R Notebook

library("haven")

警告： 套件 ‘haven’ 是用 R 版本 4.3.3 來建造的

library("tidyverse")

警告： 套件 ‘tidyverse’ 是用 R 版本 4.3.3 來建造的警告： 套件 ‘tidyr’ 是用 R 版本 4.3.3 來建造的警告： 套件 ‘readr’ 是用 R 版本 4.3.3 來建造的警告： 套件 ‘purrr’ 是用 R 版本 4.3.3 來建造的警告： 套件 ‘forcats’ 是用 R 版本 4.3.3 來建造的警告： 套件 ‘lubridate’ 是用 R 版本 4.3.3 來建造的── Attaching core tidyverse packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::group_rows() masks kableExtra::group_rows()
✖ dplyr::lag()        masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library("dplyr")
library("foreign")
library("survey")

警告： 套件 ‘survey’ 是用 R 版本 4.3.3 來建造的載入需要的套件：grid
載入需要的套件：Matrix

載入套件：‘Matrix’

下列物件被遮斷自 ‘package:tidyr’:

    expand, pack, unpack

載入需要的套件：survival

載入套件：‘survival’

下列物件被遮斷由 ‘.GlobalEnv’:

    cancer


載入套件：‘survey’

下列物件被遮斷自 ‘package:graphics’:

    dotchart

library("ggplot2")
library("car")

警告： 套件 ‘car’ 是用 R 版本 4.3.3 來建造的載入需要的套件：carData
警告： 套件 ‘carData’ 是用 R 版本 4.3.3 來建造的
載入套件：‘car’

下列物件被遮斷自 ‘package:dplyr’:

    recode

下列物件被遮斷自 ‘package:purrr’:

    some

library("rms")

警告： 套件 ‘rms’ 是用 R 版本 4.3.3 來建造的載入需要的套件：Hmisc
警告： 套件 ‘Hmisc’ 是用 R 版本 4.3.3 來建造的Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

載入套件：‘Hmisc’

下列物件被遮斷自 ‘package:survey’:

    deff

下列物件被遮斷自 ‘package:dplyr’:

    src, summarize

下列物件被遮斷自 ‘package:base’:

    format.pval, units

警告： "replValueSp" 類別的子類別 "ndiMatrix" 沒有定義；因此沒有更新
載入套件：‘rms’

下列物件被遮斷自 ‘package:car’:

    Predict, vif

下列物件被遮斷自 ‘package:survey’:

    calibrate

library("SciViews")

#list variable

colnames(Fulldat_mediation_pfas)

 [1] "SEQN"                                  "chronological_age"                     "Gender"                                "Race"                                 
 [5] "Pregnancy"                             "Marital_Status"                        "Ratio_income_poverty"                  "Interview_Weight"                     
 [9] "MEC_Weight"                            "psu"                                   "Strata"                                "BMI"                                  
[13] "Vitamin_A"                             "Vitamin_C"                             "Vitamin_E"                             "Zinc"                                 
[17] "Selenium"                              "sleep_disorders"                       "Smoked_days"                           "now_smoke"                            
[21] "quit_smoking"                          "Avg_alcohol_drinks"                    "equipment_walk"                        "walk_difficulty"                      
[25] "had_cancer"                            "weight_2"                              "Perfluorohexane_sulfonic_acid"         "Perfluorohexane_sulfonic_acid_comment"
[29] "Perfluorononanoic_acid"                "Perfluorononanoic_acid_comment"        "perfluorooctanoic_acid"                "perfluorooctanoic_acid_comment"       
[33] "perfluorooctane_sulfonic_acid"         "perfluorooctane_sulfonic_acid_comment" "White_blood_cell_count"                "Lymphocyte_percent"                   
[37] "Red_cell_distribution_width"           "Mean_cell_volume"                      "Albumin"                               "Creatinine"                           
[41] "Glucose_serum"                         "Alkaline_phosphotase"                  "xb"                                    "Phenotypic_Age"                       
[45] "cate_age"                              "age_binary"                            "BMI_cat"                               "income_cat"                           
[49] "triglycerides"                         "fastglucose"                           "TriGlu_BMI"                            "pfas_comment"

#Examine the pfas and Phenotypic_Age

```r
ggplot(Fulldat_Pheno, aes(x = Perfluorohexane_sulfonic_acid)) +
  geom_histogram(binwidth = 100, color = \skyblue\, fill = \red\, alpha = 0.7) +
  labs(title = \Distribution of Perfluorohexane_sulfonic_acid\,
       x = \Perfluorohexane_sulfonic_acid\,
       y = \Frequency\) +
  theme_minimal()

ggplot(Fulldat_Pheno, aes(x = Perfluorohexane_sulfonic_acid, y = Phenotypic_Age)) +
  geom_point() +
  geom_smooth(method = \lm\, se = FALSE) + 
  labs(x = \Perfluorohexane_sulfonic_acid\, y = \Phenotypic_Age\, title = \Scatter Plot Perfluorohexane_sulfonic_acid vs Phenotypic_Age with Regression Line\)

# Define breaks for age groups
breaks <- c(20, 45, 65, Inf)  # Breaks represent the age boundaries

# Define labels for the age groups
labels <- c(\1\, \2\, \3\)

# Categorize chronological_age into groups and assign custom labels
Fulldat_Pheno$cate_age <- cut(Fulldat_Pheno$chronological_age, breaks = breaks, labels = labels, include.lowest = TRUE)


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#sample density curves of pfas concentrations among accelerated and delayed age

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<!-- rnb-source-begin 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 -->

