for vital status information. The vital status of subcohort members, who did not respond was completed by contacting the municipal population registers. Incident cases occurring in the entire cohort were detected by annual record linkages to the Netherlands Cancer Registry and the nationwide network and registry of histopathology and cytopathology in the Netherlands (PALGA). Further details on the design of the study and methods of follow-up have been presented elsewhere [31, 32].
The present analysis is restricted to cancer incidence in the 11.3-year follow-up period from September 1986 to December 1997. The completeness of cancer follow up was estimated to be at least 96% [33], and no subcohort members were lost to follow up.
Three hundred and twenty-seven incident, microscopically confirmed, invasive, primary endometrial carcinomas were detected after a follow-up period of 11.3 years. Cases were excluded from analysis if they had been diagnosed with non-epithelial tumors (n = 12), and if information on either alcohol consumption or cigarette smoking was incomplete (n = 35).
Women were eligible for the subcohort if they did not report at baseline that they had undergone hysterectomy. Application of this inclusion criterion yielded a subcohort of 2,229 members. Individuals were excluded from the analysis if they had been diagnosed with cancer other than skin cancer at baseline (n = 151) and if information on either alcohol consumption or cigarette smoking was missing (n = 177). After these exclusions, 280 cases and 1,901 subcohort members remained available for analysis.
Questionnaire data
Consumption of alcoholic beverages during the year preceding the baseline interview was assessed by consumption frequency questions on beer, red wine, white wine, sherry, other fortified wine, liqueur, and liquor. Categories ranked from ‘never’ to ‘6–7 times per week’ and information on the number of glasses per consumption day was also requested. Questionnaire data of all cases and subcohort members were key entered twice and processed in a manner blinded with regard to case/subcohort status in order to minimize observer bias in the coding and interpretation of data. The questionnaire has been validated against a nine-day diet record [34, 35]. The Pearson correlation coefficient between the mean daily ethanol intake assessed by the questionnaire and that estimated by the nine-day record was 0.86 for all subjects and 0.78 for users of alcoholic beverages [34]. Respondents that reported to drink alcohol less than once per month were considered non-drinkers. Four items from the questionnaire (red wine, white wine, sherry, and liqueur) were combined into one single wine variable since these items were highly correlated and separate analysis would have resulted in small numbers of subjects within each stratum. Mean daily alcohol consumption was calculated using the Dutch food composition table [36]. Based on data from a pilot study, standard glasses were defined as follows: 200 ml for beer, 105 ml for wine, 80 ml for sherry, and 45 ml for both liqueur and liquor, corresponding to 8 g, 10 g, 11 g, 7 g, and 13 g of alcohol, respectively.
Smoking was addressed at baseline by questions on age at first exposure to smoking, age at last exposure to smoking, smoking frequency, and smoking duration of cigarette, cigar, and pipe smokers. As the vast majority of smoking subcohort members was cigarette smokers, analyses were restricted to that particular group. Based on the questionnaire data, the following cigarette smoking variables were constructed: cigarette smoking status (never versus ever and never versus former or current), frequency (number of cigarettes per day), duration (years), age at first exposure (years), and time since cessation (years). Time since cessation was calculated as ‘age at baseline’ minus ‘age at smoking cessation’.
Concerning the use of HRT, women were asked whether they had ever used HRT because of complaints related to the menopause. We can assume that all members of our cohort were postmenopausal in 1986, when the baseline questionnaires were completed, and that possible treatment with HRT took place prior to 1986 in most women. Based on information regarding HRT prescription in the Netherlands in the past [37–39], we assume that all HRT users enrolled in the NLCS were treated with unopposed oral estrogens.
Data analysis
A variable was considered a confounder if (I) it was associated with endometrial cancer risk, if (II) it was associated with alcohol consumption or cigarette smoking, and if (III) age-adjusted hazard ratios changed by more than 10% after adjustment for the potentially confounding factor. Based on the literature [40] and previous analyses, we considered the following variables as potential confounders: age (continuous), age at menarche (continuous), use of oral contraceptives (ever versus never), duration of oral contraceptive use (continuous), age at first child birth (continuous), parity (continuous), age at menopause (continuous), use of postmenopausal hormones (ever versus never), duration of postmenopausal hormone use (continuous), non-occupational physical activity (categorized), BMI (continuous), height (continuous), energy intake (continuous), total fat intake (continuous), intake of saturated fat (continuous), intake of carbohydrates (continuous), intake of dietary fiber (continuous), intake of vegetables (continuous), intake of fruits (continuous), coffee consumption (yes versus no), education (categorized), diagnosis of hypertension (yes versus no), diagnosis of diabetes mellitus (yes versus no), family history of endometrial cancer (yes versus no), and if applicable: total alcohol consumption per day (continuous), type of alcoholic beverage (categorized), current smoking (yes versus no), number of cigarettes smoked per day (continuous), and duration of smoking (continuous).
Incidence rate ratios (RR) and corresponding 95 percent confidence intervals (95% CI) for endometrial cancer were estimated in the age-adjusted and multivariate case–cohort analyses with categorized and continuous alcohol and cigarette smoking variables, using the Cox proportional hazards model [41] processed with the Stata statistical software package [42]. Standard errors were estimated using the robust Hubert–White sandwich estimator to account for additional variance introduced by sampling from the cohort. This method is equivalent to the variance–covariance estimator by Barlow [43]. The proportional hazards assumption was tested using the scaled Schoenfeld residuals [44]. Tests for dose-response trends in risk of endometrial cancer were assessed by fitting ordinal exposure variables as continuous terms. Tests for interaction were performed by using the Wald test. Two-sided p values are reported throughout the paper.
Results
The percentage of women reporting alcohol consumption was similar among cases and subcohort members (67.5% and 66.9%, respectively), as was the mean alcohol consumption per day among users in both groups (7.7 g with standard deviation (sd) = 10.8, and 8.5 g (sd = 10.4), respectively). Current smoking was less prevalent among cases than among subcohort members (15.4% vs. 21.8%), but the number of cigarettes smoked per day did not differ considerably between smokers in both groups (13.6 (sd = 8.4) and 13.2 (sd = 8.1), respectively).
Drinkers reported a slightly higher age at menopause and a higher prevalence of both oral contraceptive use and current cigarette smoking than non-drinkers (see Table 1). With regard to smoking status, former and current smokers were slightly leaner and had fewer children than never-smokers. On average, current smokers reported having reached menopause 1 year earlier than former-smokers and never-smokers. The prevalence of both oral contraceptive use and alcohol use was higher among smokers than among never-smokers. Also, average alcohol consumption was approximately twice as high among smokers as among never-smokers (see Table 1).
Table 1Means (standard deviation) and distribution (n) of potential confounders according to alcohol consumption and cigarette smoking status among subcohort members, the Netherlands Cohort Study (1986–1995)
CharacteristicUnitAlcohol consumption statusCigarette smoking statusNo (n = 630)Yes (n = 1,271)Never (n = 1,100)Former (n = 387)Current (n = 414)Mean (sd)Mean (sd)Mean (sd)Mean (sd)Mean (sd)AgeYears61.8 (4.3)61.4 (4.3)62.0 (4.3)61.1 (4.4)60.7 (4.1)Body Mass Indexkg/m225.4 (3.9)24.9 (3.4)25.3 (3.5)24.7 (3.3)24.6 (3.8)ParityNumber of children2.8 (2.4)2.7 (2.2)3.0 (2.4)2.5 (1.8)2.5 (2.1)Age at 1st child birthYears22.0 (11.2)21.9 (11.3)22.2 (11.2)22.3 (11.3)20.8 (11.3)Age at menopauseYears48.3 (4.8)49.2 (4.3)49.1 (4.4)49.2 (4.1)48.1 (4.8)Total energy intake (including alcohol)kcal1,628 (415)1,724 (386)1,694 (393)1,676 (406)1,705 (406)Alcohol consumption G/day0 (0)8.5 (10.4)5.7 (7.8)10.4 (11.2)12.5 (12.8)CigarettesNo./day13.0 (8.8)11.1 (8.1)0 (0)9.8 (8.0)13.2 (8.1)n (%)an (%)an (%)an (%)an (%)aOral contraceptive useEver124 (20.0)336 (26.7)223 (20.6)127 (32.9)110 (26.8)Physical activity>30 min/day425 (69.1)975 (77.8)796 (74.1)306 (79.5)298 (72.9)Diagnosis of hypertensionYes198 (31.4)351 (27.6)339 (30.8)107 (27.7)103 (24.9)Diagnosis of diabetesYes33 (5.2)40 (3.2)48 (4.4)13 (3.4)12 (2.9)Hormone replacement therapyEver62 (10.0)152 (12.1)107 (9.9)58 (15.1)49 (12.0)Family history of endometrial cancerYes20 (3.2)32 (2.5)23 (2.1)16 (4.1)13 (3.1)Alcohol usersYes––663 (60.3)312 (80.6)296 (71.5)Currently smoking cigarettesYes118 (18.7)296 (23.3)–––a The percentage reported for some variables does sometimes not correspond with the numbers per smoker stratum since part of the information for these variables was missing
Based on the literature and based on the methodological criteria specified above, we found the following confounders: age, BMI, parity, oral contraceptive use, non-occupational physical activity, hypertension, age at first child birth, and age at menopause. Alcohol consumption and cigarette smoking status were found to confound each other’s association with endometrial cancer. We controlled for all these confounders in multivariate analyses. In additional analyses, we mutually controlled the age-adjusted risk estimates regarding qualitative smoking measures for the other smoking measures.
