1471-2407-6-148 1471-2407 Research article <p>Inverse association of antioxidant and phytoestrogen nutrient intake with adult glioma in the San Francisco Bay Area: a case-control study</p> Tedeschi-Blok Nicole tedeschi@usc.edu Lee Marion mmlee@itsa.ucsf.edu Sison D Jennette jdsison@itsa.ucsf.edu Miike Rei wrensch@itsa.ucsf.edu Wrensch Margaret wrensch@itsa.ucsf.edu

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, SRT 1015, MS 103, Children's Hospital Los Angeles, Los Angeles, USA

Department of Neurological Surgery, University of California San Francisco, School of Medicine, San Francisco, USA

Department of Epidemiology and Biostatistics, University of California San Francisco, School of Medicine, San Francisco, USA

 BMC Cancer 1471-2407 2006 6 1 148 http://www.biomedcentral.com/1471-2407/6/148 16749939 10.1186/1471-2407-6-148 19 1 2006 03 6 2006 03 6 2006 2006 Tedeschi-Blok et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Increasing evidence from epidemiologic studies suggest that oxidative stress may play a role in adult glioma. In addition to dietary antioxidants, antioxidant and weak estrogenic properties of dietary phytoestrogens may attenuate oxidative stress. Our hypothesis is that long-term consumption of dietary antioxidants and phytoestrogens such as genistein, daidzein, biochanin A, formononetin, matairesinol, secoisolariciresinol and coumestrol, may reduce the risk of adult glioma.

Methods

Using unconditional logistic regression models, we compared quartiles of consumption for several specific antioxidants and phytoestrogens among 802 adult glioma cases and 846 controls from two study series from the San Francisco Bay Area Adult Glioma Study, 1991 – 2000, controlling for vitamin supplement usage, age, socioeconomic status, gender, ethnicity and total daily calories. For cases, dietary information was either self-reported or reported by a proxy. For controls, dietary information was self-reported. Gender- and series- specific quartiles of average daily nutrient intake, estimated from food-frequency questionnaires, were computed from controls.

Results

Significant p-values (trend test) were evaluated using significance levels of either 0.05 or 0.003 (the Bonferroni corrected significance level equivalent to 0.05 adjusting for 16 comparisons). For all cases compared to controls, statistically significant inverse associations were observed for antioxidant index (p < 0.003), carotenoids (alpha- and beta-carotene combined, p < 0.05), daidzein (p = 0.003), matairesinol (p < 0.05), secoisolariciresinol (p < 0.003), and coumestrol (p < 0.003). For self-reported cases compared to controls, statistically significant inverse associations were observed for antioxidant index (p < 0.05) and daidzein (p < 0.05).

Conclusion

Our results support inverse associations of glioma with higher dietary antioxidant index and with higher intake of certain phytoestrogens, especially daidzein.

Background

Epidemiologic evidence from studies of dietary antioxidant intake and adult glioma support a role for oxidative stress in gliomagenesis. Several studies have shown that antioxidant consumption from fruits and vegetables may be protective against adult glioma 1234. Most recently, a large case-control study in Nebraska showed that increased intake of carotenoids reduces the risk of glioma by 50 percent and suggests that antioxidant as well as phytochemical nutrient intake is likely to play a protective role in adult glioma 4. Pro-inflammatory and pro-angiogenic mediators may also be implicated in glioma development and progression. Recent studies suggest that estrogen receptors may play a role in decreasing both pro-inflammatory and pro-angiogenic mediators 56. Furthermore, estrogen receptor expression is negatively correlated with astrocytic tumor grade 7. Phytoestrogens, plant-derived chemicals with both antioxidative and estrogenic properties, have structural similarities to 17-beta-estradiol and can be categorized to isoflavones (eg., genistein, daidzein, biochanin A, and formononetin), lignans (eg., matairesinol and secoisolariciresinol), and coumestans (eg., coumestrol). Some foods rich in isoflavones include soy, lentils, and beans. Foods rich in lignans include oilseeds, whole-grain cereals, legumes, and berries; while alfalfa and clover are foods rich in coumestans. In addition to their antioxidative and estrogenic properties, phytoestrogens are noted for their clinical potential in antiviral, antibacterial, antiproliferative, angiogenic activities 8.