```r
```r
Fulldat_Pheno <- Fulldat_Pheno %>% mutate(age_binary = case_when(
  Phenotypic_Age-chronological_age >= 0 ~\accelerated\,
  Phenotypic_Age-chronological_age < 0 ~\delayed\
))

library(ggplot2)
cols <- c(\#F76D5E\, \#72D8FF\)

#Perfluorohexane_sulfonic_acid
ggplot(Fulldat_Pheno, aes(x = Perfluorohexane_sulfonic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)


#Perfluorohexane_sulfonic_acid_comment
ggplot(Fulldat_Pheno, aes(x = Perfluorononanoic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)


#Perfluorononanoic_acid
ggplot(Fulldat_Pheno, aes(x = perfluorooctanoic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)

#Perfluorononanoic_acid_comment
ggplot(Fulldat_Pheno, aes(x = perfluorooctane_sulfonic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)


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#Main model of regression for association, and adjust for covariates (Table 2)
#Perfluorohexane_sulfonic_acid

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<!-- rnb-output-begin {"data":"\nCall:\nsvyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                      Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                            45.4975     0.2657 171.230   <2e-16 ***\nPerfluorohexane_sulfonic_acid_comment   5.2534     2.1305   2.466   0.0149 *  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 377.0007)\n\nNumber of Fisher Scoring iterations: 2\n\n                                          2.5 %    97.5 %\n(Intercept)                           44.971890 46.023018\nPerfluorohexane_sulfonic_acid_comment  1.039329  9.467451\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                      Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                            50.2773     1.4506  34.660  < 2e-16 ***\nPerfluorohexane_sulfonic_acid_comment   5.9817     2.2028   2.716  0.00752 ** \nGender                                 -0.3176     0.4117  -0.771  0.44188    \nRace                                    1.2279     0.1857   6.613 9.05e-10 ***\nMarital_Status                         -2.9136     0.3698  -7.879 1.20e-12 ***\nRatio_income_poverty                   -0.3267     0.1731  -1.887  0.06146 .  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 336.8121)\n\nNumber of Fisher Scoring iterations: 2\n\n                                           2.5 %      97.5 %\n(Intercept)                           47.4072877 53.14741148\nPerfluorohexane_sulfonic_acid_comment  1.6234305 10.33995224\nGender                                -1.1321702  0.49698242\nRace                                   0.8605062  1.59519676\nMarital_Status                        -3.6452647 -2.18194568\nRatio_income_poverty                  -0.6692319  0.01591455\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                                        Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                            7.853e+01  5.157e+00  15.227  < 2e-16 ***\nPerfluorohexane_sulfonic_acid_comment  1.475e+01  3.398e+00   4.342 3.56e-05 ***\nGender                                -9.155e-01  9.737e-01  -0.940  0.34950    \nRace                                   1.482e+00  4.314e-01   3.436  0.00088 ***\nMarital_Status                        -3.117e+00  3.603e-01  -8.650 1.36e-13 ***\nRatio_income_poverty                  -1.089e+00  3.932e-01  -2.770  0.00675 ** \nBMI                                    2.014e-01  7.634e-02   2.638  0.00977 ** \nsleep_disorders                       -2.740e+00  1.133e+00  -2.418  0.01755 *  \nquit_smoking                           3.096e-04  2.797e-05  11.069  < 2e-16 ***\nAvg_alcohol_drinks                     5.775e+00  1.210e+00   4.772 6.67e-06 ***\nhad_cancer                            -1.480e+01  1.597e+00  -9.266 6.68e-15 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 305.0519)\n\nNumber of Fisher Scoring iterations: 2\n\n                                              2.5 %        97.5 %\n(Intercept)                            6.828635e+01  8.876488e+01\nPerfluorohexane_sulfonic_acid_comment  8.005694e+00  2.149824e+01\nGender                                -2.848896e+00  1.017804e+00\nRace                                   6.258390e-01  2.338989e+00\nMarital_Status                        -3.832281e+00 -2.401372e+00\nRatio_income_poverty                  -1.869679e+00 -3.084527e-01\nBMI                                    4.978659e-02  3.529389e-01\nsleep_disorders                       -4.989964e+00 -4.897551e-01\nquit_smoking                           2.540562e-04  3.651262e-04\nAvg_alcohol_drinks                     3.371952e+00  8.177537e+00\nhad_cancer                            -1.796713e+01 -1.162605e+01\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid), \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                  Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                        44.9501     0.2767 162.427  < 2e-16 ***\nln(Perfluorohexane_sulfonic_acid)   1.7040     0.2593   6.571 1.04e-09 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 374.8053)\n\nNumber of Fisher Scoring iterations: 2\n\n                                      2.5 %   97.5 %\n(Intercept)                       44.402761 45.49753\nln(Perfluorohexane_sulfonic_acid)  1.191031  2.21689\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                  Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                        48.6123     1.4635  33.217  < 2e-16 ***\nln(Perfluorohexane_sulfonic_acid)   1.9230     0.2694   7.139 6.09e-11 ***\nGender                              0.7550     0.4463   1.692   0.0931 .  \nRace                                1.1880     0.1755   6.770 4.09e-10 ***\nMarital_Status                     -2.9354     0.3712  -7.907 1.03e-12 ***\nRatio_income_poverty               -0.4698     0.1749  -2.685   0.0082 ** \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 334.2883)\n\nNumber of Fisher Scoring iterations: 2\n\n                                       2.5 %     97.5 %\n(Intercept)                       45.7168177 51.5078710\nln(Perfluorohexane_sulfonic_acid)  1.3901110  2.4559776\nGender                            -0.1279973  1.6380246\nRace                               0.8408182  1.5352174\nMarital_Status                    -3.6699243 -2.2009383\nRatio_income_poverty              -0.8158850 -0.1236473\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                                    Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                        7.556e+01  5.515e+00  13.700  < 2e-16 ***\nln(Perfluorohexane_sulfonic_acid)  2.772e+00  7.354e-01   3.769 0.000286 ***\nGender                             4.531e-01  1.109e+00   0.409 0.683827    \nRace                               1.337e+00  4.190e-01   3.191 0.001927 ** \nMarital_Status                    -3.082e+00  3.570e-01  -8.634 1.47e-13 ***\nRatio_income_poverty              -1.201e+00  3.827e-01  -3.139 0.002262 ** \nBMI                                2.214e-01  7.540e-02   2.936 0.004183 ** \nsleep_disorders                   -2.985e+00  1.123e+00  -2.659 0.009211 ** \nquit_smoking                       2.993e-04  2.624e-05  11.410  < 2e-16 ***\nAvg_alcohol_drinks                 5.968e+00  1.246e+00   4.789 6.25e-06 ***\nhad_cancer                        -1.467e+01  1.642e+00  -8.937 3.35e-14 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 301.3908)\n\nNumber of Fisher Scoring iterations: 2\n\n                                          2.5 %        97.5 %\n(Intercept)                        6.460681e+01  8.650730e+01\nln(Perfluorohexane_sulfonic_acid)  1.311524e+00  4.231870e+00\nGender                            -1.749331e+00  2.655629e+00\nRace                               5.052188e-01  2.169197e+00\nMarital_Status                    -3.790907e+00 -2.373289e+00\nRatio_income_poverty              -1.961260e+00 -4.415802e-01\nBMI                                7.165011e-02  3.710765e-01\nsleep_disorders                   -5.213988e+00 -7.561097e-01\nquit_smoking                       2.472443e-04  3.514249e-04\nAvg_alcohol_drinks                 3.493657e+00  8.443142e+00\nhad_cancer                        -1.792941e+01 -1.141069e+01\n"} -->