The multivariate risk estimates did not change substantially when oral contraceptive use (ever/never) was replaced by duration of oral contraceptive use (data not shown). Accordingly, we considered it sufficient to control only for oral contraceptive use (ever/never).
Table 2 shows the results for the association between alcohol consumption and endometrial cancer. The multivariate adjusted RR associated with alcohol consumption was 1.06 (95% CI = 0.78–1.43). The multivariate RRs of endometrial cancer for women who consumed up to 4, 5–14, 15–29, and 30 or more gram of alcohol per day versus non-drinkers were 1.09 (95% CI = 0.78–1.52), 0.95 (95% CI = 0.62–1.45), 0.94 (95% CI = 0.52–1.69), and 1.78 (95% CI = 0.88–3.60), respectively. No significant trend was observed (ptrend = 0.62).
Table 2Rate ratios of endometrial cancer according to baseline alcohol consumption in the Netherlands Cohort Study, 1986–1997Alcohol consumption (g/ day)Age adjustedMultivariate adjustedCategorical medianCasesPerson-years in subcohortRR (95% CI)aCasesPerson-years in subcohortRR (95% CI)bTotal alcohol    No0916,6411 (ref.)825,8371 (ref.)    Yes4.018913,7461.01 (0.77–1.32)17212,1371.06 (0.78–1.43)    0.1–41.61147,5991.10 (0.82–1.48)1056,6431.09 (0.78–1.52)    5–149.1473,6400.94 (0.65–1.37)393,2790.95 (0.62–1.45)    15–2920.9171,8220.69 (0.40–1.18)171,5750.94 (0.52–1.69)    ≥3037.3116841.20 (0.62–2.34)116391.78 (0.88–3.60)    p trend0.490.62Alcohol from wine    Yes3.218213,0091.06 (0.81–1.37)16611,5071.13 (0.84–1.52)    0.1–41.51258,0721.17 (0.88–1.55)1127,1131.16 (0.84–1.59)    5–148.9383,1460.91 (0.61–1.36)352,7841.07 (0.68–1.67)    ≥1521.8191,7910.80 (0.48–1.35)191,6111.11 (0.64–1.93)    p trend0.430.64Alcohol from beer    Yes1.14291,8731.15 (0.76–1.74)261,6291.30 (0.82–2.07)Alcohol from liquor    Yes3.7342,6480.93 (0.64–1.37)312,3491.11 (0.73–1.68)aRR = rate ratios; CI = confidence interval; n.a. = not applicablebRate ratios adjusted for age (years), body mass index (kg/m2), parity (number of children), use of oral contraceptives (ever versus never), non-occupational physical activity (low, moderate, active, very active), hypertension (yes versus no), age at first child birth (years), age at menopause (years), and current cigarette smoking (yes versus no)
The multivariate RR of endometrial cancer for wine-consumers versus non-consumers was slightly, but non-significantly, elevated (RR = 1.13, 95% CI = 0.84–1.52). The RRs were also slightly, but non-significantly elevated when calculated according to different levels of wine consumption and no significant trend was observed (ptrend = 0.64). The multivariate RR of endometrial cancer associated with drinking beer equaled 1.30 (95% CI = 0.82–2.07) and the RR for drinking liquor was 1.11 (95% CI = 0.73–1.68).
Regarding smoking, age-adjusted analysis revealed an inverse association between ever-smoking and endometrial cancer risk (RR = 0.70, 95% CI = 0.54–0.92, see Table 3). We observed significant inverse trends of endometrial cancer risk with all quantitative smoking measures. However, these trends became non-significant when never-smokers were excluded. Some of these age-adjusted risk estimates changed substantially after additional adjustment for current smoking status, smoking frequency and smoking duration (see Table 3).
Table 3Rate ratios of endometrial cancer according to baseline cigarette smoking features in the Netherlands Cohort Study, 1986–1997Cigarette smoking featuresCategorical medianAdjusted for ageAdjusted for age, current smoking status, frequency and duration of smokingAdjusted for all confoundersCasesPerson-years in subcohortRR (95% CI)aCasesPerson-years in subcohortRR (95% CI)bCasesPerson-years in subcohortRR (95% CI)cSmoking status    Neverdn.a.18711,8721 (ref.)–––16910,3301 (ref.)    Ever smokers938,5160.70 (0.54–0.92)–––857,6440.71 (0.53–0.95)    Neverd18711,8721 (ref.)–––16910,3301 (ref.)    Former smokersn.a504,1810.77 (0.55–1.07)–––473,7570.83 (0.58–1.20)    Current smokersn.a434,3340.64 (0.45–0.91)–––383,8880.59 (0.40–0.88)    p trend0.010.01Frequency (cigarettes/day)    Neverd018711,8721 (ref.)18711,8721 (ref.)16910,3301 (ref.)    0.1–9 4383,7190.65 (0.45–0.95)373,5690.86 (0.49–1.50)333,2231.07 (0.58–1.98)    10–19 12282,4380.74 (0.48–1.14)272,4151.02 (0.54–1.92)252,2141.28 (0.66–2.46)    20+20222,0140.70 (0.44–1.12)222,0141.03 (0.46–2.29)211,7981.31 (0.56–3.03)    p trend0.040.750.43Duration (years)    Neverd018711,8721 (ref.)18711,8721 (ref.)16910,3301.0 (ref.)    0.1–19 10.5232,0090.74 (0.47–1.17)221,9910.71 (0.42–1.18)211,8480.77 (0.43–1.39)    20–3930524,2740.79 (0.57–1.10)514,1200.80 (0.47–1.34)473,6900.89 (0.51–1.56)    40+411319700.42 (0.23–0.75131,8870.44 (0.19–1.02)111,6970.37 (0.15–0.90)    p trend0.000.100.13Age at first exposure (years)    Neverdn.a18711,8721 (ref.)18711,8721 (ref.)16910,3301 (ref.)    <1917343,0980.71 (0.48–1.05)322,9470.78 (0.34–1.80)282,7240.91 (0.37–2.23)    19–2420212,5240.53 (0.33–0.86)202,3710.60 (0.29–1.27)202,1020.88 (0.38–2.02)    25+30312,6910.73 (0.49–1.09)312,5960.82 (0.47–1.46)282,3251.00 (0.53–1.87)    p trend0.010.470.94Time since cessation (years)    Neverdn.a18711,8721 (ref.)18711,8721 (ref.)16910,3301 (ref.)    20+e26.5201,1421.11 (0.68–1.84)171,0410.80 (0.41–1.57)179431.12 (0.52–2.41)    10–19e14141,4590.62 (0.35–1.09)141,4160.38 (0.14–1.03)131,2700.50 (0.17–1.45)    0.1–9e5131,3920.60 (0.33–1.08)131,3470.32 (0.10–1.06)121,2570.50 (0.12–2.00)    p trend0.030.060.26aRR = rate ratios; CI = confidence interval; n.a. = not applicablebRRs were adjusted for age (years). In addition, frequency was adjusted for current smoking status (yes/no) and duration (years); duration was adjusted for current smoking status and frequency (cigarettes/day); age at first exposure was adjusted for current smoking status, frequency, and duration; time since cessation was adjusted for frequency and durationcRRs were adjusted as mentioned under footnote b and additionally for body mass index (kg/m2), parity (number of children), use of oral contraceptives (ever versus never), non-occupational physical activity (low, moderate, active, very active), hypertension (yes versus no), age at first child birth (years), age at menopause (years), and alcohol consumption (gram/ day)dNever smoked cigars, pipe, or cigaretteseEx-smokers only
When we adjusted for all confounders, multivariate analysis showed a statistically significant 29% reduced risk of endometrial cancer for ever-smokers when contrasted with never-smokers. When considered separately, the risk reduction appeared to be stronger among current smokers (RR = 0.59, 95% CI = 0.40–0.88) than among former smokers (RR = 0.83, 95% CI = 0.58–1.20). Tests for trends were not significant for any of the quantitative smoking variables when these were adjusted for age and additional confounders.
