A STUDY OF DETERMINANTS OF PLASMA RETINOL AND BETA-CAROTENE

A STUDY OF DETERMINANTS OF PLASMA RETINOL AND BETA-CAROTENE

Tutor: Dr. Kaibo WangApplied Statistics, Industrial Engineering, Tsinghua UniversityTeam member:

– Wang Jun 2009210552 Cui Wen 2009210554– Sun Ningning 2009210571 Lv Shikun 2009210566

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I. INTRODUCTIONII. LITERATURE REWIEWIII. PURPOSE OF THE STUDYIV. ANALYSYS RESULTSV. REFERANCE

Outline

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Outline

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INTRODUCTION

Past research: low dietary intake or low plasma concentrations of retinol, beta-carotene, or other carotenoids might be associated with increased risk of developing certain types of cancer.

Cross-sectional study: to investigate the relationship between personal characteristics and dietary factors, and plasma concentrations of retinol, beta-carotene and other carotenoids.

Experimernt: – N=315– Patients:

• Had an elective surgical procedure during a three-year period• Removed a lesion of the lung, colon, breast, skin, ovary or uterus• Non-cancerous

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Outline

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1 、 Observational studies have suggested that low dietary intake or low plasma

concentrations of retinol, beta-carotene, or other carotenoids might be

associated with increased risk of developing certain types of cancer ;

2 、 The relationship between plasma carotenoids, plasma cholesterol, cigarette

smoking, vitamin supplement use, and intakes of alcohol, vitamin A, and

carotene were investigated in 1981 by in the research of Russell-Briefel R ;

3 、 The relationship of diet and nutritional supplements, cigarette use, alcohol

consumption, and blood lipids to plasma levels of beta-carotene was studied

among 330 men and women aged 18–79 years in the research of Stryker WS.

LITERATURE REWIEW

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1 、 Many epidemiologic studies have been conducted primarily as dietary studies

of vitamin A and carotene, or as blood studies of serum retinol.

2 、 Willett WC showed that, with higher levels of retinol plasma, the risks of get

cancer may be decreased. However, plasma retinol levels are under strict

control and a high intake of preformed vitamin does not seem to be relevant

for cancer prevention;

3 、 Stähelin, H. B. suggested an inverse relationship between vitamin A and

cancer risk, although some studies have found no relationship. Then people

find that a lower retinol levels is not the cause of an invasive cancer. Instead,

it is the cancer that brings about a lower retinol level in human body;

LITERATURE REWIEW

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Outline

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PURPOSE OF THE STUDY

Tofind out internal factors which may have some effect or relationship with the beta-carotene and retinol in people’s plasma.

– Age (years)– Quetelet: – Number of calories consumed per day.– Grams of fat consumed per day.– Grams of fiber consumed per day.– Number of alcoholic drinks consumed per week.– Cholesterol consumed (mg per day).– Dietary beta-carotene consumed (mcg per day).– Dietary retinol consumed (mcg per day)– Sex (1=Male, 2=Female).– Smoking status (1=Never, 2=Former, 3=Current Smoker)– Vitamin Use (1=Yes, fairly often, 2=Yes, not often, 3=No)

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PURPOSE OF THE STUDY

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Outline

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Content:1. Variables Types and Levels Quantitative variables & Categorical variables2. Descriptive Analysis For all 12 independent variables, with: Summary Statistics/Histogram/Scatter Plot3. Data Analysis via Regression & General Linear Model 3.1 BETA-CAROTENE 3.2 RETINOL

ANALYSYS RESULTS

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Variable: SEX

1 ： Male 2 ： FemalePlasma Retinol: Male is higher than femaleBeta-Carotene: Female is a little higher and more outliers

2.Descriptive Analysis

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Variable: VITUSE(Vitamin use)

1=Yes, fairly often, 2=Yes, not often, 3=NoPlasma Retinol: No much difference, almost in the same levelBeta-Carotene: Often users>Not-often users>Non-users


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Variable: SMOKSTAT(Smoking Status)

1=Never, 2=Former, 3=Current SmokerPlasma Retinol: Former smokers has the highest levelBeta-Carotene: Never smokers contains higher level


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An example for continuous variables


Mean StDev Min Q1 Median Q3 Max

Age 50.146 14.575 19.000 39.000 48.000 63.000 83.000

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Variable : AGE, QUETELET , CALORIES

AGE(age): Most in the area between 32 and 77who are basically middle-age or elderly people.

QUETELET( ): Most between 18.5 and 30 who are normal and some are a little overweight.

CALORIES(calories): Most are concentrated between 1000 and 2200.


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Variable: QUETELET( )Standard category from WHO:

Quetelet is a statistical measurement which compares a person's weight and height.


