Transcript PowerPoint Template

A STUDY OF DETERMINANTS OF PLASMA
RETINOL AND BETA-CAROTENE
Tutor:
Dr. Kaibo Wang
Applied Statistics, Industrial Engineering, Tsinghua University
Team member:
–
Wang Jun
2009210552
Cui Wen
2009210554
–
Sun Ningning
2009210571
Lv Shikun
2009210566
Outline
I.
INTRODUCTION
II. LITERATURE REWIEW
III. PURPOSE OF THE STUDY
IV. ANALYSYS RESULTS
V. REFERANCE
Page  2
Outline
I.
INTRODUCTION
II. LITERATURE REWIEW
III. PURPOSE OF THE STUDY
IV. ANALYSYS RESULTS
V. REFERANCE
Page  3
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
Page  4
Outline
I.
INTRODUCTION
II. LITERATURE REWIEW
III. PURPOSE OF THE STUDY
IV. ANALYSYS RESULTS
V. REFERANCE
Page  5
LITERATURE REWIEW
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.
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LITERATURE REWIEW
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;
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Outline
I.
INTRODUCTION
II. LITERATURE REWIEW
III. PURPOSE OF THE STUDY
IV. ANALYSYS RESULTS
V. REFERANCE
Page  8
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:
𝑤𝑒𝑖𝑔ℎ𝑡
ℎ𝑒𝑖𝑔ℎ𝑡 2
– 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
I.
INTRODUCTION
II. LITERATURE REWIEW
III. PURPOSE OF THE STUDY
IV. ANALYSYS RESULTS
V. REFERANCE
Page  11
ANALYSYS RESULTS
Content:
1.
Variables Types and Levels
Quantitative variables & Categorical variables
2.
Descriptive Analysis
For all 12 independent variables, with:
Summary Statistics/Histogram/Scatter Plot
3.
Data Analysis via Regression & General Linear Model
3.1 BETA-CAROTENE
3.2 RETINOL
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2.Descriptive Analysis
Variable: SEX
1：Male
2：Female
Plasma Retinol: Male is higher than female
Beta-Carotene: Female is a little higher and more outliers
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2.Descriptive Analysis
Variable: VITUSE(Vitamin use)
1=Yes, fairly often, 2=Yes, not often, 3=No
Plasma Retinol: No much difference, almost in the same level
Beta-Carotene: Often users>Not-often users>Non-users
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2.Descriptive Analysis
Variable: SMOKSTAT(Smoking Status)
1=Never, 2=Former, 3=Current Smoker
Plasma Retinol: Former smokers has the highest level
Beta-Carotene: Never smokers contains higher level
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2.Descriptive Analysis
An example for continuous variables
Age
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Mean
StDev
Min
Q1
Median
Q3
Max
50.146
14.575
19.000
39.000
48.000
63.000
83.000
2.Descriptive Analysis
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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2.Descriptive Analysis
Variable: QUETELET(
)
Standard category from WHO:
Category
BMI range – kg/m2
Severely
underweight
Underweight
less than 16.5
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
Quetelet is a statistical measurement which compares a person's
weight and height.
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2.Descriptive Analysis
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 18
ALCOHOL: 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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2.Descriptive Analysis
Variable: CHOLESTEROL, BETADIET, RETDIET
CHOLESTEROL: milligram of cholesterol consumed per day
BETADIET : microgram of dietary beta-carotene consumed per day
RETDIET : microgram of dietary retinol consumed per day
Most are between 500 and 1500.