The strongest reduction in risk, which could be observed in both univariate and multivariate models, was associated with a smoking history of 40 or more years compared with having never smoked (RR = 0.37, 95% CI = 0.15–0.90). Moreover, we observed a non-significant 50% reduction in risk in women that quit smoking either nine or less years ago or that quit 10–19 years ago. The data indicated no association between age at first smoking exposure and endometrial cancer.
Omitting age at menopause and BMI from the multivariate models either separately or simultaneously did not cause meaningful changes in the corresponding estimates. No interactions in determining endometrial cancer risk could be observed between alcohol consumption and HRT use (p = 0.43), BMI (p = 0.38), age at menopause (p = 0.39), or current cigarette smoking (p = 0.83). When we stratified multivariate alcohol analyses according to smoking status, we observed a non-significantly lower risk of endometrial cancer among alcohol consumers that have ever smoked (RR = 0.91, 95% CI = 0.52–1.58) than among alcohol consumers that have never smoked (RR = 1.16, 95% CI = 0.81–1.67). Concerning smoking and HRT use, the interaction term was not statistically significant (p = 0.11). In this subset analysis, current smoking was associated with a reduced risk of endometrial cancer in women not using HRT (RR = 0.54, 95% CI = 0.35–0.84). In current smokers that did use HRT, the RR was 1.32 (95% CI = 0.57–3.04). Numbers were very small however: only 28 women were current smokers and used HRT and only eight women were current smokers and did not use HRT.
Discussion
Our results do not suggest a meaningful association between alcohol consumption and endometrial cancer risk. Current smoking is associated with a reduced risk of endometrial cancer. This inverse relationship is neither mediated by BMI nor by age at menopause.
Regarding the biological mechanism underlying endometrial carcinogenesis, the so-called “unopposed estrogen hypothesis” is widely accepted [2]. According to this hypothesis endometrial cancer develops when the endometrium is exposed to high levels of unopposed endogenous or exogenous estrogens for a long period of time. This exposure results in elevated mitotic proliferation of endometrial cells which, in turn, increases the risk of DNA replication errors and DNA mutations which can lead to endometrial cancer [2].
Although female alcohol consumers could be expected to be at increased risk of endometrial cancer due to their hormonal profile, we have not detected significant associations between alcohol consumption and endometrial cancer risk. This finding is consistent with the vast majority of previous studies [13, 15–18]. However, in the EPIC study, significantly elevated blood estrone levels were found only in postmenopausal women who consumed more than 25 g of alcohol per day compared to non-drinkers [3]. Thus, possibly, a marked increase in estrone concentrations, and ultimately in endometrial cancer risk, can only be observed in women who consume more than moderate amounts. This notion might be supported by our data as we have observed an (non-significantly) elevated risk of endometrial cancer in women who reported to drink more than 30 g of alcohol per day. Based on literature reviews [7, 8], we hypothesized that we might find a positive association between alcohol consumption and endometrial cancer risk in particular among HRT users. However, our findings do not support the hypothesis of an effect-modification by HRT use; neither did most of the previous epidemiological studies [13, 17, 18].
Concerning smoking, an anti-estrogenic effect has been suggested [23], which should lower the risk of endometrial cancer according to the unopposed estrogen hypothesis. Accordingly, our data indicated a significant risk reduction in current smokers, just like the Nurses’ Health Study did, which has reported a RR of 0.72 (95% CI = 0.57–0.90) for current smokers [29]. In contrast, other cohort studies have observed non-significant associations [25, 26, 28]. We found that the inverse association between smoking and endometrial cancer was more pronounced among current smokers than among former smokers. These findings are in line with the evidence from several case–control [17, 45–47] and two cohort studies [26, 28] and they suggest that the degree of protection might partly depend on the time since smoking cessation.
Although not statistically significant, our prospective data support this notion, because we observed that endometrial cancer risk is possibly higher in women that quit smoking 20 or more years ago compared to the risk in women that quit 19 years ago or less. In the epidemiological literature, a few studies presented data on time since cessation [17, 28, 29, 48–50], but only one of them found a significantly lowered risk among women that quit smoking less than 10 years ago [48].
Considering the risk associated with duration of smoking, we have observed a reduction in risk with a long smoking history, just like previous studies [17, 29, 47, 48].
With regard to smoking intensity, we have found that smoking 20 or more cigarettes per day is associated with a non-significantly elevated risk of endometrial cancer. Though one needs to bear in mind that the corresponding confidence intervals were large, these point estimates are possibly not in line with the majority of previous studies, which generally indicated that a high smoking intensity (e.g., smoking 20 or more cigarettes per day) is associated with a decreased risk of endometrial cancer [17, 22, 28, 29, 45, 47, 48, 51]. This inconsistency could be explained by the lack of adjustment for other smoking variables in earlier studies: only one [29] out of six [22, 25–29] prospective cohort studies has adjusted its smoking frequency estimates for the potentially confounding effects of smoking duration. When we omitted smoking duration from our multivariate models, the RRs for smoking frequency also suggested an inverse association with endometrial cancer (data not shown).
Our results do not indicate any important association between the age at starting smoking and endometrial cancer risk. Four previous studies [26, 28, 47, 49] found a (non-significant) risk reduction with young age at first exposure, that is, starting to smoke between age 15 [28] or age 20 or earlier [49], but none of these studies has reported a significant trend.
It has been hypothesized that smoking might lower the levels of estrogens partly by reducing the amount of fat tissue or by decreasing the age at menopause [24]. In our cohort, smokers were slightly leaner than non-smokers (24.6 kg/m2 vs. 25.3 kg/m2). Moreover, on average, current smokers appeared to have reached menopause 1 year earlier than never-smokers and former smokers (48.1 years vs. 49.1 years). However, in conclusion, our analyses indicated that BMI and age at menopause are no mediating factors.
In contrast to smoking, use of unopposed HRT increases endometrial cancer risk in postmenopausal women. Although an interaction between smoking and HRT use seems biologically plausible, the small numbers in our analysis did not allow to draw firm conclusions regarding a possible effect modification by HRT use. Moreover, we had no precise information on what type of HRT women in the NLCS have used. If we could have included such information in our analysis, results might differ according to type of HRT used.
Another potential drawback of our study might be misclassification of self-reported alcohol consumption and/or self-reported cigarette smoking. However, these misclassifications might be non-differential owing to the prospective study design. Consequently, the risk estimates would probably be biased towards no effect. Moreover, the correlation between the alcohol consumption measured by the NLCS questionnaire and the measurement in a nine-day record was high due to the large variation in alcohol consumption [34].
An important strength of our study is that the exposures were assessed prior to the diagnosis of endometrial cancer. Therefore, our findings cannot be influenced by recall bias. Moreover, selection bias is unlikely as the follow-up of subcohort members and cases was almost complete [33, 52]. Another strength is the way alcohol consumption and cigarette smoking was assessed in the NLCS. The detailed assessment enabled us to evaluate associations between endometrial cancer and various measures of both exposures. Furthermore, we were able to control for confounding by the most important risk factors of endometrial cancer [40].
To sum up our major findings, we found that alcohol consumption is not associated with endometrial cancer. Current smoking was associated with a reduced risk of endometrial cancer in postmenopausal women. This association was probably not mediated by a decreased BMI or by an earlier age at menopause. Larger prospective studies with information on the type of HRT are needed in order to investigate possible effect modification by different types of HRT.
Possibly, the incidence of endometrial cancer could be reduced if smoking was more common in female populations; however, such a reduction would be overshadowed by a dramatically increasing incidence of many other chronic diseases. Thus, individuals should still be encouraged to quit or not to start smoking.