Call: svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.4975 0.2657 171.230 <2e-16 ** Perfluorohexane_sulfonic_acid_comment 5.2534 2.1305 2.466 0.0149
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 377.0007)

Number of Fisher Scoring iterations: 2

                                      2.5 %    97.5 %

(Intercept) 44.971890 46.023018 Perfluorohexane_sulfonic_acid_comment 1.039329 9.467451

Call: svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.2773 1.4506 34.660 < 2e-16 Perfluorohexane_sulfonic_acid_comment 5.9817 2.2028 2.716 0.00752 Gender -0.3176 0.4117 -0.771 0.44188
Race 1.2279 0.1857 6.613 9.05e-10 Marital_Status -2.9136 0.3698 -7.879 1.20e-12 * Ratio_income_poverty -0.3267 0.1731 -1.887 0.06146 .
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 336.8121)

Number of Fisher Scoring iterations: 2

                                       2.5 %      97.5 %

(Intercept) 47.4072877 53.14741148 Perfluorohexane_sulfonic_acid_comment 1.6234305 10.33995224 Gender -1.1321702 0.49698242 Race 0.8605062 1.59519676 Marital_Status -3.6452647 -2.18194568 Ratio_income_poverty -0.6692319 0.01591455

Call: svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.853e+01 5.157e+00 15.227 < 2e-16 Perfluorohexane_sulfonic_acid_comment 1.475e+01 3.398e+00 4.342 3.56e-05 Gender -9.155e-01 9.737e-01 -0.940 0.34950
Race 1.482e+00 4.314e-01 3.436 0.00088 Marital_Status -3.117e+00 3.603e-01 -8.650 1.36e-13 Ratio_income_poverty -1.089e+00 3.932e-01 -2.770 0.00675 ** BMI 2.014e-01 7.634e-02 2.638 0.00977 ** sleep_disorders -2.740e+00 1.133e+00 -2.418 0.01755 *
quit_smoking 3.096e-04 2.797e-05 11.069 < 2e-16 Avg_alcohol_drinks 5.775e+00 1.210e+00 4.772 6.67e-06 had_cancer -1.480e+01 1.597e+00 -9.266 6.68e-15 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 305.0519)

Number of Fisher Scoring iterations: 2

                                          2.5 %        97.5 %

(Intercept) 6.828635e+01 8.876488e+01 Perfluorohexane_sulfonic_acid_comment 8.005694e+00 2.149824e+01 Gender -2.848896e+00 1.017804e+00 Race 6.258390e-01 2.338989e+00 Marital_Status -3.832281e+00 -2.401372e+00 Ratio_income_poverty -1.869679e+00 -3.084527e-01 BMI 4.978659e-02 3.529389e-01 sleep_disorders -4.989964e+00 -4.897551e-01 quit_smoking 2.540562e-04 3.651262e-04 Avg_alcohol_drinks 3.371952e+00 8.177537e+00 had_cancer -1.796713e+01 -1.162605e+01

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid), design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.9501 0.2767 162.427 < 2e-16 ln(Perfluorohexane_sulfonic_acid) 1.7040 0.2593 6.571 1.04e-09 — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 374.8053)

Number of Fisher Scoring iterations: 2

                                  2.5 %   97.5 %

(Intercept) 44.402761 45.49753 ln(Perfluorohexane_sulfonic_acid) 1.191031 2.21689

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.6123 1.4635 33.217 < 2e-16 ln(Perfluorohexane_sulfonic_acid) 1.9230 0.2694 7.139 6.09e-11 Gender 0.7550 0.4463 1.692 0.0931 .
Race 1.1880 0.1755 6.770 4.09e-10 Marital_Status -2.9354 0.3712 -7.907 1.03e-12 Ratio_income_poverty -0.4698 0.1749 -2.685 0.0082 ** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 334.2883)

Number of Fisher Scoring iterations: 2

                                   2.5 %     97.5 %

(Intercept) 45.7168177 51.5078710 ln(Perfluorohexane_sulfonic_acid) 1.3901110 2.4559776 Gender -0.1279973 1.6380246 Race 0.8408182 1.5352174 Marital_Status -3.6699243 -2.2009383 Ratio_income_poverty -0.8158850 -0.1236473

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.556e+01 5.515e+00 13.700 < 2e-16 ln(Perfluorohexane_sulfonic_acid) 2.772e+00 7.354e-01 3.769 0.000286 Gender 4.531e-01 1.109e+00 0.409 0.683827
Race 1.337e+00 4.190e-01 3.191 0.001927 ** Marital_Status -3.082e+00 3.570e-01 -8.634 1.47e-13 Ratio_income_poverty -1.201e+00 3.827e-01 -3.139 0.002262 BMI 2.214e-01 7.540e-02 2.936 0.004183 sleep_disorders -2.985e+00 1.123e+00 -2.659 0.009211 quit_smoking 2.993e-04 2.624e-05 11.410 < 2e-16 Avg_alcohol_drinks 5.968e+00 1.246e+00 4.789 6.25e-06 had_cancer -1.467e+01 1.642e+00 -8.937 3.35e-14 ** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 301.3908)

Number of Fisher Scoring iterations: 2

                                      2.5 %        97.5 %

(Intercept) 6.460681e+01 8.650730e+01 ln(Perfluorohexane_sulfonic_acid) 1.311524e+00 4.231870e+00 Gender -1.749331e+00 2.655629e+00 Race 5.052188e-01 2.169197e+00 Marital_Status -3.790907e+00 -2.373289e+00 Ratio_income_poverty -1.961260e+00 -4.415802e-01 BMI 7.165011e-02 3.710765e-01 sleep_disorders -5.213988e+00 -7.561097e-01 quit_smoking 2.472443e-04 3.514249e-04 Avg_alcohol_drinks 3.493657e+00 8.443142e+00 had_cancer -1.792941e+01 -1.141069e+01




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#"Perfluorononanoic_acid"  "Perfluorononanoic_acid_comment"  

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<!-- rnb-output-begin {"data":"\nCall:\nsvyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                               Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                     45.5414     0.2615 174.168   <2e-16 ***\nPerfluorononanoic_acid_comment   1.2082     2.5944   0.466    0.642    \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 377.3081)\n\nNumber of Fisher Scoring iterations: 2\n\n                                   2.5 %    97.5 %\n(Intercept)                    45.024247 46.058640\nPerfluorononanoic_acid_comment -3.923447  6.339837\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                               Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                     50.4123     1.4495  34.780  < 2e-16 ***\nPerfluorononanoic_acid_comment  -0.1223     2.5110  -0.049   0.9612    \nGender                          -0.2958     0.4101  -0.721   0.4720    \nRace                             1.2229     0.1839   6.650 7.54e-10 ***\nMarital_Status                  -2.9142     0.3698  -7.881 1.18e-12 ***\nRatio_income_poverty            -0.3530     0.1733  -2.037   0.0437 *  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 337.238)\n\nNumber of Fisher Scoring iterations: 2\n\n                                    2.5 %      97.5 %\n(Intercept)                    47.5444365 53.28010557\nPerfluorononanoic_acid_comment -5.0903542  4.84577011\nGender                         -1.1072206  0.51559131\nRace                            0.8590130  1.58672663\nMarital_Status                 -3.6458461 -2.18263403\nRatio_income_poverty           -0.6959141 -0.01015699\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                                 Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                     7.815e+01  5.256e+00  14.868  < 2e-16 ***\nPerfluorononanoic_acid_comment -7.871e+00  4.689e+00  -1.678  0.09660 .  \nGender                         -7.635e-01  9.504e-01  -0.803  0.42383    \nRace                            1.416e+00  4.272e-01   3.314  0.00131 ** \nMarital_Status                 -3.169e+00  3.543e-01  -8.946 3.20e-14 ***\nRatio_income_poverty           -1.157e+00  3.943e-01  -2.934  0.00421 ** \nBMI                             2.148e-01  7.719e-02   2.783  0.00651 ** \nsleep_disorders                -2.678e+00  1.136e+00  -2.358  0.02045 *  \nquit_smoking                    3.112e-04  2.895e-05  10.750  < 2e-16 ***\nAvg_alcohol_drinks              5.964e+00  1.248e+00   4.778 6.52e-06 ***\nhad_cancer                     -1.468e+01  1.606e+00  -9.139 1.25e-14 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 305.8616)\n\nNumber of Fisher Scoring iterations: 2\n\n                                       2.5 %        97.5 %\n(Intercept)                     67.713073728  8.858582e+01\nPerfluorononanoic_acid_comment -17.181591762  1.440350e+00\nGender                          -2.650618824  1.123632e+00\nRace                             0.567440494  2.264067e+00\nMarital_Status                  -3.872912412 -2.466056e+00\nRatio_income_poverty            -1.939889398 -3.739266e-01\nBMI                              0.061579130  3.681027e-01\nsleep_disorders                 -4.932421575 -4.229425e-01\nquit_smoking                     0.000253748  3.687221e-04\nAvg_alcohol_drinks               3.485471884  8.442395e+00\nhad_cancer                     -17.864999070 -1.148764e+01\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid), \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                           Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                 45.8115     0.2650 172.864  < 2e-16 ***\nln(Perfluorononanoic_acid)   1.1868     0.3181   3.731 0.000282 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 376.4274)\n\nNumber of Fisher Scoring iterations: 2\n\n                                2.5 %    97.5 %\n(Intercept)                45.2872993 46.335675\nln(Perfluorononanoic_acid)  0.5575797  1.816049\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                           Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                50.84129    1.45272  34.997  < 2e-16 ***\nln(Perfluorononanoic_acid)  1.30520    0.30823   4.235 4.32e-05 ***\nGender                     -0.05984    0.41534  -0.144   0.8857    \nRace                        1.14134    0.18422   6.196 7.22e-09 ***\nMarital_Status             -2.93685    0.37232  -7.888 1.14e-12 ***\nRatio_income_poverty       -0.42736    0.17571  -2.432   0.0164 *  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 336.159)\n\nNumber of Fisher Scoring iterations: 2\n\n                                2.5 %      97.5 %\n(Intercept)                47.9670558 53.71552440\nln(Perfluorononanoic_acid)  0.6953656  1.91503874\nGender                     -0.8815941  0.76191673\nRace                        0.7768553  1.50582544\nMarital_Status             -3.6734893 -2.20020581\nRatio_income_poverty       -0.7749988 -0.07971216\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                             Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                 7.920e+01  5.292e+00  14.966  < 2e-16 ***\nln(Perfluorononanoic_acid)  1.670e+00  8.571e-01   1.948  0.05437 .  \nGender                     -6.575e-01  1.007e+00  -0.653  0.51519    \nRace                        1.283e+00  4.291e-01   2.990  0.00356 ** \nMarital_Status             -3.128e+00  3.621e-01  -8.640 1.43e-13 ***\nRatio_income_poverty       -1.176e+00  3.964e-01  -2.967  0.00381 ** \nBMI                         2.185e-01  7.602e-02   2.874  0.00501 ** \nsleep_disorders            -2.819e+00  1.135e+00  -2.483  0.01481 *  \nquit_smoking                3.107e-04  2.801e-05  11.093  < 2e-16 ***\nAvg_alcohol_drinks          5.753e+00  1.218e+00   4.723 8.10e-06 ***\nhad_cancer                 -1.479e+01  1.641e+00  -9.015 2.29e-14 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 305.1917)\n\nNumber of Fisher Scoring iterations: 2\n\n                                   2.5 %        97.5 %\n(Intercept)                 6.869223e+01  8.970715e+01\nln(Perfluorononanoic_acid) -3.196447e-02  3.371595e+00\nGender                     -2.655974e+00  1.340950e+00\nRace                        4.311156e-01  2.135158e+00\nMarital_Status             -3.847105e+00 -2.409357e+00\nRatio_income_poverty       -1.963194e+00 -3.891319e-01\nBMI                         6.756232e-02  3.694403e-01\nsleep_disorders            -5.073132e+00 -5.646113e-01\nquit_smoking                2.550562e-04  3.662659e-04\nAvg_alcohol_drinks          3.334454e+00  8.170740e+00\nhad_cancer                 -1.804694e+01 -1.153193e+01\n"} -->

Call: svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.5414 0.2615 174.168 <2e-16 *** Perfluorononanoic_acid_comment 1.2082 2.5944 0.466 0.642
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 377.3081)

Number of Fisher Scoring iterations: 2

                               2.5 %    97.5 %

(Intercept) 45.024247 46.058640 Perfluorononanoic_acid_comment -3.923447 6.339837

Call: svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.4123 1.4495 34.780 < 2e-16 Perfluorononanoic_acid_comment -0.1223 2.5110 -0.049 0.9612
Gender -0.2958 0.4101 -0.721 0.4720
Race 1.2229 0.1839 6.650 7.54e-10 Marital_Status -2.9142 0.3698 -7.881 1.18e-12 ** Ratio_income_poverty -0.3530 0.1733 -2.037 0.0437
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 337.238)

Number of Fisher Scoring iterations: 2

                                2.5 %      97.5 %

(Intercept) 47.5444365 53.28010557 Perfluorononanoic_acid_comment -5.0903542 4.84577011 Gender -1.1072206 0.51559131 Race 0.8590130 1.58672663 Marital_Status -3.6458461 -2.18263403 Ratio_income_poverty -0.6959141 -0.01015699

Call: svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.815e+01 5.256e+00 14.868 < 2e-16 Perfluorononanoic_acid_comment -7.871e+00 4.689e+00 -1.678 0.09660 .
Gender -7.635e-01 9.504e-01 -0.803 0.42383
Race 1.416e+00 4.272e-01 3.314 0.00131 Marital_Status -3.169e+00 3.543e-01 -8.946 3.20e-14 Ratio_income_poverty -1.157e+00 3.943e-01 -2.934 0.00421 BMI 2.148e-01 7.719e-02 2.783 0.00651 ** sleep_disorders -2.678e+00 1.136e+00 -2.358 0.02045 *
quit_smoking 3.112e-04 2.895e-05 10.750 < 2e-16 Avg_alcohol_drinks 5.964e+00 1.248e+00 4.778 6.52e-06 had_cancer -1.468e+01 1.606e+00 -9.139 1.25e-14 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 305.8616)

Number of Fisher Scoring iterations: 2

                                   2.5 %        97.5 %

(Intercept) 67.713073728 8.858582e+01 Perfluorononanoic_acid_comment -17.181591762 1.440350e+00 Gender -2.650618824 1.123632e+00 Race 0.567440494 2.264067e+00 Marital_Status -3.872912412 -2.466056e+00 Ratio_income_poverty -1.939889398 -3.739266e-01 BMI 0.061579130 3.681027e-01 sleep_disorders -4.932421575 -4.229425e-01 quit_smoking 0.000253748 3.687221e-04 Avg_alcohol_drinks 3.485471884 8.442395e+00 had_cancer -17.864999070 -1.148764e+01

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid), design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.8115 0.2650 172.864 < 2e-16 ln(Perfluorononanoic_acid) 1.1868 0.3181 3.731 0.000282 — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 376.4274)

Number of Fisher Scoring iterations: 2

                            2.5 %    97.5 %

(Intercept) 45.2872993 46.335675 ln(Perfluorononanoic_acid) 0.5575797 1.816049

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 50.84129 1.45272 34.997 < 2e-16 ln(Perfluorononanoic_acid) 1.30520 0.30823 4.235 4.32e-05 Gender -0.05984 0.41534 -0.144 0.8857
Race 1.14134 0.18422 6.196 7.22e-09 Marital_Status -2.93685 0.37232 -7.888 1.14e-12 Ratio_income_poverty -0.42736 0.17571 -2.432 0.0164 *
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 336.159)

Number of Fisher Scoring iterations: 2

                            2.5 %      97.5 %

(Intercept) 47.9670558 53.71552440 ln(Perfluorononanoic_acid) 0.6953656 1.91503874 Gender -0.8815941 0.76191673 Race 0.7768553 1.50582544 Marital_Status -3.6734893 -2.20020581 Ratio_income_poverty -0.7749988 -0.07971216

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.920e+01 5.292e+00 14.966 < 2e-16 ln(Perfluorononanoic_acid) 1.670e+00 8.571e-01 1.948 0.05437 .
Gender -6.575e-01 1.007e+00 -0.653 0.51519
Race 1.283e+00 4.291e-01 2.990 0.00356 Marital_Status -3.128e+00 3.621e-01 -8.640 1.43e-13 Ratio_income_poverty -1.176e+00 3.964e-01 -2.967 0.00381 BMI 2.185e-01 7.602e-02 2.874 0.00501 ** sleep_disorders -2.819e+00 1.135e+00 -2.483 0.01481 *
quit_smoking 3.107e-04 2.801e-05 11.093 < 2e-16 Avg_alcohol_drinks 5.753e+00 1.218e+00 4.723 8.10e-06 had_cancer -1.479e+01 1.641e+00 -9.015 2.29e-14 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 305.1917)

Number of Fisher Scoring iterations: 2

                               2.5 %        97.5 %

