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. Page 6 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; Page 7 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) Page 9 PURPOSE OF THE STUDY Page 10 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 Page 12 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 Page 13 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 Page 14 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 Page 15 2.Descriptive Analysis An example for continuous variables Age Page 16 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. Page 17 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. Page 18 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. Page 19 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. Page 20 3.1 data analysis about BETA-CAROTENE AGE QUETELET CALORIES FAT FIBER ALCOHOL CHOLESTEROL BETADIET RETDIET SEX SMOKSTAT VITUSE Page 21 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 Page 22 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 Page 23 800 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 Page 25 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 Page 26 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 Page 27 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 Page 28 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 Page 29 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 Page 30 3.1.2 data analysis via GLM(BETA-CAROTENE) 4、Residual check Page 31 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. Page 32 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. Page 36 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 Page 38 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 al P lot s fo r RE TP LA SM A Normal Probability Plot Versus Fits 99 400 90 200 Re si dua l P erc en t 99.9 50 10 0 -200 1 -400 400 0.1 -500 -250 0 R esi dua l 250 500 500 40 400 30 200 20 10 0 Page 42 700 Versus Order Res id ual F re que nc y Histogram 600 F itt ed V alu e 0 -200 -400 -300 -200 -100 0 100 R esi dua l 200 300 400 1 20 40 60 80 100 120 140 160 180 200 220 240 260 O bse rva tio n O rde r 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. Page 43 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. Page 47 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. Page 48 Page 49