If oxidative stress is involved in brain tumor development, then dietary intake of nutrients of fruits and vegetables with antioxidative and estrogenic properties may reduce the risk of adult glioma. We addressed this hypothesis by comparing nutrient intake between incident glioma cases and controls of the San Francisco Bay Area Adult Glioma Study, 1991–2000.

Methods

Human subject methods were approved by the University of California Committee on Human Research, approval number H6539-04956. Informed consent was documented through subject signature on the approved consent document.

Case ascertainment

The San Francisco Bay Area Adult Glioma Study, 1991–2000, is described in detail elsewhere 29101112. Briefly, cases were identified within a median of seven weeks of diagnosis using Northern California Cancer Center's rapid case ascertainment system. Eligible cases included those age 20 years and older, with histologically confirmed newly diagnosed glioma, International Classification of Disease for Oncology codes 9380 to 9481, who resided in 6-counties of the San Francisco Bay Area, which included Alameda, Contra Costa, Marin, San Mateo, San Francisco, or Santa Clara. Ascertainment periods included August 1991 to April 1994 (series 1) and from May 1997 to August 2000 (series II). Pathologic specimens were obtained and reviewed by a single neuropathologist in each series 1314, thereby minimizing misclassification of diagnosis.

Control ascertainment

In both series, controls were identified by random digit-dialing and frequency matched to cases on ten-year age groups, gender, and ethnicity (White, Black, Hispanic, Asian, or other). Initial sampling units included area code, 3-digit prefix, and 2 digits of cases' phone numbers. Eligible matches were found for each sampling unit through 2 digit suffixes generated from random number tables 2.

Dietary questionnaires and interviews

Series I

Eligible cases or next-of-kin and controls were sent a letter and telephoned to schedule an in-person interview at subjects' home or other location of their choice 2. Interviews were conducted with consenting subjects or their next-of-kin (proxies) when cases were not available due to death or disability. Subjects were sent a self-administered dietary questionnaire in advance, which was collected or completed at the in-person interview. All questions were asked for the year prior to glioma diagnosis for cases and for the previous year for controls.

The series I questionnaire consisted of 79-item food-frequency questions modified from the US National Cancer Institutes' (Block's) Health Habits and History Questionnaire 15 and the Los Angeles Glioma and Meningioma Study Questionnaire 16. The modifications were originally made to emphasize antioxidant, nitrite, and nitrate – containing foods 2. Food items were group into the following categories: fruits and juices; vegetables; breakfast foods; lunch foods; meat, fish, poultry and mixed dishes; breads, salty snacks and spreads; sweets; dairy products and beverages. Participants were asked to choose usual frequencies of consumption of each food item. The frequency choices included: 'never', 'less than 1 per month', '1 per month', '2–3 per month', '1 per week', '2 per week', '3–4 per week', '5–6 per week', '1 per day', and '2 or more per day'. Portion sizes were not asked, but were estimated using gender- and age- specific values from the Block and colleagues' National Cancer Institute's Health Habits and History Questionnaire 15. Additional questions were asked about how often lemon juice was added to fish, fruit juices or tea was consumed with cured meat or bacon, and beer was consumed with meals. Frequency of vitamin supplement use (never or number of times per day, week, month, or year as well as dose) was also asked.

Series II

Similarly, eligible cases or next-of-kin and controls were sent a letter and telephoned to schedule an in-person interview at subjects' home or other location of their choice. Interviews were conducted with consenting subjects or their proxies when cases were not available due to death or disability. Rather than a self-administered diet questionnaire as in series I, interviewers asked the diet questions in series II. As in series I, all questions were asked for the year prior to glioma diagnosis for cases and for the previous year for controls in series II.