smoking
netherlands
cohort studies
alcohol drinking
endometrial neoplasms


Decomposition cross-correlation for analysis of collagen matrix deformation by single smooth muscle cells

Microvascular remodeling is known to depend on cellular interactions with matrix tissue. However, it is difficult to study the role of specific cells or matrix elements in an in vivo setting. The aim of this study is to develop an automated technique that can be employed to obtain and analyze local collagen matrix remodeling by single smooth muscle cells. We combined a motorized microscopic setup and time-lapse video microscopy with a new cross-correlation based image analysis algorithm to enable automated recording of cell-induced matrix reorganization. This method rendered 60–90 single cell studies per experiment, for which collagen deformation over time could be automatically derived. Thus, the current setup offers a tool to systematically study different components active in matrix remodeling.
Introduction
The extracellular matrix (ECM) provides a biophysical and biochemical environment for cell mechanical behavior. In turn, cellular interactions with the ECM resulting from adhesive, proteolytic and migratory activity govern continuous matrix reorganization. One such example occurs in eutrophic inward remodeling of small arteries [34]. Here, the existing collagen matrix is rearranged around a smaller diameter. Such inward remodeling is a hallmark of many hypertensive disorders and has been shown to have predictive value for cardiovascular events [9].
In vitro setups of cell-seeded matrix scaffolds allow studying the specific components that are active in vascular remodeling [22]. Especially, matrix reorganization can be monitored in the presence of specific cells, matrix elements, and blockers or markers of remodeling enzymes such as the matrix metalloproteases and transglutaminases [1, 2]. Several approaches using multi-cellular preparations have been developed. Thus, free-floating collagen gels can be seeded with contractile cells, and the remodeling is monitored from the dimensions of these gels [8, 13, 16, 20]. In the so-called culture force monitor, collagen gels mixed with cells are allowed to polymerize between a fixed plate and a force transducer. Contractile force development is measured during subsequent culture of this preparation [12, 14, 22, 36]. However, interpretation of data derived from these multi-cellular approaches is troubled by variability in cellular mechanical activity, lack of information on cell density, and synergistic contractile effects [30]. Therefore, microscopic observations on matrix reorganization by single cells may provide more detailed and fundamental information on the mechanisms of remodeling.
Several groups have studied the effect of single cell activity on local deformation of matrices [5, 28]. The most frequently used substrates are silicone rubber [3, 6, 18, 21] and polyacrylamide sheets. Local deformation is then monitored by tracing the texture, sometimes facilitated by using fluorescent beads or imprinted micropatterns. Although these inert scaffolds provide a geometrical framework to study processes like cell locomotion, they do not resemble the physiological ECM. This has been solved in some cases by coating the polyacrylamide membrane with a thin layer of collagen [11, 17, 26, 27, 37]. However, remaining issues are the orientation of the collagen fibrils in the coating as compared to native collagen, and the inability of the cells to chemically modify (e.g. by cross-linking) the artificial sheets.
A possible way to overcome these issues is the use of collagen-based matrices. Cell-induced deformation patterns of these matrices and the degree of reversibility of such deformation provide information on physical remodeling and the underlying biochemical processes. Deformations have been assessed by manually tracing beads or landmarks in consecutive images [6, 28, 30]. However, so far, only a few algorithms were developed for automatic detection of substrate deformations. In particular, very few of such algorithms allow tracing in the absence of embedded beads [23, 33, 35], and more stable algorithms are required.
While single cell observations provide a more fundamental insight into matrix remodeling than macroscopic studies, a concern is efficiency. Time-lapsed video microscopy of single cells forms a labor-intensive and time-consuming experiment. Meaningful experiments require the comparison of large data sets, including different cell types, various matrix compositions, or the use of biochemical or molecular interventions. A fully automated, motorized microscopy setup is required that scans series of individual cells and their surrounding matrix. In addition, each individual cell should automatically be kept in focus during remodeling experiments, which take up 24 h or more. Finally, stable algorithms are needed for the automated analysis of geometrical reorganization with minimal user input.
The aim of this study is to develop an automated technique that can be employed to obtain and analyze local matrix remodeling by individual cells. The system that we present allows for monitoring of ∼75 cells in parallel, using time-lapse video microscopy and computer-controlled stage positioning. In addition, we present and evaluate a new algorithm for automated detection of collagen matrix deformation around these cells.
Methods
Cell culturing and collagen matrix preparation
Smooth muscle cells, obtained from mesenteric small arteries, were cultured in Leibovitz medium with 10% (v/v) heat-inactivated fetal calf serum. Cells from passages three to nine were used in experiments.
Matrix constructs were produced from calf skin collagen (MP Biomedicals) at a concentration of 1 mg/ml; pH was buffered by HEPES, and a mix of antibiotics (PSF and ciproxin) was added. Immediately after preparation at 4°C, the collagen mixture was poured into a 3.8 cm2 culture well and a 1.5-h polymerization period at 37°C was allowed. Then, SMCs were seeded at a concentration of about 1 cell per mm2 in the presence of 1 ml serum-free Leibovitz medium. The cells were maintained in an incubation chamber that was set to a temperature of 37°C throughout the experimental procedure. After a stabilization period of about 1 h, cell–matrix interactions were monitored by microscopic imaging for a period of 24 h of spontaneous cell contraction.
Automated microscopic imaging
In order to enable simultaneous monitoring of cell-induced matrix remodeling at multiple locations, microscopy was combined with a motorized stage. Individual cells and their surrounding matrix were studied by phase-contrast microscopy (Olympus IMT-2 with 10× objective and 2.5× projection lens). Images were captured by a Qimaging Retiga SRV camera. The calibration factor for these images (1,392 × 1,040 pixels) was 0.88 μm/pixel. The microscopic field of view was set by a motorized stage, controlled by custom written software (Matlab 7.0 with Image Acquisition Toolbox 2.0). After manually determining and storing a set of x, y, z-coordinates for about 60–90 appropriate cells, these positions were tracked through time at a 15-min interval and images were captured by an automated procedure.
During the time-lapsed image acquisition, samples were kept in focus by means of implementation into the acquisition software of one of the general auto-focus algorithms. Image contrast is optimal when a histogram of intensity values shows a broad distribution over all bins. This characteristic feature can be approximated by the standard deviation of pixel intensity values. For each image, contrast was enhanced by histogram equalization, and standard deviation was calculated. A normalized focus index (FI) was defined by dividing the standard deviation of the original image (SDoriginal) by the standard deviation of the contrast enhanced image (SDenhanced): For each cell, a series of images was captured at five different heights. The image was then defined to be in focus at the vertical level of maximal normalized focus. This height (z) was used as the central level when capturing the z-series in the next time step, thus allowing gradual vertical shift during the time-lapsed image acquisition. Finally, a stack of time-lapsed images was constructed for each x, y-position and analyzed off-line.
Gel dynamics analysis
Cell–matrix interactions were quantified offline using in-house designed, automated image analysis software (Matlab 7.0 with Image Processing Toolbox 4.2). Matrix reorganization was assessed by calculation of the displacement field around a cell. This was achieved by performing a cross-correlation between each two successive images in an image stack; resolution of the displacement field was refined by correlation of subimages of decreasing size. This procedure is explained below and illustrated in Fig. 1.
Fig. 1Graphical representation of parameters used in matrix deformation analysis. The image shows a single SMC in the center, surrounded with a collagen matrix of relatively smooth texture. The white box (768 × 768 pixels) indicates the area for the most coarse correlation analysis. This correlation between two successive images was applied over an area as indicated by the yellow box (960 × 960 pixels). The green boxes show the more refined cross-correlation windows (stages 2–5). Table 1 indicates the area expansion used for these correlation analyses
First, gross displacement was defined at the point of maximal correlation between two parent images I
1 (at t = t
0) and I
2 (at t = t
0 + Δt), using a correlation threshold of 0.5 and a maximum tested displacement of 11 pixels. Then, I
1 was decomposed into four equal-sized square subimages, for which another cross-correlation was performed. The position of the correlation subwindow of I
2 was refined using the displacement as calculated for the parent image, with a safety measure to prevent I
2 crossing the borders of I
1. This procedure of accurately positioning a correlation window not only reduced the possibility of an accidental cross-correlation match between a subimage of I
1 and any random subimage of I
2, but also drastically reduced calculation time. Resolution of the displacement field was refined to the fifth decomposition stage in our experiments (see Fig. 1), using maximum tested displacements (n
cc) as indicated in Table 1. When insufficient correlation (r < 0.5) was found in any small area, displacement field from the next larger images were used. The calculated displacements were then assigned to the corresponding subimage centers. Subsequently, a continuous displacement field was obtained by bicubic interpolation. Finally, matrix compaction for each stack was quantified by tracking circular areas centered around the cell (see Fig. 5).