Category BMI range – kg/m2

Severely underweight

less than 16.5

Underweight from 16.5 to 18.4

Normal from 18.5 to 24.9

Overweight from 25 to 30

Obese Class I from 30.1 to 34.9

Obese Class II from 35 to 40

Obese Class III over 40

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Variable: FAT, FIBER, ALCOHOL

FAT: Grams of fat consumed per day. Most are between 45 and 135.FIBER: Grams of fiber consumed per day. Between 6 and 18ALCOHOL: Number of alcoholic drinks consumed per week. Most rarely

drink, but there is an obvious outlier, which reaches 203 alcohol per week.


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Variable: CHOLESTEROL, BETADIET, RETDIET

CHOLESTEROL: milligram of cholesterol consumed per dayBETADIET : microgram of dietary beta-carotene consumed per dayRETDIET : microgram of dietary retinol consumed per day Most are between 500 and 1500.


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3.1 data analysis about BETA-CAROTENE

AGEQUETELETCALORIES

FATFIBER

ALCOHOLCHOLESTEROL

BETADIETRETDIET

SEXSMOKSTAT

VITUSE

Beta-carotene content in

plasma1 、 Regression2 、 GLM

？

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Steps of Regression:1 、 Check data distribution through scatter plots 2 、 Best subset and stepwise regression to select predictors3 、 Do regression and residual check4 、 Do transformation5 、 The final model

3.1.1 data analysis via Regression （ BETA-CAROTENE ）

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1 、 Check data distribution through scatter plots transformation can not avoid data aggregations, and therefore delete the outliers

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Pl asma beta- carotene, Age, Quetel et, CALORI ES 的矩阵图


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2 、 Use best subset and stepwise regression to select predictors Use dummy variables to take place of discreet variables:

SEX, SMOKSTAT and VITUSE Result of stepwise regression :

Variables T-Value P-value

QUETLET -4.11 0.000

BETADIET 3.57 0.000

Vitamin_status_3 -3.17 0.002

Smoking_status_3 -2.04 0.042

FAT -1.88 0.061


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3 、 Do regression and residual check


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4 、 Do transformation use log (plasma beta-carotene) to replace plasma beta-carotene Redo step1—step3

Variables P-value coefficient

QUETLET 0.000 -0.0140

BETADIET 0.054 0.000025Vitamin_status_3 0.001 -0.124

Smoking_status_3 0.023 -0.116

FAT 0.048 -0.00113

AGE 0.046 0.00248

Sex_2 0.085 0.0934

FIBER 0.132 0.00632


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5 、 The final model

Log (plasma beta-carotene) = 2.32 - 0.0140QUETLET -

0.124vitamin_status_3- 0.116 smoking_status_3 +

0.000025 BETADIET - 0.00113 FAT+ 0.00248 AGE+

0.0934 sex_2 + 0.00632 FIBER


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Steps of GLM:1 、 Check data distribution through scatter plots 2 、 Select predictors by trial3 、 GLM model4 、 Residual check

3.1.2 data analysis via GLM （ BETA-CAROTENE ）

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1 、 Check data distribution through scatter plots similar to step 1 of regression

2 、 Select predictors by trial Variables P-value coefficient

AGE 0.090 0.002224QUETLET 0.000 -0.014010

CALORIES 0.385 -0.000082FAT 0.758 0.000447

FIBER 0.139 0.00818ALCOHOL 0.750 0.001381

CHOLESTEROL 0.603 -0.000109BETADIET 0.111 0.000021RETDIET 0.337 0.000033BETADIET*Vitami

n

Vitamin_1 0.027 0.000034Vitamin_2 0.858 0.000003


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3 、 GLM model

Log (plasma beta-carotene) =2.3061+0.002224 AGE-

0.014010QUETLET+0.00818FIBER+0.000021BETADIE

T+0.000034BETADIET*Vitamin_1


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4 、 Residual check


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Conclusion :

1 、 The coefficient of QUETLET, vitamin_status_3, smoking_status_3 and FAT are

negative, which indicates that with the increase of these variables, there would

be a decrease of the content of beta-carotene in plasma;

2 、 The coefficient of BETADIET, AGE, Sex_2 and FIBER are positive, which

indicates that with the increase of average number of these variables, there

would also be an increase of the content of beta-carotene in plasma.

Log (plasma beta-carotene) = 2.32 - 0.0140QUETLET -0.124vitamin_status_3- 0.116 smoking_status_3 + 0.000025 BETADIET - 0.00113 FAT+ 0.00248 AGE+ 0.0934 sex_2 + 0.00632 FIBER

3.1 data analysis about BETA-CAROTENE

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Steps of Regression:1 、 Check data distribution through scatter plots 2 、 Best subset and stepwise regression (3 methods) to select predictors3 、 Do regression and residual check4 、 Draw conclusion

3.2.1 data analysis via Regression （ RETINOL ）

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1 、 Check data distribution through scatter plots


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1 、 Check data distribution through scatter plots


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2 、 Use best subset and stepwise regression to select predictors Using dummy variables to transform the Categorical variables

Define SEX_F=SEX-1, so SEX_F=1, when SEX=Female; SEX_F=0, when SEX=Male.