The series II diet questionnaire consisted of 96 food-frequency questions modified from the US National Cancer Institutes' (Block's) Health Habits and History Questionnaire 15 and included questions on portion sizes. Modifications included additional fruits, vegetables, and soy products (eg., avocado, banana, fresh peaches, mangoes or papayas, beets, cauliflower, celery, turnips, radishes, rhubarb, salsa, soy burgers, soy milk, and, tofu). The frequency choices included: 'never or less than 1 per month', '1 per month', '2–3 per month, '1 per week', '2 per week', '3–4 per week', '5–6 per week', '1 per day'. Portion sizes were asked according to the number of pieces, or either small, medium, large, or extra-large, for food frequencies of 1 per week or greater; using visual aids and 3-dimensional models. Questions on type (multi-vitamin or antioxidant), frequency and duration of vitamin supplements usage were also included.

Series I & II

For the purposes of assessing major differences of dietary consumption comparing eligible participants to eligible non-participants, an abbreviated questionnaire was administered to some unwilling to participate in the full interview. In a 5-minute phone interview, the abbreviated questionnaire asked basic demographic and dietary (including vitamin supplement use) questions.

Dietary analyses

For both series, only one value was assigned for each food frequency choice by converting the unit of year, month or day to the number of times per day. For ranges of frequencies, the mid-value was assigned before conversion, e.g. 2.5 times per month was assigned if the '2–3 per month' category was given. Intake of each food item was converted to grams consumed per day by applying an appropriate algorithm for each series as described in the following: For series I, the frequency and an assigned gender- and age-specific medium portion size were multiplied against a nutrient database to produce grams consumed per day for each food item. The assigned gender- and age-specific medium portions were based on the Block and colleagues' National Cancer Institute's Health Habits and History Questionnaire Personal Computer System Packet 17. For series II, the frequency and portion reported were multiplied against the same nutrient database to produce grams consumed per day for each food item. For both series, this nutrient database, which included a total of 27 nutrients per 100 grams for each food item, was based on the California Teacher's Study 1819, USDA nutrient database 20, and the Block and colleagues' National Cancer Institute's Health Habits and History Questionnaire Personal Computer System Packet 17. The summation of nutrient amounts over all food items provided a subject's total daily intake for 27 nutrients. Of the 27 nutrients, there was a total of 15 antioxidants and phytoestrogens for our case-control comparisons.

Additionally for both series, a total daily antioxidant index intake was calculated by summing the product of grams consumed over all food items and units of antioxidant index per gram from an antioxidant index database for fruit, vegetable, juice and tea food items. We constructed the antioxidant index database based on data from Wang and colleagues 21 (for fruits and fruit juices), Cao and colleagues 22 (for fruits and vegetables) and Rice-Evans and Miller 23 (for fruits, vegetables, and teas). A total of 35 items of fruits, vegetables, juices and teas from either series were included in the antioxidant index database. All 35 food items were available for series II participants, while only 22 of the 35 food items were available for series I participants (see above for additional fruits and vegetables included in series II only). Therefore, because the antioxidant index was the sum of antioxidant values across food items reportedly consumed by each subject, series II participants had higher values for the total antioxidant index than series I participants. Antioxidant index values were units of micromoles Trolox equivalents per gram of food (fruits, vegetables, juices or teas). Trolox equivalents per gram of food were measured by the oxygen radical absorbance capacity (ORAC) assay 2122. The ORAC assay measures the degree to which a sample inhibits oxidizing agent action using Trolox, a water-soluble analogue of vitamin E, as a control standard.