Table 1Settings for matrix compaction analysis by decomposition cross-correlationDecomposition stageCross-correlation windowSearch area expansion (n
cc)1768 × 768962384 × 384483192 × 19224496 × 9624
5
48 × 48
24
624 × 2412712 × 126Dimensions of the cross-correlation window I
1 were reduced by 50% for each decomposition stage. The search area of I
2 for correlation with I
1 was obtained by increasing the cross-correlation window with n
cc pixels on all image sides. The settings which were used for gel dynamics analysis and validation are given in bold
Validation
The method described above (decomposition CC) was validated on several test series against a straightforward cross-correlation analysis (direct CC), with settings according to decomposition stage 1.
The first test case consisted of an image of a collagen-embedded cell, which was artificially resized by 3%, thereby simulating matrix compaction. Secondly, increasing amounts of white noise were added to the resized image in order to test the stability of both correlation methods. Relative dispersion (RD), which is defined as standard deviation divided by mean, was used as a noise level index. Finally, image resizing was followed by a horizontal translation of 60 pixels for a low and high noise example (see Table 2).
Table 2Characteristics of validation images: CC was tested by addition of Gaussian white noise with mean 0.0 and increasing variance levels, in several cases the image was resized or translatedIndexScaling (%)Gaussian white noise varianceRD imageRD increase (%)RD noiseHorizontal shift (pixels)a10.00000.2500b0.970.00000.2510.50c0.970.00050.2604.10.1040d0.970.00100.2697.60.1460e0.970.00200.28514.10.2070f0.970.00300.30120.20.2530g0.970.00400.31626.30.2930h0.970.00500.32931.70.3270i0.970.00050.2594.10.10860j0.970.00300.30320.20.26560Relative dispersion (RD) in an image is defined as standard deviation divided by mean, RD increase is expressed as RD value compared to case “a”, RD noise is calculated as ratio of noise variance and image mean
Results
Parallel recording of matrix compaction movies
We were able to record on average 66 movies on collagen compaction by single cells in parallel at a time resolution of 15 min (n = 10 experiments). Critical issues that limited this number were the need to avoid rapid acceleration and deceleration of the microscope stage, and the time-consuming autofocus algorithm. If cells were properly focused in the initialization stage, only a small fraction (<5%) ran out of focus during 24-h compaction experiments. Under the given incubation conditions, the majority of the cells adhered firmly to the collagen matrix and demonstrated little migratory activity. Typically, cell centroids remained within a 150 μm radius of their original xy position. z position decreased slowly in many cases, reflecting compaction of the matrix in the vertical direction (data not shown).
Validation of the collagen compaction analysis
Analysis based on cross-correlation of images at a series of decomposition stages was compared with straightforward cross-correlation. This was performed on images simulating matrix compaction, subsequently followed by a challenge of increasing amounts of image noise and artificial translation.
Decomposition CC was more time-consuming than direct CC: respectively, 84 and 54 s per image pair. This was due to a larger number of cross-correlations that have to be performed, and more complex data storage and lookup operations. For low noise levels (Table 2, case c, d), both analysis methods render 100% correct displacement vectors (see Fig. 2). When noise increased, the number of vectors in the smallest decomposition stage that had insufficient cross-correlation (r < 0.5) rose. However, this effect was smaller in the decomposition CC versus direct CC. As an example, the percentage correct displacement vectors at 20.2% noise (Table 2, case f) was 77.7% in direct CC versus 89.5% in decomposition CC. Concurrently, the average displacement error at this noise level amounted to 2.1 and 0.7 pixels, respectively.
Fig. 2Noise sensitivity of the cross-correlation methods. Top from left to right simulated displacement field of 3% compaction (Table 2, case b), displacement field as predicted by direct CC, displacement field as predicted by decomposition CC; both cross-correlations were performed at a RD increase of 20.2% (Table 2, case f); vectors were not scaled to absolute displacements, but to optimal visual illustration; red asterisks indicate vectors based on cross-correlation values below threshold (0.5) at the highest resolution. Middle the fraction of displacement vectors in the smallest decomposition stage that is calculated correctly (error in horizontal and vertical direction ≤1 pixel). This fraction decreased at higher noise levels (relative dispersion: Table 2, case c–h). Bottom the average error between calculated and simulated displacement vectors increased for higher noise levels (Table 2, case c–h). The decomposition method resulted in a slightly lower number of incorrect vectors, and a large decrease in displacement error
Figure 3 shows estimates for matrix compaction for these simulated deformations. The simulated compaction over a disc with 350 pixels radius was 6%. For noise levels up to 7.6% (Table 2, case d), such compaction was indeed found by both correlation methods. At higher noise levels, direct CC underestimated compaction much more severely as compared to decomposition CC. As an example, after a noise increase of 20.2%, decomposition CC estimated a compaction value of 5.9% as compared to only 4.1% for direct CC.
Fig. 3Calculated area at a radial distance of 350 pixels after a simulated 3% compaction. Increasing displacement errors, caused by higher noise levels, resulted in a mismatch between calculated area and simulated compacted area (94% of original). The decomposition method showed stable results at larger relative dispersion values (Table 2, case c–h)
When an additional horizontal shift was imposed, both analysis methods correctly estimated compaction for 4.1% noise (Table 2, case i). For higher noise levels, decomposition CC succeeded in a correct area assessment. In contrast, at 20.2% noise and 60 pixels translation (Table 2, case j), direct CC predicted 1.2% rather than 6% compaction of the 350 pixels disc (see Fig. 4). This effect was not due to more incorrect displacement vectors, but could be attributed to a rise in average displacement error up to 14.6 pixels.
Fig. 4Sensitivity of the correlation methods to translation of the image. A horizontal displacement of 60 pixels was imposed after application of a 3% compaction and noise addition (4.1, respectively, 20.2% RD increase). Top left displacement field for high noise example (RD increase: 20.2%), as calculated by direct CC. Red asterisks indicate displacements where cross-correlation was below threshold (0.5); at these positions, a large mismatch between calculated compaction and simulated compaction occurs. Top right visual representation of matrix deformation at a radial distance of 270 pixels. Bottom calculated area remaining after a simulated compaction. At low noise levels (Table 2, case i), a horizontal shift after image resizing did not affect the calculated area; for high noise (Table 2, case j), failing correlation in the direct method results in a high offset in calculated area
Collagen compaction by individual smooth muscle cells
Figure 5 shows an example of collagen matrix compaction by an individual smooth muscle cell. In this particular case, gross geometrical reorganization occurred in the first 6 h of the 22-h observation period. Compaction magnitude decreases with distance, and there was a time delay of around 1.5 h before compaction visible at a radius of 97 μm (110 pixels) became apparent at a distance of 308 μm (350 pixels).
Fig. 5Typical example of collagen matrix compaction by individual smooth muscle cell as estimated by decomposition cross-correlation, obtained at a resolution of 1 frame per 15 min. Top left displacement field represented by vectors, refined in five decomposition stages. Vectors originating from a red asterisk were created at a larger correlation scale; for the smallest window, cross-correlation value at these positions was below threshold (0.5). Top right visual representation of matrix compaction at a radial distance of 350 pixels, as estimated from a series of displacement fields, showing an initial (yellow) circle and its deformed (green) state. Bottom estimation of compacted area after 22 h at distances of 110, 230 and 350 pixels (scaling: 0.88 μm/pixel)
Discussion
This study aimed at developing an automated technique for obtaining and analyzing matrix remodeling by individual cells. Emphasis was put on construction of an automatic, reliable algorithm for assessment of a detailed matrix displacement field. Especially, refinement of a cross-correlation based image analysis with a decomposition scheme was investigated. While “classic” direct CC sufficed for pairs of images with high correlation and low noise, this was no longer the case when substantial matrix remodeling occurred within the time frame between two consecutive images. This resulted in failing of CC at spots of high geometrical reorganization. However, when using decomposition CC, the gross displacements at these positions could be estimated by analysis of parent images with larger dimensions. This way, at a RD increase of 20.2% the average displacement error was lowered threefold in decomposition CC as compared to direct CC.