(Intercept) 6.869223e+01 8.970715e+01 ln(Perfluorononanoic_acid) -3.196447e-02 3.371595e+00 Gender -2.655974e+00 1.340950e+00 Race 4.311156e-01 2.135158e+00 Marital_Status -3.847105e+00 -2.409357e+00 Ratio_income_poverty -1.963194e+00 -3.891319e-01 BMI 6.756232e-02 3.694403e-01 sleep_disorders -5.073132e+00 -5.646113e-01 quit_smoking 2.550562e-04 3.662659e-04 Avg_alcohol_drinks 3.334454e+00 8.170740e+00 had_cancer -1.804694e+01 -1.153193e+01




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#"perfluorooctanoic_acid"  "perfluorooctanoic_acid_comment"    

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<!-- rnb-output-begin {"data":"\nCall:\nsvyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                               Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                     43.4970     0.3398 127.989  < 2e-16 ***\nperfluorooctanoic_acid_comment   4.8208     0.6522   7.391 2.29e-11 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 368.6468)\n\nNumber of Fisher Scoring iterations: 2\n\n                                   2.5 %    97.5 %\n(Intercept)                    42.824027 44.170014\nperfluorooctanoic_acid_comment  3.529163  6.112411\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                               Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                     48.5424     1.5971  30.393  < 2e-16 ***\nperfluorooctanoic_acid_comment   3.1479     0.6242   5.043 1.74e-06 ***\nGender                          -0.3302     0.4575  -0.722   0.4719    \nRace                             1.2890     0.2016   6.393 3.70e-09 ***\nMarital_Status                  -2.8443     0.3903  -7.287 4.45e-11 ***\nRatio_income_poverty            -0.3586     0.1767  -2.030   0.0447 *  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 330.7506)\n\nNumber of Fisher Scoring iterations: 2\n\n                                    2.5 %       97.5 %\n(Intercept)                    45.3784365 51.706315821\nperfluorooctanoic_acid_comment  1.9114453  4.384409757\nGender                         -1.2365070  0.576118449\nRace                            0.8895389  1.688413922\nMarital_Status                 -3.6175446 -2.071017131\nRatio_income_poverty           -0.7085828 -0.008648249\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                                 Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                     7.819e+01  6.022e+00  12.985  < 2e-16 ***\nperfluorooctanoic_acid_comment  1.786e+00  1.205e+00   1.482 0.142284    \nGender                         -1.031e+00  1.059e+00  -0.973 0.333315    \nRace                            1.345e+00  4.793e-01   2.806 0.006318 ** \nMarital_Status                 -3.293e+00  3.767e-01  -8.741 3.12e-13 ***\nRatio_income_poverty           -1.074e+00  3.979e-01  -2.700 0.008491 ** \nBMI                             1.946e-01  8.413e-02   2.314 0.023286 *  \nsleep_disorders                -2.881e+00  1.244e+00  -2.316 0.023140 *  \nquit_smoking                    2.891e-04  3.467e-05   8.337 1.92e-12 ***\nAvg_alcohol_drinks              5.953e+00  1.539e+00   3.868 0.000224 ***\nhad_cancer                     -1.475e+01  1.816e+00  -8.126 4.95e-12 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 306.8411)\n\nNumber of Fisher Scoring iterations: 2\n\n                                       2.5 %        97.5 %\n(Intercept)                     6.620563e+01  9.017810e+01\nperfluorooctanoic_acid_comment -6.125094e-01  4.184327e+00\nGender                         -3.137935e+00  1.076757e+00\nRace                            3.907639e-01  2.298814e+00\nMarital_Status                 -4.042829e+00 -2.543076e+00\nRatio_income_poverty           -1.866186e+00 -2.821458e-01\nBMI                             2.719110e-02  3.620871e-01\nsleep_disorders                -5.356931e+00 -4.052082e-01\nquit_smoking                    2.200367e-04  3.580638e-04\nAvg_alcohol_drinks              2.889848e+00  9.015653e+00\nhad_cancer                     -1.836698e+01 -1.113947e+01\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid), \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                           Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                 45.8115     0.2650 172.864  < 2e-16 ***\nln(Perfluorononanoic_acid)   1.1868     0.3181   3.731 0.000282 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 376.4274)\n\nNumber of Fisher Scoring iterations: 2\n\n                                2.5 %    97.5 %\n(Intercept)                45.2872993 46.335675\nln(Perfluorononanoic_acid)  0.5575797  1.816049\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                           Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                50.84129    1.45272  34.997  < 2e-16 ***\nln(Perfluorononanoic_acid)  1.30520    0.30823   4.235 4.32e-05 ***\nGender                     -0.05984    0.41534  -0.144   0.8857    \nRace                        1.14134    0.18422   6.196 7.22e-09 ***\nMarital_Status             -2.93685    0.37232  -7.888 1.14e-12 ***\nRatio_income_poverty       -0.42736    0.17571  -2.432   0.0164 *  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 336.159)\n\nNumber of Fisher Scoring iterations: 2\n\n                                2.5 %      97.5 %\n(Intercept)                47.9670558 53.71552440\nln(Perfluorononanoic_acid)  0.6953656  1.91503874\nGender                     -0.8815941  0.76191673\nRace                        0.7768553  1.50582544\nMarital_Status             -3.6734893 -2.20020581\nRatio_income_poverty       -0.7749988 -0.07971216\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                             Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                 7.920e+01  5.292e+00  14.966  < 2e-16 ***\nln(Perfluorononanoic_acid)  1.670e+00  8.571e-01   1.948  0.05437 .  \nGender                     -6.575e-01  1.007e+00  -0.653  0.51519    \nRace                        1.283e+00  4.291e-01   2.990  0.00356 ** \nMarital_Status             -3.128e+00  3.621e-01  -8.640 1.43e-13 ***\nRatio_income_poverty       -1.176e+00  3.964e-01  -2.967  0.00381 ** \nBMI                         2.185e-01  7.602e-02   2.874  0.00501 ** \nsleep_disorders            -2.819e+00  1.135e+00  -2.483  0.01481 *  \nquit_smoking                3.107e-04  2.801e-05  11.093  < 2e-16 ***\nAvg_alcohol_drinks          5.753e+00  1.218e+00   4.723 8.10e-06 ***\nhad_cancer                 -1.479e+01  1.641e+00  -9.015 2.29e-14 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 305.1917)\n\nNumber of Fisher Scoring iterations: 2\n\n                                   2.5 %        97.5 %\n(Intercept)                 6.869223e+01  8.970715e+01\nln(Perfluorononanoic_acid) -3.196447e-02  3.371595e+00\nGender                     -2.655974e+00  1.340950e+00\nRace                        4.311156e-01  2.135158e+00\nMarital_Status             -3.847105e+00 -2.409357e+00\nRatio_income_poverty       -1.963194e+00 -3.891319e-01\nBMI                         6.756232e-02  3.694403e-01\nsleep_disorders            -5.073132e+00 -5.646113e-01\nquit_smoking                2.550562e-04  3.662659e-04\nAvg_alcohol_drinks          3.334454e+00  8.170740e+00\nhad_cancer                 -1.804694e+01 -1.153193e+01\n"} -->

Call: svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.4970 0.3398 127.989 < 2e-16 perfluorooctanoic_acid_comment 4.8208 0.6522 7.391 2.29e-11 — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 368.6468)

Number of Fisher Scoring iterations: 2

                               2.5 %    97.5 %

(Intercept) 42.824027 44.170014 perfluorooctanoic_acid_comment 3.529163 6.112411

Call: svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.5424 1.5971 30.393 < 2e-16 perfluorooctanoic_acid_comment 3.1479 0.6242 5.043 1.74e-06 Gender -0.3302 0.4575 -0.722 0.4719
Race 1.2890 0.2016 6.393 3.70e-09 Marital_Status -2.8443 0.3903 -7.287 4.45e-11 Ratio_income_poverty -0.3586 0.1767 -2.030 0.0447 *
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 330.7506)