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3.1 data analysis about BETA-CAROTENE
AGE
QUETELET
CALORIES
FAT
FIBER
ALCOHOL
CHOLESTEROL
BETADIET
RETDIET
SEX
SMOKSTAT
VITUSE
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Beta-carotene
content in
plasma
1、Regression
2、GLM
3.1.1 data analysis via Regression（BETA-CAROTENE）
Steps of Regression:
1、Check data distribution through scatter plots
2、Best subset and stepwise regression to select predictors
3、Do regression and residual check
4、Do transformation
5、The final model
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3.1.1 data analysis via Regression（BETA-CAROTENE）
1、Check data distribution through scatter plots
transformation can not avoid data aggregations, and therefore delete the outliers
Plasma beta-carotene, Age, Quetelet, CALORIES 的矩阵图
30
60
90
0
2000
4000
1600
800
Plasma beta-carotene
0
90
60
Age
30
50
35
Quetelet
20
4000
2000
CALORIES
0
0
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800
1600
20
35
50
3.1.1 data analysis via Regression（BETA-CAROTENE）
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 :
Page  24
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
3.1.1 data analysis via Regression（BETA-CAROTENE）
3、 Do regression and residual check
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3.1.1 data analysis via Regression（BETA-CAROTENE）
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
0.000025
0.001
-0.124
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
Vitamin_st
atus_3
Smoking_
status_3
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3.1.1 data analysis via Regression（BETA-CAROTENE）
 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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3.1.2 data analysis via GLM（BETA-CAROTENE）
Steps of GLM:
1、Check data distribution through scatter plots
2、Select predictors by trial
3、GLM model
4、Residual check
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3.1.2 data analysis via GLM（BETA-CAROTENE）
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
0.002224
QUETLET
0.000
-0.014010
CALORIES
0.385
-0.000082
FAT
0.758
0.000447
FIBER
0.139
0.00818
ALCOHOL
0.750
0.001381
CHOLESTEROL
0.603
-0.000109
BETADIET
0.111
0.000021
RETDIET
0.337
0.000033
Vitamin_1
0.027
0.000034
Vitamin_2
0.858
0.000003
BETADIET*Vitami
n
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3.1.2 data analysis via GLM（BETA-CAROTENE）
3、GLM model
Log (plasma beta-carotene) =2.3061+0.002224 AGE0.014010QUETLET+0.00818FIBER+0.000021BETADIE
T+0.000034BETADIET*Vitamin_1
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3.1.2 data analysis via GLM（BETA-CAROTENE）
4、Residual check
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3.1 data analysis about BETA-CAROTENE
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
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.
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3.2.1 data analysis via Regression（ RETINOL ）
Steps of Regression:
1、Check data distribution through scatter plots
2、Best subset and stepwise regression (3 methods) to select predictors
3、Do regression and residual check
4、Draw conclusion
Page  33
3.2.1 data analysis via Regression（ RETINOL ）
1、Check data distribution through scatter plots
Page  34
3.2.1 data analysis via Regression（ RETINOL ）
1、Check data distribution through scatter plots
Page  35
3.2.1 data analysis via Regression（ RETINOL ）
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.
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SMOKSTAT
SMOK_1
SMOK_2
1
0
0
2
0
1
3
1
0
VITUSE
VITUSE _1
VITUSE _2
1
0
0
2
0
1
3
1
0
3.2.1 data analysis via Regression（ RETINOL ）
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 :
Page  37
Variables
T-Value
P-value
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
3.2.1 data analysis via Regression（ RETINOL ）
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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3.2.2 data analysis via GLM（RETINAL）
Steps of GLM:
1、Select interaction predictors by trial
2、GLM model
3、Residual check
Page  39
3.2.2 data analysis via GLM（RETINAL）
1、Select predictors by trial
Finally find no interaction predictor.
Variables
T-Value
P-value
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%
Page  40
3.2.2 data analysis via GLM（RETINAL）
3、GLM model
RETPLASMA=510.86+1.8777AGE+5.002ALCOHOL-0.013507BETADIET
Page  41
3.2.2 data analysis via GLM（RETINAL）
4、Residual check
Re si du a l Pl ot s f or RET P LA SM A
Normal Probability Plot
Versus Fits
99
400
90
200
Re sidu al
Perc ent
99.9
50
10
0
-200
1
-400
400
0.1
-500
-250
0
Residual
250
500
500
Histogram
400
30
Re sid ual
Freq uen cy
700
Versus Order
40
20
10
200
0
-200
-400
0
-300 -200 -100
Page  42
600
Fitted Value
0
100
Residual
200
300
400
1
20 40
60
80 100 120 140 160 180 200 220 240 260
Observation Order
800
3.2.2 data analysis via GLM（RETINAL）
Regression:
RETPLASMA = 517 + 2.09 AGE +5.228 ALCOHOL- 0.0149 BETADIET- 71.7
SEX_F + 41.9 SMOK_2 - 43.4 VITUSE_1
GLM:
RETPLASMA=510.86+1.8777AGE+5.002ALCOHOL-0.013507BETADIET
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.
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3.2.2 data analysis via GLM（RETINAL）
Regression:
RETPLASMA = 517 + 2.09 AGE +5.228 ALCOHOL- 0.0149 BETADIET- 71.7
SEX_F + 41.9 SMOK_2 - 43.4 VITUSE_1
GLM:
RETPLASMA=510.86+1.8777AGE+5.002ALCOHOL-0.013507BETADIET
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 .
Page  44
Discussion
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.
Page  45
Outline
I.
INTRODUCTION
II. LITERATURE REWIEW
III. PURPOSE OF THE STUDY
IV. ANALYSYS RESULTS
V. REFERANCE
Page  46
REFERANCE
 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.
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REFERANCE
 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.
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