The method of refining displacement field accuracy with each decomposition step becomes progressively more important with larger displacements. Therefore we investigated the effect of a horizontal shift superimposed on a simulated compaction. Such shift occurred in our in vitro experiments when a group of neighboring cells pulled strongly on the matrix adjacent to a cell of interest. The shift interfered with direct CC because zero deformation was assumed when no proper correlation could be found. The result was an irregular displacement field (see Fig. 4). When using decomposition CC, on the other hand, the gross displacement was already detected in the first decomposition stage. Displacement values were then adjusted when local compaction was detected in subsequent decomposition stages. Using this strategy, a lack of local correlation at the finest resolution resulted in only a minor displacement error, with an average of 0.9 as compared to 14.6 pixels for direct CC.
Both cross-correlation methods differ not only in stability of displacement field estimation, but in efficiency of calculation time as well. Direct CC requires a large search area, i.e. the subwindows of I
2 have to extend considerably beyond the boundaries of their corresponding I
1 subwindow in order to perform a meaningful correlation. On the other hand, with decomposition CC n
cc can decrease with each stage (see Table 1), since each subwindow of I
2 is repositioned according to the preliminary displacement as calculated for its parent window. Due to the increased number of calculations and data manipulations that have to be performed, analysis time was still about 50% larger for decomposition CC at the settings as stated in Table 1.
We chose a correlation threshold of 0.5 for both techniques, as well as cross-correlation windows as indicated in Table 1. These values were empirically determined as an optimum in the trade-off between calculation time, false positive displacements and overlooking local deformations. Clearly, these choices depend on the contrast and texture of the images and the nature of the deformation, and will need to be optimized for specific future experiments.
Cell traction is frequently assessed by quantification of deformations in a flexible substratum. Both 2D and 3D approaches have been used, resulting in different cellular morphologies [19, 20, 29]. While the latter seems more physiological, interpretation of the 3D experiments is more complex [35]. 2D substrates offer more straightforward tools for analyzing mechanical behavior of single cells. These materials can be enriched with fluorescent microbeads to increase image contrast. In order to achieve a high resolution in the vicinity of cells, which can change their morphology rapidly, manual tracking of specific landmarks was employed in several studies [6, 28, 30]. However, this labor-intensive method inherently limits the number of cells under study. In several studies particle tracking was performed on individual microbeads [11]. In this case, resolution depends on particle distribution, which is typically far from homogeneous in collagen substrates [23].
Several nested cross-correlation methods have been developed [35]. All of these algorithms are based on empirical determination of square size, distance for pattern search, and normalized CC threshold. In order to spatially limit the search region, a relative translational shift has been derived from image registration [23, 33]. Several schemes have been implemented to increase displacement field resolution in a step-by-step manner. In a single refinement cycle, Dembo and colleagues [26] decreased the subwindow size if significant displacement was observed. On the other hand, window size can be increased if correspondence failure occurs at high resolution [23]. The algorithm developed by Wang and coworkers [33] resembled our decomposition CC to a large extent. However, these authors aimed at construction of traction fields with a smooth nature by incorporation of filtering procedures at several stages.
Geometrical matrix reorganization provides a qualitative index of the traction forces present in the underlying material. The actual forces can be derived from a displacement field series using material properties of the ECM construct. However, for collagen scaffolds these are highly heterogenous. Frequently, local stiffness is estimated by either microneedles [5, 10, 21, 30] or optical tweezers [4, 15, 32]. Tractions can then be calculated by application of stress–strain relationships, including appropriate boundary conditions [7, 24, 25, 31, 37, 38]. However, this translation to quantitative traction forces lies beyond the scope of this article.
In conclusion, we presented integral methodology for the study of matrix remodeling. These techniques allow systematic screening of the role of matrix components such as collagen, elastin, fibronectin and laminin. Likewise, the function of stationary cells like smooth muscle cells, fibroblasts, and osteoblasts can be investigated. Our method can be applied under a wide variety of other experimental conditions. The single requirement for the image quality is a sufficiently high contrast in the material under investigation, without the neeed for laborious and potentially interfering micropatterning. Furthermore, our graphical user interface enables a flexible tuning of parameters such as number of decompositions, size of correlation window, search area and cross-correlation threshold. Using our motorized microscopy stage it is possible to patch individual images together in order to create one large field of view for the study of motile cells such as keratocytes. Finally, the method can be extended with fluorescence imaging of specific cell structures, cytokines, hormones and enzymes [15].

cross-correlation
collagen
deformation
smooth muscle cell
matrix remodeling
microscopy
image analysis


Gender differences in the ratio between humerus width and length are established prior to puberty

Summary On a sample of 1,317 children aged 9.9 years we developed a novel method of measuring humeral dimensions from total body dual-energy X-ray absorptiometry (DXA) scans and showed that gender differences in the ratio between humeral width and length are established prior to puberty.
Introduction
Traditionally, the increased risk of osteoporotic fracture in women compared to men has been attributed to their low bone mass as a consequence of reduced peak bone mass acquisition and increased rates of bone loss following the menopause. However, more recent studies indicate that several other factors influence fracture risk independently of bone mass, such as skeletal geometry and the material properties of bone [1]. Furthermore there are gender differences in skeletal geometry that may contribute to the greater fracture risk in women compared to men. For example, long bone cross-sectional area is greater in men, which is thought to reflect higher rates of periosteal apposition from the time of puberty onwards [2]. One study of 68 girls and 59 boys aged 11.9 years, who underwent prospective peripheral quantitative computed tomography (pQCT) measurements, reported that periosteal growth was more rapid in pubertal boys compared to girls [3].
Skeletal growth is coordinated to ensure that the ratio between different skeletal dimensions is maintained despite rapid changes in size [4]. Therefore, it is possible that the greater cross-sectional area in boys compared to girls is a reflection of their larger size. On the other hand, the ratio between periosteal growth and longitudinal bone growth may be different in boys compared to girls, leading to gender differences in bone shape. However, little is known of the inter-relationships between longitudinal and periosteal bone growth, since investigation of possible gender differences in skeletal geometry have generally been confined to analysis of cross-sectional area; in the absence of techniques capable of simultaneous measurement of long bone length, it has been difficult to accurately assess how the ratio between longitudinal and periosteal growth is affected by gender and other factors such as puberty.
Recently, we developed a novel method for evaluating long bone geometry based on analysis of the humerus on total body dual-energy X-ray absorptiometry (DXA) scans. The humerus offers important advantages over other long bones in that its entire outline can readily be traced on total body DXA images, and its shape can be modelled as a cylinder with reasonable accuracy. To explore the utility of regional DXA analysis at the humerus, ‘volumetric’ bone density of the humerus was derived by dividing humeral bone mineral content (BMC) by estimated humeral cylindrical volume, and then analysed in relation to fracture risk. Interestingly, humeral volumetric bone density obtained in this way was indeed related to fracture risk, as analysed in a subgroup of 1,317 children from the Avon Longitudinal Study of Parents and Children (ALSPAC), in whom total body DXA scans were available at 9.9 years of age [5].
In the present study, we aimed to characterise the influence of gender and puberty on the ratio between longitudinal and periosteal growth, by exploiting this novel technique of humeral geometric analysis. In particular, we wished to determine whether, in analyses combining fracture and non-fracture controls from the study described above, gender differences exist in the ratio between width and length of the humerus, and if so whether these differences are established prior to puberty.
Methods
Study population
The Avon Longitudinal Study of Parents and Children (ALSPAC) is a geographically based cohort that recruited pregnant women residing in Avon with an expected date of delivery between 1 April 1991 and 31 December 1992. A total of 14,541 pregnancies were initially enrolled, with 14,062 children born. This represented 80–90% of the eligible population–see http://www.alspac.bris.ac.uk for further details [6]. Of these births, 13,988 were alive at 12 months. The population from ALSPAC used for this study consisted of 1,290 children: those who had DXA scans performed at aged 9.9 years, had measures of humeral dimensions performed and had data on pubertal stage available. Ethical approval was obtained from the ALSPAC Law & Ethics Committee, and Local Research Ethics Committees.