Number of Fisher Scoring iterations: 2

                                2.5 %       97.5 %

(Intercept) 45.3784365 51.706315821 perfluorooctanoic_acid_comment 1.9114453 4.384409757 Gender -1.2365070 0.576118449 Race 0.8895389 1.688413922 Marital_Status -3.6175446 -2.071017131 Ratio_income_poverty -0.7085828 -0.008648249

Call: svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.819e+01 6.022e+00 12.985 < 2e-16 perfluorooctanoic_acid_comment 1.786e+00 1.205e+00 1.482 0.142284
Gender -1.031e+00 1.059e+00 -0.973 0.333315
Race 1.345e+00 4.793e-01 2.806 0.006318 Marital_Status -3.293e+00 3.767e-01 -8.741 3.12e-13 Ratio_income_poverty -1.074e+00 3.979e-01 -2.700 0.008491 BMI 1.946e-01 8.413e-02 2.314 0.023286 *
sleep_disorders -2.881e+00 1.244e+00 -2.316 0.023140 *
quit_smoking 2.891e-04 3.467e-05 8.337 1.92e-12 Avg_alcohol_drinks 5.953e+00 1.539e+00 3.868 0.000224 had_cancer -1.475e+01 1.816e+00 -8.126 4.95e-12 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 306.8411)

Number of Fisher Scoring iterations: 2

                                   2.5 %        97.5 %

(Intercept) 6.620563e+01 9.017810e+01 perfluorooctanoic_acid_comment -6.125094e-01 4.184327e+00 Gender -3.137935e+00 1.076757e+00 Race 3.907639e-01 2.298814e+00 Marital_Status -4.042829e+00 -2.543076e+00 Ratio_income_poverty -1.866186e+00 -2.821458e-01 BMI 2.719110e-02 3.620871e-01 sleep_disorders -5.356931e+00 -4.052082e-01 quit_smoking 2.200367e-04 3.580638e-04 Avg_alcohol_drinks 2.889848e+00 9.015653e+00 had_cancer -1.836698e+01 -1.113947e+01

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid), design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

(Dispersion parameter for gaussian family taken to be 376.4274)

Number of Fisher Scoring iterations: 2

                            2.5 %    97.5 %

(Intercept) 45.2872993 46.335675 ln(Perfluorononanoic_acid) 0.5575797 1.816049

Call: svyglm(formula = Phenotypic_Age ~ ln(Perfluorononanoic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

(Dispersion parameter for gaussian family taken to be 336.159)

Number of Fisher Scoring iterations: 2

                            2.5 %      97.5 %

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

(Dispersion parameter for gaussian family taken to be 305.1917)

Number of Fisher Scoring iterations: 2

                               2.5 %        97.5 %




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#"perfluorooctane_sulfonic_acid"     "perfluorooctane_sulfonic_acid_comment"

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<!-- rnb-output-begin {"data":"\nCall:\nsvyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                      Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                            45.1300     0.2921 154.485   <2e-16 ***\nperfluorooctane_sulfonic_acid_comment   8.2645     4.1835   1.975   0.0505 .  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 373.6055)\n\nNumber of Fisher Scoring iterations: 2\n\n                                            2.5 %   97.5 %\n(Intercept)                           44.55146084 45.70846\nperfluorooctane_sulfonic_acid_comment -0.02003231 16.54910\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                      Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                            49.6362     1.6016  30.991  < 2e-16 ***\nperfluorooctane_sulfonic_acid_comment   9.6344     5.2158   1.847   0.0673 .  \nGender                                 -0.3451     0.4578  -0.754   0.4525    \nRace                                    1.3338     0.1969   6.773 5.80e-10 ***\nMarital_Status                         -2.9403     0.3994  -7.362 3.03e-11 ***\nRatio_income_poverty                   -0.3481     0.1821  -1.911   0.0585 .  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 332.5743)\n\nNumber of Fisher Scoring iterations: 2\n\n                                           2.5 %      97.5 %\n(Intercept)                           46.4633496 52.80905212\nperfluorooctane_sulfonic_acid_comment -0.6980838 19.96687482\nGender                                -1.2519299  0.56172801\nRace                                   0.9437016  1.72397764\nMarital_Status                        -3.7314691 -2.14916351\nRatio_income_poverty                  -0.7088672  0.01268752\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                                        Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                            7.928e+01  5.907e+00  13.421  < 2e-16 ***\nperfluorooctane_sulfonic_acid_comment  1.955e+00  8.065e+00   0.242 0.809060    \nGender                                -9.339e-01  1.079e+00  -0.865 0.389401    \nRace                                   1.390e+00  4.734e-01   2.936 0.004350 ** \nMarital_Status                        -3.376e+00  4.007e-01  -8.426 1.29e-12 ***\nRatio_income_poverty                  -1.080e+00  4.013e-01  -2.692 0.008669 ** \nBMI                                    2.052e-01  8.167e-02   2.512 0.014037 *  \nsleep_disorders                       -3.008e+00  1.246e+00  -2.414 0.018112 *  \nquit_smoking                           3.012e-04  3.324e-05   9.063 7.34e-14 ***\nAvg_alcohol_drinks                     5.591e+00  1.486e+00   3.762 0.000322 ***\nhad_cancer                            -1.481e+01  1.831e+00  -8.090 5.83e-12 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 307.6054)\n\nNumber of Fisher Scoring iterations: 2\n\n                                              2.5 %        97.5 %\n(Intercept)                            6.752297e+01  9.103943e+01\nperfluorooctane_sulfonic_acid_comment -1.409706e+01  1.800763e+01\nGender                                -3.081612e+00  1.213880e+00\nRace                                   4.476930e-01  2.332064e+00\nMarital_Status                        -4.173432e+00 -2.578424e+00\nRatio_income_poverty                  -1.878813e+00 -2.814733e-01\nBMI                                    4.260483e-02  3.677075e-01\nsleep_disorders                       -5.487817e+00 -5.272295e-01\nquit_smoking                           2.350757e-04  3.673987e-04\nAvg_alcohol_drinks                     2.632925e+00  8.549037e+00\nhad_cancer                            -1.845341e+01 -1.116557e+01\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid), \n    design = des, family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                  Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                        39.9300     0.6158  64.838  < 2e-16 ***\nln(perfluorooctane_sulfonic_acid)   2.4350     0.2560   9.513 2.86e-16 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 368.1841)\n\nNumber of Fisher Scoring iterations: 2\n\n                                      2.5 %   97.5 %\n(Intercept)                       38.710501 41.14959\nln(perfluorooctane_sulfonic_acid)  1.928069  2.94185\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty, design = des, \n    family = \"gaussian\", data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients:\n                                  Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                        43.8418     1.6852  26.016  < 2e-16 ***\nln(perfluorooctane_sulfonic_acid)   2.3710     0.2761   8.586 5.28e-14 ***\nGender                              0.7187     0.4690   1.532   0.1282    \nRace                                1.1613     0.1930   6.016 2.21e-08 ***\nMarital_Status                     -2.9709     0.4024  -7.382 2.74e-11 ***\nRatio_income_poverty               -0.4607     0.1839  -2.505   0.0137 *  \n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 327.8)\n\nNumber of Fisher Scoring iterations: 2\n\n                                       2.5 %      97.5 %\n(Intercept)                       40.5034947 47.18019243\nln(perfluorooctane_sulfonic_acid)  1.8239443  2.91797149\nGender                            -0.2104365  1.64778219\nRace                               0.7789264  1.54371534\nMarital_Status                    -3.7681302 -2.17367574\nRatio_income_poverty              -0.8250408 -0.09633897\n\nCall:\nsvyglm(formula = Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid) + \n    Gender + Race + Marital_Status + Ratio_income_poverty + BMI + \n    sleep_disorders + Smoked_days + now_smoke + quit_smoking + \n    Avg_alcohol_drinks + had_cancer, design = des, family = \"gaussian\", \n    data = Fulldat_Pheno)\n\nSurvey design:\nsvydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, \n    data = Fulldat_Pheno)\n\nCoefficients: (2 not defined because of singularities)\n                                    Estimate Std. Error t value Pr(>|t|)    \n(Intercept)                        7.028e+01  6.372e+00  11.028  < 2e-16 ***\nln(perfluorooctane_sulfonic_acid)  3.564e+00  7.574e-01   4.706 1.06e-05 ***\nGender                             8.875e-01  1.188e+00   0.747  0.45717    \nRace                               9.922e-01  4.446e-01   2.232  0.02847 *  \nMarital_Status                    -3.330e+00  4.022e-01  -8.278 2.50e-12 ***\nRatio_income_poverty              -1.156e+00  3.827e-01  -3.020  0.00340 ** \nBMI                                2.393e-01  7.966e-02   3.005  0.00356 ** \nsleep_disorders                   -3.253e+00  1.246e+00  -2.609  0.01084 *  \nquit_smoking                       2.907e-04  3.064e-05   9.487 1.09e-14 ***\nAvg_alcohol_drinks                 5.004e+00  1.482e+00   3.376  0.00114 ** \nhad_cancer                        -1.442e+01  1.827e+00  -7.890 1.43e-11 ***\n---\nSignif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n\n(Dispersion parameter for gaussian family taken to be 297.0953)\n\nNumber of Fisher Scoring iterations: 2\n\n                                          2.5 %        97.5 %\n(Intercept)                        5.759441e+01  8.296255e+01\nln(perfluorooctane_sulfonic_acid)  2.056421e+00  5.071592e+00\nGender                            -1.476654e+00  3.251566e+00\nRace                               1.072713e-01  1.877214e+00\nMarital_Status                    -4.130473e+00 -2.529210e+00\nRatio_income_poverty              -1.917263e+00 -3.939495e-01\nBMI                                8.078405e-02  3.978968e-01\nsleep_disorders                   -5.733735e+00 -7.715856e-01\nquit_smoking                       2.296882e-04  3.516615e-04\nAvg_alcohol_drinks                 2.053687e+00  7.955170e+00\nhad_cancer                        -1.805161e+01 -1.077864e+01\n"} -->

Call: svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.1300 0.2921 154.485 <2e-16 *** perfluorooctane_sulfonic_acid_comment 8.2645 4.1835 1.975 0.0505 .
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 373.6055)

Number of Fisher Scoring iterations: 2

                                        2.5 %   97.5 %

(Intercept) 44.55146084 45.70846 perfluorooctane_sulfonic_acid_comment -0.02003231 16.54910

Call: svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.6362 1.6016 30.991 < 2e-16 perfluorooctane_sulfonic_acid_comment 9.6344 5.2158 1.847 0.0673 .
Gender -0.3451 0.4578 -0.754 0.4525
Race 1.3338 0.1969 6.773 5.80e-10 Marital_Status -2.9403 0.3994 -7.362 3.03e-11 *** Ratio_income_poverty -0.3481 0.1821 -1.911 0.0585 .
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.5743)

Number of Fisher Scoring iterations: 2

                                       2.5 %      97.5 %

(Intercept) 46.4633496 52.80905212 perfluorooctane_sulfonic_acid_comment -0.6980838 19.96687482 Gender -1.2519299 0.56172801 Race 0.9437016 1.72397764 Marital_Status -3.7314691 -2.14916351 Ratio_income_poverty -0.7088672 0.01268752

Call: svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.928e+01 5.907e+00 13.421 < 2e-16 perfluorooctane_sulfonic_acid_comment 1.955e+00 8.065e+00 0.242 0.809060
Gender -9.339e-01 1.079e+00 -0.865 0.389401
Race 1.390e+00 4.734e-01 2.936 0.004350 Marital_Status -3.376e+00 4.007e-01 -8.426 1.29e-12 Ratio_income_poverty -1.080e+00 4.013e-01 -2.692 0.008669 BMI 2.052e-01 8.167e-02 2.512 0.014037 *
sleep_disorders -3.008e+00 1.246e+00 -2.414 0.018112 *
quit_smoking 3.012e-04 3.324e-05 9.063 7.34e-14 Avg_alcohol_drinks 5.591e+00 1.486e+00 3.762 0.000322 had_cancer -1.481e+01 1.831e+00 -8.090 5.83e-12 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 307.6054)

Number of Fisher Scoring iterations: 2

                                          2.5 %        97.5 %

(Intercept) 6.752297e+01 9.103943e+01 perfluorooctane_sulfonic_acid_comment -1.409706e+01 1.800763e+01 Gender -3.081612e+00 1.213880e+00 Race 4.476930e-01 2.332064e+00 Marital_Status -4.173432e+00 -2.578424e+00 Ratio_income_poverty -1.878813e+00 -2.814733e-01 BMI 4.260483e-02 3.677075e-01 sleep_disorders -5.487817e+00 -5.272295e-01 quit_smoking 2.350757e-04 3.673987e-04 Avg_alcohol_drinks 2.632925e+00 8.549037e+00 had_cancer -1.845341e+01 -1.116557e+01

Call: svyglm(formula = Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid), design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 39.9300 0.6158 64.838 < 2e-16 ln(perfluorooctane_sulfonic_acid) 2.4350 0.2560 9.513 2.86e-16 — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 368.1841)

Number of Fisher Scoring iterations: 2

                                  2.5 %   97.5 %

(Intercept) 38.710501 41.14959 ln(perfluorooctane_sulfonic_acid) 1.928069 2.94185

Call: svyglm(formula = Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 43.8418 1.6852 26.016 < 2e-16 ln(perfluorooctane_sulfonic_acid) 2.3710 0.2761 8.586 5.28e-14 Gender 0.7187 0.4690 1.532 0.1282
Race 1.1613 0.1930 6.016 2.21e-08 Marital_Status -2.9709 0.4024 -7.382 2.74e-11 Ratio_income_poverty -0.4607 0.1839 -2.505 0.0137 *
— Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 327.8)

Number of Fisher Scoring iterations: 2

                                   2.5 %      97.5 %

(Intercept) 40.5034947 47.18019243 ln(perfluorooctane_sulfonic_acid) 1.8239443 2.91797149 Gender -0.2104365 1.64778219 Race 0.7789264 1.54371534 Marital_Status -3.7681302 -2.17367574 Ratio_income_poverty -0.8250408 -0.09633897

Call: svyglm(formula = Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid) + Gender + Race + Marital_Status + Ratio_income_poverty + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, design = des, family = “gaussian”, data = Fulldat_Pheno)

Survey design: svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, data = Fulldat_Pheno)

Coefficients: (2 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.028e+01 6.372e+00 11.028 < 2e-16 ln(perfluorooctane_sulfonic_acid) 3.564e+00 7.574e-01 4.706 1.06e-05 Gender 8.875e-01 1.188e+00 0.747 0.45717
Race 9.922e-01 4.446e-01 2.232 0.02847 *
Marital_Status -3.330e+00 4.022e-01 -8.278 2.50e-12 Ratio_income_poverty -1.156e+00 3.827e-01 -3.020 0.00340 BMI 2.393e-01 7.966e-02 3.005 0.00356 sleep_disorders -3.253e+00 1.246e+00 -2.609 0.01084 *
quit_smoking 2.907e-04 3.064e-05 9.487 1.09e-14 Avg_alcohol_drinks 5.004e+00 1.482e+00 3.376 0.00114 ** had_cancer -1.442e+01 1.827e+00 -7.890 1.43e-11 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 297.0953)

Number of Fisher Scoring iterations: 2

                                      2.5 %        97.5 %

(Intercept) 5.759441e+01 8.296255e+01 ln(perfluorooctane_sulfonic_acid) 2.056421e+00 5.071592e+00 Gender -1.476654e+00 3.251566e+00 Race 1.072713e-01 1.877214e+00 Marital_Status -4.130473e+00 -2.529210e+00 Ratio_income_poverty -1.917263e+00 -3.939495e-01 BMI 8.078405e-02 3.978968e-01 sleep_disorders -5.733735e+00 -7.715856e-01 quit_smoking 2.296882e-04 3.516615e-04 Avg_alcohol_drinks 2.053687e+00 7.955170e+00 had_cancer -1.805161e+01 -1.077864e+01




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# run cubic spline model for non-linear regression(Figure 2)
#Perfluorohexane_sulfonic_acid

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```r
```r
library(\rcssci\)
rcssci_linear(data = Fulldat_Pheno, y = \Phenotypic_Age\, x = \Perfluorohexane_sulfonic_acid\, covs=c(\Gender\, \Race\, \BMI\,\had_cancer\), prob = 0.1,ref.zero=FALSE,
              filepath = \C:/Users/HKUSCM/Documents\)