SMOKSTAT SMOK_1 SMOK_21 0 02 0 13 1 0

VITUSE VITUSE _1 VITUSE _21 0 02 0 13 1 0

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2 、 Use best subset and stepwise regression to select predictors Select 7 variables : AGE, QUETELET, ALCOHOL, BETADIET, SEX_F, SMOK_2, and VITUSE_1 Result of stepwise regression :



AGE 3.32 0.002

QUETELET 1.72 0.295

ALCOHOL 3.24 0.053

BETADIET -2.04 0.031

SEX_F -1.97 0.027

SMOK_2 1.70 0.035

VITUSE_1 -2.95 0.033

R-sq.= 13.55; R-Sq.(adj)=11.42

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2 、 Use best subset and stepwise regression to select predictors The model is :


RETPLASMA = 517 + 2.09 AGE - 0.0149 BETADIET+5.228 ALCOHOL- 71.7 SEX_F + 41.9 SMOK_2 - 43.4 VITUSE_1

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Steps of GLM:1 、 Select interaction predictors by trial2 、 GLM model3 、 Residual check

3.2.2 data analysis via GLM （ RETINAL ）

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1 、 Select predictors by trial Finally find no interaction predictor.



Constant 6.57 0.000

AGE 2.50 0.013

QUETLET 0.90 0.370

CALORIES -0.70 0.486

FAT -1.43 0.153

FIBER -0.79 0.428

ALCOHOL 1.77 0.079

CHOLESTEROL 0.92 0.360

BETADIET -1.66 0.097

RETDIET 0.40 0.688

R-sq.= 14.75%; R-Sq.(adj)= 9.52%

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3 、 GLM model


RETPLASMA=510.86+1.8777AGE+5.002ALCOHOL-0.013507BETADIET

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4 、 Residual check


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Conclusion :

1. The coefficient of AGE is positive in both models, indicating that as people get

older, the plasma retinal level will raise.

2. Both model shows that people drink more wine will have higher plasma retinal

level. But the data of ALCOHOL is almost all less than 10, so its influence is

not obivous.


RETPLASMA = 517 + 2.09 AGE +5.228 ALCOHOL- 0.0149 BETADIET- 71.7 SEX_F + 41.9 SMOK_2 - 43.4 VITUSE_1


Regression:

GLM:

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RETPLASMA = 517 + 2.09 AGE +5.228 ALCOHOL- 0.0149 BETADIET- 71.7 SEX_F + 41.9 SMOK_2 - 43.4 VITUSE_1


Regression:

GLM:

Conclusion :

3. The coefficient of BETADIET is negative in both models, which means that

people consuming more beta-carotene have lower level of plasma retinal. So

balance of different vitamin is very important.

4. The coefficient of 3 dummy variables in regression model is -71.7, 41.9 and -

43.4, indicating women’s average plasma retinal level is lower than men’s.

People who are former smokers or never use vitamin have lower plasma retinal

level .

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We conclude that there is wide variability in plasma concentrations

of these micronutrients in humans, and that much of this variability is

associated with dietary habits and personal characteristics. A better

understanding of the physiological relationship between some personal

characteristics and plasma concentrations of these micronutrients will

require further study.

Discussion

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Outline

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Peto R, Doll R, Buckley JD, et al. Can dietary beta-carotene materially reduce human cancer rates? Nature 1981;290:201-8.

Russell-Briefel R, Bates MW, Kuller LH. The relationship of plasma carotenoids to health andbiochemical factors in middle-aged men. Am J Epidemiol 1986;122:741-9.

Stryker WS, Kaplan LA, Stein EA, et al. The relation of diet, cigarette smoking, and alcohol consumption to plasma beta-carotene and alphatocopherol levels. Am J Epidemiol 1988;127:283- 96.

Adams-Campbell, L. L., M. U. Nwankwo, et al. (1992). Serum retinol, carotenoids, vitamin E, and cholesterol in Nigerian women. Nutritional Biochemistry 3(2): 58-61.

REFERANCE

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Comstock, G. W., M. S. Menkes, et al. (1988). Serum levels of retinol, beta-carotene, and alpha-tocopherol in older adults.American Journal of Epidemiology 127(1): 114-123.

Russellbriefel, R., M. W. Bates, et al. (1985). The relationship of plasma carotenoids to health and biohchemical factors in middle-aged men. American Journal of Epidemiology 122(5): 741-749.

Stähelin, H. B., E. Buess, et al. (1982). vitamin A, cardiovascular risk factors, and mortality. The Lancet 319(8268): 394-395.

Van Poppel, G. and H. van den Berg (1997). Vitamins and cancer. Cancer Letters 114(1-2): 195-202.

REFERANCE

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Thank YouFor

For Attention

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A STUDY OF DETERMINANTS OF PLASMA RETINOL AND BETA-CAROTENE