Measure of size at birth
In the immediate post-partum stage, whilst mother and child were still in hospital, trained ALSPAC staff measured crown-heel length with the Harpenden Neonatometer (Holtain Ltd., Crosswell, UK). Alternatively, this measure was collected from clinical records for babies who were not captured by the ALSPAC staff.
Measures of size at age 9.8 years
Children were seen in a research clinic at age 9.8 years (± 0.33 years). Parental consent and child’s assent were obtained for all measurements made. Height was measured to the last complete millimeter (mm) using the Harpenden Stadiometer. Weight was measured to the nearest 50 grams (g) using the Tanita Body Fat Analyzer (model TBF 305, HealthCheck Systems, New York, USA). Total body less head (TBLH) bone area (cm2), TBLH bone mineral content (g), TBLH bone mineral density (g/cm2), total body (TB) fat mass (kg) and TB lean mass (kg) were measured using a Lunar Prodigy dual-energy X-ray absorptiometer (Lunar Corp., Madison, WI, USA) on 7,444 children, using the default mode for all scans. The child was positioned carefully on the scanner and asked to lay their hands flat on the bed, palms down.
Measures of humeral dimensions
The present investigation was based on a subgroup of 393 children reporting fractures, for whom DXA scans were available at age 9.8 years, and an additional randomly selected group of 897 children, giving a total of 1,290 children. These children had originally been chosen for a study investigating the relationships between bone mass and fractures [5]. The measurer (EC) was blinded to the fracture status of the children. Customised settings were available on the Lunar Prodigy software and these were applied to the total body DXA image on screen. (No measures were performed during the DXA scan.) A region of interest (ROI) was drawn around the right humerus where possible (in case of movement artifact, the left humerus was used) after enlargement of the image to maximum magnification. The bone edge was detected visually with ease for the shaft and head of the humerus. At the distal end a straight line was drawn across the joint space from medial to lateral epicondyle, with the head of the ulna included within the humeral ROI. Where arm positioning was not ideal (such as palms not flat on the bed) the ROI was fitted as accurately as possible. The area (cm2) of the humeral ROI was recorded (Fig. 1). Length of the humerus was obtained by use of an electronic ruler positioned between its upper and lower extremities. Average humeral width (cm) was calculated as area divided by length. The humeral aspect ratio (AR) was calculated as humerus width divided by length and then multiplied by 100, so the AR is the humerus width expressed as a percentage of humerus length.
Fig. 1Total body DXA scan showing position of the region of interest (ROI) around the right humerus. a Total body scan image. b Image following maximal (i.e. fourfold) magnification, which was selected prior to placement of the ROI and measurement of length by electronic ruler
The precision of measurements of humeral geometry was calculated as the coefficient of variation (CV), based on ten scans with the measures repeated five times. The CV was 2.9% [95% confidence interval (CI): 2.1–3.7] for width, 1.5% (95% CI: 1.2–1.7) for length, and 3.2% (95% CI: 2.4–4.0) for humeral AR.
Other measures
The mother’s, partner’s and grandparent’s race and ethnic group and mothers’ highest educational qualification were recorded at 32 weeks gestation as described elsewhere [7]. Gender was obtained from birth notifications. At the time of the DXA scan and measurement of the anthropometric variables, the child’s age was calculated from the date of birth and date of attendance at the research clinic. Puberty was assessed by self-completion questionnaires using diagrams based on Tanner staging of pubic hair distribution for boys and girls, which we have previously found to show expected relationships with DXA measures in this cohort [8]. Prepuberty was defined as Tanner stage 1, and early puberty as Tanner stage 2.
Statistical analyses
Results from children reporting previous fractures and the randomly selected subgroup were pooled. Statistical analyses were performed with STATA 8.0. A two-tailed unpaired t-test was used to test the null hypothesis of no difference in the means for boys and girls. Linear regression was used to assess the associations between gender and humerus dimensions, which were adjusted for age on the day of the DXA scan and pubertal status. Additional analyses were performed following adjustment for TB fat mass and for fracture status.
Results
No differences in gender, ethnicity, socio-economic status, body composition or humerus dimensions were found between the children with and without fractures (results not shown) so these results were pooled for all further analyses. Length at birth, age at DXA measurement, height, weight, TB fat and lean mass and TBLH bone area of the 1,290 children in whom humeral dimensions were measured are shown in Table 1 according to gender and pubertal stage. There was no difference in age, height or weight between prepubertal boys and girls. However, boys had been longer at birth (by 0.3 cm on average), even after adjustment for gestational age and birth weight (P < 0.001). Prepubertal girls had a greater TB fat mass (P < 0.001), whereas prepubertal boys had a greater TB lean mass (P < 0.001). Puberty-related differences in size measures were also seen. For example, girls in early puberty (Tanner stage 2) were on average 4.4 cm taller and 5 kg heavier than prepubertal girls (Tanner stage 1). Boys’ pubertal stage showed similar trends to those observed in girls, but differences in size measures were considerably smaller.
Table 1Mean age, height, weight, and DXA-derived total body fat mass and lean mass, and total body less head bone area for 648 boys and 642 girls with measurements of humeral size and dimensionsa BoysGirls Prepubertal, N = 551Early pubertal,N = 97P value for difference between boys in pre- and early pubertyPrepubertal, N = 548Early puberty, N = 94P value for difference between girls in pre- and early pubertyP value for difference between prepubertal boys and girlsP value for difference between boys and girls in early pubertyMeanSDMeanSDMeanSDMeanSDBirth length (cm)b50.91.550.91.50.99550.61.550.71.50.658<0.0010.316Age at DXA (years)9.80.39.90.30.6159.80.39.80.30.9460.6310.602Height (cm)139.46.1140.76.10.041138.76.0143.16.0<0.0010.0810.011Weight (kg)34.17.134.97.10.30334.27.339.27.3<0.0010.795<0.001TB fat mass (kg)7.34.97.74.90.4719.34.912.15.0<0.001<0.001<0.001TB lean mass (kg)25.33.025.63.00.31423.32.825.22.8<0.001<0.0010.332TBLH bone area (cm2)1,1411541,1611550.2611,1131571,217158<0.0010.0020.002aResults are shown separately for boys and girls, who are further subdivided according to results of Tanner stage self-completion questionnaire. P values shown are for the difference between prepubertal boys (Tanner stage 1) and boys in early puberty (Tanner stage 2); for the difference between prepubertal girls (Tanner stage 1) and girls in early puberty (Tanner stage 2); for the difference between prepubertal boys and girls; and for the difference between boys and girls in early puberty, all calculated by an unpaired Student’s t-test. For TB fat mass, TB lean mass and TBLH bone area results are adjusted for age at DXA measurement, by linear regression.bBirth length adjusted for gestational age and birth weight by linear regression.
We then examined relationships between dimensions of the humerus, age, height and puberty. As expected, height was positively related to humerus, width and length based on analyses in boys and girls combined (P < 0.001). In spite of the relatively narrow age range of our study population, a positive association was also observed between age and width and length of the humerus (P < 0.001). In contrast, age and height were not related to humeral AR. Similar results were seen when boys and girls were analysed separately. Girls in early puberty had greater humeral width, length and area compared to prepubertal girls, but humeral AR in these two groups was similar (Table 2). In contrast, no differences were observed in any measure of humeral geometry between pre- and early pubertal boys.
Table 2Humeral dimensions in prepubertal boys, boys in early puberty, prepubertal girls and girls in early pubertya BoysGirls Prepubertal, N = 551Early pubertal, N = 97P value for difference between boys in pre- and early pubertyPrepubertal, N = 548Early puberty, N = 94P value for difference between girls in pre- and early pubertyP value for difference between prepubertal boys and girlsP value for difference between boys and girls in early pubertyMeanSDMeanSDMeanSDMeanSDLength (cm)24.71.425.01.40.15924.91.425.91.5<0.0010.026<0.001Width (cm)1.920.21.930.20.6091.880.21.930.20.002<0.0010.985AR (%)7.780.77.760.70.6997.530.67.470.60.390<0.0010.006Area (cm2)47.76.048.46.10.29746.965.950.265.9<0.0010.0230.052aResults show mean and standard deviation for humeral length, width, aspect ratio (AR) and area. P values shown are for the difference between prepubertal boys (Tanner stage 1) and boys in early puberty (Tanner stage 2); for the difference between prepubertal girls (Tanner stage 1) and girls in early puberty (Tanner stage 2); for the difference between prepubertal boys and girls; and for the difference between boys and girls in early puberty, all calculated by an unpaired Student’s t-test. All analyses are adjusted for age at DXA measurement by linear regression.