SAS version 8.02 (SAS Institute Inc, Cary, NC) was used for all statistical analyses. Demographic distributions of cases were compared to those of controls using t-tests or analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Means and standard errors as well as geometric means with upper and lower 95% confidence limits for daily consumption of total antioxidant index as well as various individual antioxidants and phytoestrogens adjusted for total calories were generated with general linear models for all cases, self-reporting cases and controls using SAS procedure GLM. Logistic regression models estimated odds ratios for quartiles of consumption of antioxidant index or each nutrient adjusting for age (continuous), gender, ethnicity (White versus other), socioeconomic status (combined income and education categories), and total calories (continuous). Thus, to adjust for total energy intake, we employed the standard multivariate method 24. In addition, vitamin supplement use and/or meat consumption were explored as covariates in the logistic regression models.

Case-control comparisons were analyzed separately by series since data on nutrient and antioxidant index intake was collected by different methods between study series. Because comparison of series-specific odds ratios and corresponding 95% confidence showed that odds ratios were largely in the same direction and their corresponding 95% confidence intervals overlapped between series, we also conducted combined series case-control comparisons with a study series indicator variable as a necessary covariate in logistic regression models. Gender- and series- specific quartile cutpoints were determined for the antioxidant index or nutrient intake levels based on food consumption among the controls. Test for trend were computed for case-control odds ratios of antioxidant index or nutrient quartiles. Results are described for p-values generated based on a 5% significance level and two-sided hypothesis tests. Finally, a Bonferroni correction for 16 simultaneous comparisons (an antioxidant index plus 15 nutrients) was applied to emphasize results based on a 0.003 (= 0.05/16) significance level.

Results

The total number of subjects from both series that were identified as eligible to participate in the San Francisco Bay Area Adult Glioma Study, 1991–2000, includes 1,110 cases and 1,284 controls. Participation was greater among eligible cases (79%–80% for series I and II) than among eligible controls (67% for combined series data, 74% for series I, and 60% for series II) 91011. Dietary data for 98 percent (846/864) of controls, 94 (497/524) percent of self-reported cases and 87 (305/349) percent of proxy-reported cases were included in the dietary analyses, based on the number of items with missing food frequency and daily intake of total calories. Table 1 summarizes frequency distributions for vitamin supplement intake, demographics, and clinical factors for 802 cases (including responses from 305 proxies and 497 self-reporting cases) and 846 controls included in the dietary analyses. Because of better survival of younger cases, the mean age for self-reported cases was 50 years, significantly lower than that of the controls (p < 0.001, table 1). For combined income and education, self-reported cases had greater socioeconomic status than controls (p < 0.001, table 1); likely due to younger ages of self-reporting cases. Geometric means of total daily nutrients consumed adjusted for total calories among cases and controls are shown in table 2. The observed average daily nutrients consumed were reasonable values for the U.S.

<p>Table 1</p>

Distribution of vitamin supplement intake, age, gender, ethnicity, income, education, body mass index and tumor type for eligible participants for combined series I & II; San Francisco Bay Area Adult Glioma Study, 1991–2000

Description

Cases1

Controls (N = 846)

All (N = 802)

Self-reported (N = 497)

Proxy-reported (N = 305)

Vitamin Supplement intake (% YES)

62.6

52.4

***

50.2

***

56.0

*

General Demographics

Age (median) ± SE2

55.1 ± 0.6

55.4 ± 0.6

ns

49.7 ± 0.7

***

64.7 ± 0.8

***

Gender as % Male

54.5

57.1

ns

58.6

ns

54.8

ns

Ethnicity as % White3

83.8

83.3

ns

83.9

ns

82.3

ns

Socio-economic Status Measures (%)