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#Perfluorononanoic_acid

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```r
```r
library(\rcssci\)
rcssci_linear(data = Fulldat_Pheno, y = \Phenotypic_Age\, x = \Perfluorononanoic_acid\, covs=c(\Gender\, \Race\, \BMI\,\had_cancer\), prob = 0.1,ref.zero=FALSE,
              filepath = \C:/Users/HKUSCM/Documents\)


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#perfluorooctanoic_acid

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```r
```r
library(\rcssci\)
rcssci_linear(data = Fulldat_Pheno, y = \Phenotypic_Age\, x = \perfluorooctanoic_acid\, covs=c(\Gender\, \Race\, \BMI\,\had_cancer\), prob = 0.1,ref.zero=FALSE,
              filepath = \C:/Users/HKUSCM/Documents\)


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#perfluorooctane_sulfonic_acid

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```r
```r
library(\rcssci\)
rcssci_linear(data = Fulldat_Pheno, y = \Phenotypic_Age\, x = \perfluorooctane_sulfonic_acid\, covs=c(\Gender\, \Race\, \BMI\,\had_cancer\), prob = 0.1,ref.zero=FALSE,
              filepath = \C:/Users/HKUSCM/Documents\)

```

---
title: "R Notebook"
output:
  html_notebook: default
  pdf_document: default
---


```{r}
library("haven")
library("tidyverse")
library("dplyr")
library("foreign")
library("survey")
library("ggplot2")
library("car")
library("rms")
library("SciViews")
```

#list variable
```{r}
colnames(Fulldat_mediation_pfas)
```


#Examine the pfas and Phenotypic_Age
```{r}
ggplot(Fulldat_Pheno, aes(x = Perfluorohexane_sulfonic_acid)) +
  geom_histogram(binwidth = 100, color = "skyblue", fill = "red", alpha = 0.7) +
  labs(title = "Distribution of Perfluorohexane_sulfonic_acid",
       x = "Perfluorohexane_sulfonic_acid",
       y = "Frequency") +
  theme_minimal()

ggplot(Fulldat_Pheno, aes(x = Perfluorohexane_sulfonic_acid, y = Phenotypic_Age)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) + 
  labs(x = "Perfluorohexane_sulfonic_acid", y = "Phenotypic_Age", title = "Scatter Plot Perfluorohexane_sulfonic_acid vs Phenotypic_Age with Regression Line")

# Define breaks for age groups
breaks <- c(20, 45, 65, Inf)  # Breaks represent the age boundaries

# Define labels for the age groups
labels <- c("1", "2", "3")

# Categorize chronological_age into groups and assign custom labels
Fulldat_Pheno$cate_age <- cut(Fulldat_Pheno$chronological_age, breaks = breaks, labels = labels, include.lowest = TRUE)
```

#sample density curves of pfas concentrations among accelerated and delayed age
```{r}
Fulldat_Pheno <- Fulldat_Pheno %>% mutate(age_binary = case_when(
  Phenotypic_Age-chronological_age >= 0 ~"accelerated",
  Phenotypic_Age-chronological_age < 0 ~"delayed"
))

library(ggplot2)
cols <- c("#F76D5E", "#72D8FF")

#Perfluorohexane_sulfonic_acid
ggplot(Fulldat_Pheno, aes(x = Perfluorohexane_sulfonic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)


#Perfluorohexane_sulfonic_acid_comment
ggplot(Fulldat_Pheno, aes(x = Perfluorononanoic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)


#Perfluorononanoic_acid
ggplot(Fulldat_Pheno, aes(x = perfluorooctanoic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)

#Perfluorononanoic_acid_comment
ggplot(Fulldat_Pheno, aes(x = perfluorooctane_sulfonic_acid, colour = age_binary)) +
  geom_density(lwd = 1.2, linetype = 1) + 
  scale_color_manual(values = cols)

```


#Main model of regression for association, and adjust for covariates (Table 2)
#Perfluorohexane_sulfonic_acid
```{r echo=FALSE,message=FALSE,warning=TRUE}
# svy design
des <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Fulldat_Pheno)#adjust weight before modelling

#binary Perfluorohexane_sulfonic_acid
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_1)
confint(model_1)

#model_2 -- adjusted for chronological_age, Gender, Race, CDAI, Marital_Status and family Ratio_income_poverty.
model_2 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_2)
confint(model_2)

#model_3 -- adjusted for BMI + sleep_disorders + Smoked_days + Avg_alcohol_drinks + had_cancer.
model_3 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous Perfluorohexane_sulfonic_acid
model_X1 <- svyglm(Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid), data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid) + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ ln(Perfluorohexane_sulfonic_acid)+ Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X3)
confint(model_X3)
```

#"Perfluorononanoic_acid"  "Perfluorononanoic_acid_comment"  
```{r echo=FALSE,message=FALSE,warning=TRUE}
# svy design
des <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Fulldat_Pheno)#adjust weight before modelling

#binary
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_1)
confint(model_1)

#model_2 -- adjusted for chronological_age, Gender, Race, CDAI, Marital_Status and family Ratio_income_poverty.
model_2 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_2)
confint(model_2)

#model_3 -- adjusted for BMI + sleep_disorders + Smoked_days + Avg_alcohol_drinks + had_cancer.
model_3 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous
model_X1 <- svyglm(Phenotypic_Age ~ ln(Perfluorononanoic_acid), data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ ln(Perfluorononanoic_acid) + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ ln(Perfluorononanoic_acid)+ Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X3)
confint(model_X3)
```


#"perfluorooctanoic_acid"  "perfluorooctanoic_acid_comment"    
```{r echo=FALSE,message=FALSE,warning=TRUE}
# svy design
des <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Fulldat_Pheno)#adjust weight before modelling

#binary
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_1)
confint(model_1)

#model_2 -- adjusted for chronological_age, Gender, Race, CDAI, Marital_Status and family Ratio_income_poverty.
model_2 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_2)
confint(model_2)

#model_3 -- adjusted for BMI + sleep_disorders + Smoked_days + Avg_alcohol_drinks + had_cancer.
model_3 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous
model_X1 <- svyglm(Phenotypic_Age ~ ln(Perfluorononanoic_acid), data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ ln(Perfluorononanoic_acid) + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ ln(Perfluorononanoic_acid)+ Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X3)
confint(model_X3)
```

#"perfluorooctane_sulfonic_acid"     "perfluorooctane_sulfonic_acid_comment"
```{r echo=FALSE,message=FALSE,warning=TRUE}
# svy design
des <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Fulldat_Pheno)#adjust weight before modelling

#binary
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_1)
confint(model_1)

#model_2 -- adjusted for chronological_age, Gender, Race, CDAI, Marital_Status and family Ratio_income_poverty.
model_2 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_2)
confint(model_2)

#model_3 -- adjusted for BMI + sleep_disorders + Smoked_days + Avg_alcohol_drinks + had_cancer.
model_3 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer
                  , data = Fulldat_Pheno, design = des, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous
model_X1 <- svyglm(Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid), data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid) + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ ln(perfluorooctane_sulfonic_acid)+ Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, data = Fulldat_Pheno, design = des, family = "gaussian")
summary(model_X3)
confint(model_X3)
```







# run cubic spline model for non-linear regression(Figure 2)
#Perfluorohexane_sulfonic_acid
```{r}
library("rcssci")
rcssci_linear(data = Fulldat_Pheno, y = "Phenotypic_Age", x = "Perfluorohexane_sulfonic_acid", covs=c("Gender", "Race", "BMI","had_cancer"), prob = 0.1,ref.zero=FALSE,
              filepath = "C:/Users/HKUSCM/Documents")
```

#Perfluorononanoic_acid
```{r}
library("rcssci")
rcssci_linear(data = Fulldat_Pheno, y = "Phenotypic_Age", x = "Perfluorononanoic_acid", covs=c("Gender", "Race", "BMI","had_cancer"), prob = 0.1,ref.zero=FALSE,
              filepath = "C:/Users/HKUSCM/Documents")
```
#perfluorooctanoic_acid
```{r}
library("rcssci")
rcssci_linear(data = Fulldat_Pheno, y = "Phenotypic_Age", x = "perfluorooctanoic_acid", covs=c("Gender", "Race", "BMI","had_cancer"), prob = 0.1,ref.zero=FALSE,
              filepath = "C:/Users/HKUSCM/Documents")
```
#perfluorooctane_sulfonic_acid
```{r}
library("rcssci")
rcssci_linear(data = Fulldat_Pheno, y = "Phenotypic_Age", x = "perfluorooctane_sulfonic_acid", covs=c("Gender", "Race", "BMI","had_cancer"), prob = 0.1,ref.zero=FALSE,
              filepath = "C:/Users/HKUSCM/Documents")
```