We then investigated the effects of gender on measures of humeral geometry according to pubertal stage, following adjustment for age of DXA scan. The humerus of prepubertal boys was slightly shorter (on average 0.2 cm), but of greater width (average of 0.04 cm) and area (on average 0.8 cm2), compared to prepubertal girls, as a result of which humeral AR was greater in prepubertal boys (an average of 3.2% greater, P < 0.001) (Table 2 and Fig. 2). Boys in early puberty still had a shorter humeral length, but a similar humeral width and a smaller area than girls in early puberty, as a result of which humeral AR remained greater in early pubertal boys (an average of 3.7% greater, see Fig. 2). Similar gender differences were seen after adjustment for TB fat mass and fracture status (results not shown).
Fig. 2Associations between humeral geometry, gender and puberty, as determined in 1,290 boys and girls. Figure shows mean ± SD a humerus width (cm), b humerus length (cm) and c humerus aspect ratio (AR) according to gender and Tanner stage of puberty. P values are for the difference between boys and girls. All analyses are adjusted for age
Discussion
Humeral width and length were positively related to age and height in boys and girls combined, and to pubertal status in girls, in this contemporary cohort of pre- and early pubertal children. These observations are similar to those previously reported for other DXA-derived measures of bone size in this cohort, such as TBLH and spinal bone area [7]. In contrast, age, height and pubertal status did not influence the ratio between humeral width and length, as reflected by humeral AR, presumably reflecting the action of mechanisms to ensure that skeletal shape remains constant as bones grow. In both pre- and early pubertal boys, humeral length was found to be shorter compared with girls, whereas humeral width was similar or greater, resulting in a greater humeral AR in boys. Taken together, these findings suggest that gender, but not puberty, affects the balance between periosteal and longitudinal growth. Hence, differences in overall skeletal shape between boys and girls appear to be established prior to puberty. However, from the present study we are unable to determine whether the AR changes during later pubertal stages, or during subsequent ageing.
Our observation that Tanner stage was found to affect humeral geometry in girls but not boys presumably reflects the fact that boys and girls in Tanner stage 2 are not equivalent in terms of skeletal development. The finding that height and weight differences between Tanner stages 1 and 2 were considerably greater in girls compared to boys is consistent with this view. Therefore, analyses of differences in humeral geometry between Tanner stage 2 boys and girls may have limited validity, since these may not have fully accounted for gender differences in skeletal maturity that are likely to have been present. Nevertheless, since humeral AR was unaffected by Tanner stage in the age of children studied, these reservations are unlikely to affect the main conclusion from this study, namely that humeral AR is greater in boys compared to girls as assessed at age 9.9 years.
The gender differences in skeletal shape shown in Table 2 (approximately 2–3%) were smaller than those observed in fat mass, lean mass and bone area as in Table 1 (ranging from 7 to 27%). The larger gender differences in TBLH bone area (6.5%) compared with humeral area (2.7%) suggest that greater gender differences in bone area are present at other skeletal sites. Consistent with this conclusion, vertebral body size has been reported to be 11% [8] and 15% [9] larger in boys compared to girls. Our observation of an 8% gender difference in lean mass and 27% difference in fat mass, compared to the 1% gender difference in humeral length and 2% difference in width, perhaps reflects the strength of association between fat or lean mass and bone area [10].
Our conclusion that gender differences in humeral shape are established prior to puberty is supported by a previous study in which greater humeral width was seen in prepubertal boys compared to girls, based on radiogrammetry [11]. In the present study, the humerus was selected as the most suitable site for providing an accurate measure of aspect ratio by analysis of total body DXA scans. Although other skeletal sites were not evaluated, we assume that equivalent gender differences in periosteal relative to longitudinal growth are established prior to puberty throughout the appendicular skeleton. Consistent with this suggestion, the metacarpals and proximal radius have been found to be wider in boys compared to girls at all stages of development, as assessed by analysis of radiographs [12, 13]. Furthermore, in a recent study of 128 boys and girls, boys had higher rates of periosteal expansion relative to girls, as measured prospectively over 20 months at the radial midshaft by pQCT, and this gender difference was similar in early, peri- and postpubertal children [3]. In a recent analysis of 18-year-old males and age-, height- and weight-matched females, long bone width was found to be greater at the hip and distal tibia as measured by DXA and pQCT, respectively, in boys compared to girls [14].
Although our results suggest that gender differences in long bone width are due to more rapid periosteal apposition relative to longitudinal growth in boys prior to puberty, the precise timing of this gender effect is currently unclear. Review of the literature shows no evidence of gender differences in forearm bone width at birth [15], but studies of preschool and older children show conflicting results: Specker et al. [16] report no gender differences in radius width in children aged 1–6 years (based on 89 children), whereas Tanner et al. [17] found that the humerus is wider in boys compared to girls from age 3 years until the time of pubertal growth acceleration in girls (based on 505 children aged 3–18 years). It is also possible that the method used to measure bone size influences whether gender differences are found. For example, studies described above that used radiographs or pQCT identified gender differences [3, 11–13, 17], whereas those that used single photon absorptiometry did not [15, 16].
Prepubertal gender differences in the relative rates of longitudinal and periosteal growth that we observed may be mediated by alterations in endocrine factors. For example, prepubertal girls have higher levels of insulin-like growth factor I, estradiol and testosterone concentrations compared to prepubertal boys [18], all of which are known to have effects on both longitudinal and periosteal bone growth. In terms of the potential influence of these differences on fracture risk, according to beam theory, columns with larger aspect ratios (i.e. ratio of width to length) have a reduced fracture risk than columns with smaller aspect ratios [19]. In addition, the ratio between periosteal diameter to long bone length provides an approximate estimate of critical buckling load, such that a lower aspect ratio results in a long bone which is more prone to failure by buckling [20]. Furthermore, in children, the majority of fractures occur at the distal forearm and can be divided into two main types: simple torus fractures and the ‘greenstick’ variety, both of which are associated with buckling or bulging on the side of the bone in compression [21].
Therefore, theoretically, measurement of the ratio between long bone width and length from total body DXA scans as described here may provide an in vivo method for evaluating biomechanical strength of the skeleton. However, against this suggestion, boys have a higher fracture risk than girls in childhood [22], whereas girls have a smaller humeral AR. Furthermore, we found no relationship between humeral AR as measured in the present study and fracture risk [5]. One possible explanation for this lack of association is that our assumption that the humerus is cylindrical ignores gender differences in shape at the epiphysis or metaphysis which might contribute to fracture risk. On the other hand, humeral AR may predict fracture risk in certain adult populations, in view of evidence that bone width has previously been reported to be related to stress fractures in soldiers [23]. In light of our results, which suggest that humeral AR can be evaluated with relatively good precision, further studies are justified to determine whether this parameter represents a novel bone mineral density (BMD)-independent risk factor for upper limb fracture.
The measure of humeral length from which we derived humeral AR is likely to be relatively accurate, since the upper and lower ends of the humerus are generally clearly visible on total body DXA scans (see Fig. 1). Alternative measurement techniques, such as pQCT, offer advantages over the approach described here, by measuring bone diameter directly, but do not provide a measure of bone length. Another limitation of the present study is that unlike girls, age 9.9 years appeared to be too young to evaluate possible effects of early puberty on skeletal development in boys. In future studies, we plan to repeat these analyses in older boys to confirm that as in girls, puberty increases humeral width and length whilst having no effect on humeral AR.
In conclusion, we have found that long bone shape, as reflected by humeral AR which we derived using a novel technique from total body DXA scans, is unaffected by age, height and puberty, as evaluated in a child cohort of relatively narrow age range and range of Tanner stages. This finding suggests that the ratio between longitudinal and periosteal growth is controlled to ensure it remains constant during rapid growth. However, humeral AR was related to gender, suggesting that the greater periosteal diameter of boys compared to girls, which is well recognised, is a consequence of gender differences in the balance between longitudinal and periosteal bone growth. Interestingly, these gender differences in humeral AR were present in prepubertal children, possibly resulting from prepubertal differences in sex hormone levels. Further studies are justified to determine whether humeral AR is an important determinant of biomechanical strength and fracture risk, particularly in adult populations.

gender differences
epidemiology
growth and development
long bone dimensions
population studies