Household Income4

<$29,999

25.0

24.3

ns

17.4

***

36.0

***

$30,000–$69,999

40.8

39.8

38.5

42.1

≥$70,000

34.1

35.9

44.1

21.9

Highest Education

Less than high school

6.0

10.1

**

4.8

ns

18.7

***

High school, some college

48.5

48.9

47.5

51.2

College graduate and/or more

45.5

41.0

47.7

30.2

Socio-economic Status5

<High School education or income <$29,999

27.3

27.7

ns

19.1

***

41.6

***

≥High School graduate or income $30,000–$69,999

53.2

50.4

52.7

46.6

College graduate and income ≥ $70,000

19.5

22.0

28.2

11.8

Body Mass Index6 (median) ± SE2

25.7 ± 0.2

25.9 ± 0.2

ns

25.7 ± 0.2

ns

26.2 ± 0.3

ns

Tumors Types (%)

Glioblastoma

59.2

47.7

78.0

Anaplastic Astrocytoma

11.2

10.7

12.1

Astrocytoma

5.4

7.2

2.3

Oligodendroglioma

11.1

16.5

2.3

Oligoastrocytoma

6.2

8.5

2.6

Ependymoma

1.0

1.4

0.3

Other

5.9

8.1

2.3

1 p-values for case-control comparisons (t-tests for continuous variables or chi-square tests for categorical variables): * p = 0.05, **p = 0.01, ***p = 0.001, or not significant (ns). 2 SE = standard error. 3 The category other than white includes African-Americans, Chinese, Japanese, other Asians, Filipinos, Mexicans, other Latinos, and others. 4 Percentages do not total 100% due to "Refuse to Answer" and "Don't Know". 5 Categories for Socio-economic Status combine highest education attained and/or household income range. 6 BMI calculated from reported height and weight at interview.

 <p>Table 2</p>

Geometric means of consumption of daily nutrients adjusted for total calories*; San Francisco Bay Area Adult Glioma Study, 1991–2000.

NUTRIENT

Controls (N = 846)

All Cases (N = 802)

Self-reported Cases (N = 497)

Antioxidant index1

2675.3

2433.3

2450.4

Vitamin E (mg)

7.8

7.7

7.6

Vitamin C (mg)

115.8

107.6

109.6

Carotenoids2 (mcg)

3743.3

3404.0

3327.1

Alpha-carotene (mcg)

672.6

581.4

554.6

Beta-carotene (mcg)

3008.8

2756.0

2697.7

Lutein (mcg)

2094.3

1884.7

1911.4

Lycopene (mcg)

1324.4

1310.6

1320.9

Cryptoxanthin (mcg)

114.1

106.7

111.0

Phytoestrogens:

Formononetin (mcg)

14.7

14.2

15.2

Coumestrol (mcg)

121.1

107.9

112.1

Matairesinol (mcg)

25.3

23.3

23.6

Genistein (mcg)

260.4

233.5

253.9

Daidzein (mcg)

396.9

352.7

372.8

Biochanin A (mcg)

25.4

23.5

24.0

Secoisolariciresinol (mcg)

115.0

104.1

103.5

Total Protein (g)

60.9

59.7

59.2

Total Fat (g)

56.4

58.2

56.8

Total Cholesterol (mg)

200.7

210.2

204.7

Total Kcal (unadjusted)

1609.6

1646.5

1612.7

1Units are daily micromoles of Trolox equivalents gram of fresh weight as determined by the oxygen radical absorbance capacity (ORAC) assay. 2 alpha- and beta-Carotene. *Note: Means within each group were adjusted for total calories.

Information on vitamin type (multi-vitamin or antioxidant), asked for series II, was not considered during the analyses because the reported data did not appear to reflect a reasonable distribution of supplement types. In addition, differential absorption rates between supplements and foods prevent the combination of nutrients from supplement types with that from foods consumed. Therefore, all vitamin supplement analyses only considered intake (yes or no). Results of an abbreviated diet questionnaire for comparisons of participants and non-participants, showed that vitamin use among controls adjusted for age, gender, and ethnicity was similar for participants and non-participants (OR = 1.3, 95% CI = 0.91 to 1.82; data not shown). Interestingly, the percent of cases (among all and self-reported cases) that regularly consumed vitamin supplements was significantly lower than that of controls (p < 0.001, table 1). Regardless of study series, a significantly lower percentage of cases (either all or self-reported) than controls reported use of vitamin supplements (p = 0.002 and p = 0.005 for series I and II, respectively; data not shown). For this reason, we also compared case-control nutrient consumption stratified by vitamin supplement use and observed a high degree of overlap among 95% confidence intervals of odds ratios across strata (data not shown).

Age- gender-, ethnicity-, socioeconomic status-, and supplement intake-adjusted odds ratios for quartiles of total calories consumed are shown in table 3. Case-control odds ratios for quartiles of nutrient intakes adjusted for age, gender, ethnicity, socioeconomic status, total calories, and supplement intake are shown in tables 4 and 5. Case-control distributions (= N) for quartiles of nutrient consumption are provided in supplemental table 1 [see Additional file 1]. Examining these results for those that were statistically significant and consistent for all cases versus controls and self-reported cases versus controls, we found that odds ratios for intakes of antioxidant index, carotenoids (alpha- and beta-carotene combined), and the phytoestrogens, daidzein, and matairesinol were reduced (tables 4 and 5). Although not consistent with self-reported cases compared to controls, other notable findings regarding nutrient intake include statistically significant differences among all cases compared to controls, which include consumption of vitamin C, and the phytoestrogens, genistein, formononetin, secoisolariciresinol and coumestrol (table 4). After applying a Bonferroni correction, statistically significant p-values for trend tests were observed for all cases compared to controls among combined series data for antioxidant index (p = 0.002), daidzein (p = 0.003), secoisolariciresinol (p = 0.001) and coumestrol (p = 0.001). Additionally, after adjustment for meat consumption, the only noteworthy changes were for vitamin C consumption. Consumption of vitamin C for cases was no longer significantly different than that of controls after adjusting for meat consumption (OR = 0.77, 95% CI = 0.58 to 1.02, for second quartile compared to lowest; OR = 0.78, 95% CI = 0.59 to 1.05, for third quartile compared to lowest; OR = 0.76, 95% CI = 0.56 to 1.04, for highest quartile compared to lowest; combined series I and II for all cases compared to controls). The effect of adjustment for meat consumption was negligible for all other dietary variables.

<p>Additional File 1</p>

Supplmental_Table1, "Case-control distributions for quartiles of nutrient consumption; San Francisco Bay Area Adult Glioma Study, 1991–2000", 133 KB

Click here for file

 <p>Table 3</p>

Odds ratios for cases versus controls by quartiles of total calories adjusted for age, gender, ethnicity, SES*, and supplement use; San Francisco Bay Area Adult Glioma Study, 1991–2000.†

Quartiles of Total Calories2

Combined Series I & II1

Series I (1991–1995)

Series II (1996–2000)

OR

95% CI3

p4

OR

95% CI3

p4

OR

95% CI3

p4

All case-control

1st

1

--

--

1

--

--

1

--

--

2nd

1.27

0.87

1.84

0.51

1.49

0.87

2.54

0.45

1.07

0.63

1.82

0.93

3rd

1.15

0.70

1.90

1.41

0.69

2.88

0.93

0.46

1.90

4th

1.47

0.72

3.02

1.79

0.65

4.97

1.21

0.43

3.39

Self-reported case-control

1st

1

--

--

1

--

--

1

--

--

2nd

1.22

0.79

1.87

0.74

1.62

0.84

3.11

0.53

0.98

0.55

1.76

0.83

3rd

1.11

0.62

1.99

1.59

0.66

3.83

0.82

0.37

1.80

4th

1.30

0.56

2.99

1.79

0.51

6.23

1.03

0.32

3.26

*Categories for socio-economic status (SES) combine highest education attained and/or household income range. †Case-control distributions (= N) for quartiles of nutrient consumption are provided in supplemental table 1 [see Additional file 1]. 1Also adjusted for study series.2Quartile cutpoints were calculated from series- and gender-specific nutrient intake distributions among controls. 3CI = Confidence interval. 4 p-value for chi-square test for trend.

 <p>Table 4</p>

Odds ratios for all glioma cases versus controls by quartiles of daily nutrient intake adjusted for age, gender, ethnicity, SES*, total calories**, and supplement use; San Francisco Bay Area Adult Glioma Study, 1991–2000.†

Nutrient Quartile2

Combined Series I & II1

Series I (1991–1995)

Series II (1996–2000)

OR

95% CI3

p4

OR

95% CI3

p4

OR

95% CI3

p4

Antioxidant Index

1st

1

--

--

0.002

1

--

--

0.004

1

--

--

0.12

2nd

0.81

0.61

1.07

0.82

0.56

1.20

0.79

0.53

1.20

3rd

0.82

0.62

1.09

0.77

0.52

1.14

0.89

0.59

1.35

4th

0.57

0.42

0.78

0.52

0.33

0.79

0.64

0.40

1.03

Vitamin E

1st

1

--

--

0.45

1

--

--

0.46

1

--

--

0.06

2nd

0.95

0.71

1.27

1.12

0.74

1.69

0.80

0.53

1.22

3rd

0.79

0.57

1.10

1.02

0.64

1.62

0.60

0.37

0.96

4th

0.91

0.62

1.34

1.28

0.75

2.19

0.62

0.35

1.09

Vitamin C

1st

1

--

--

0.02

1

--

--

0.03

1

--

--

0.31

2nd

0.74

0.56

0.97

0.75

0.51

1.09

0.72

0.48

1.09

3rd

0.73

0.55

0.97

0.71

0.48

1.05

0.75

0.49

1.14

4th

0.70

0.51

0.94

0.63

0.41

0.95

0.78

0.50

1.23

Carotenoids5

1st

1

--

--

0.02

1

--

--

0.23

1

--

--

0.03

2nd

0.72

0.54

0.95

0.73

0.49

1.08

0.71

0.48

1.06

3rd

0.84

0.63

1.11

0.93

0.64

1.37

0.74

0.49

1.11

4th

0.65

0.48

0.88

0.70

0.46

1.06

0.60

0.39

0.93

α-Carotene

1st

1

--

--

0.07

1

--

--

0.46

1

--

--

0.07

2nd

0.87

0.66

1.15

1.03

0.70

1.52

0.73

0.49

1.09

3rd

0.83

0.63

1.10

1.06

0.72

1.58

0.64

0.42

0.96

4th

0.76

0.57

1.02

0.83

0.55

1.27

0.71

0.47

1.08

β-Carotene

1st

1

--

--

0.04

1

--

--

0.24

1

--

--

0.09

2nd

0.79

0.60

1.04

0.73

0.49

1.08

0.85

0.58

1.27

3rd

0.79

0.59

1.05

0.86

0.58

1.27

0.70

0.46

1.06

4th

0.72

0.54

0.98

0.73

0.48

1.10

0.72

0.47

1.11

Lutein

1st

1

--

--

0.06

1

--

--

0.05

1

--

--

0.55

2nd

1.02

0.78

1.34

1.17

0.81

1.70

0.86

0.57

1.29

3rd

0.86

0.65

1.14

0.88

0.60

1.30

0.85

0.56

1.28

4th

0.79

0.59

1.05

0.71

0.47

1.07

0.88

0.58

1.33

Lycopene

1st

1

--

--

0.75

1

--

--

0.98

1

--

--

0.69

2nd

0.91

0.69

1.21

0.99

0.67

1.46

0.83

0.55

1.25

3rd

1.01

0.76

1.33

1.05

0.72

1.55

0.96

0.63

1.45

4th

1.02

0.76

1.36

0.98

0.66

1.48

1.05

0.68

1.60

Cryptoxanthin

1st

1

--

--

0.08

1

--