library("haven")
library("tidyverse")
library("dplyr")
library("foreign")
library("survey")
library("ggplot2")
library("car")
library("rms")

#list variable

Fulldat_Pheno <- Falldat_Pheno
colnames(Fulldat_Pheno)
 [1] "SEQN"                                  "chronological_age"                     "Gender"                                "Race"                                  "Pregnancy"                            
 [6] "Marital_Status"                        "Ratio_income_poverty"                  "Interview_Weight"                      "MEC_Weight"                            "psu"                                  
[11] "Strata"                                "BMI"                                   "Vitamin_A"                             "Vitamin_C"                             "Vitamin_E"                            
[16] "Zinc"                                  "Selenium"                              "sleep_disorders"                       "Smoked_days"                           "now_smoke"                            
[21] "quit_smoking"                          "Avg_alcohol_drinks"                    "equipment_walk"                        "walk_difficulty"                       "had_cancer"                           
[26] "weight_2"                              "Perfluorohexane_sulfonic_acid"         "Perfluorohexane_sulfonic_acid_comment" "Perfluorononanoic_acid"                "Perfluorononanoic_acid_comment"       
[31] "perfluorooctanoic_acid"                "perfluorooctanoic_acid_comment"        "perfluorooctane_sulfonic_acid"         "perfluorooctane_sulfonic_acid_comment" "White_blood_cell_count"               
[36] "Lymphocyte_percent"                    "Red_cell_distribution_width"           "Mean_cell_volume"                      "Albumin"                               "Creatinine"                           
[41] "Glucose_serum"                         "Alkaline_phosphotase"                  "xb"                                    "Phenotypic_Age"                        "cate_age"                             
[46] "age_binary"                           

#Perfluorohexane_sulfonic_acid_comment #Perfluorononanoic_acid_comment #perfluorooctanoic_acid_comment #perfluorooctane_sulfonic_acid_comment

#check effect modifiers Subgroup analysis for gender, race, BMI, income, smoking and cancer #for subgroup analysis by gender


Call:
glm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Perfluorohexane_sulfonic_acid_comment * Gender, data = Fulldat_Pheno)

Coefficients:
                                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   49.9994     0.5579  89.619  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment          6.7698     4.7274   1.432    0.152    
Gender                                        -1.5534     0.3499  -4.439  9.1e-06 ***
Perfluorohexane_sulfonic_acid_comment:Gender  -3.5514     2.8077  -1.265    0.206    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 447.3236)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6647676  on 14861  degrees of freedom
AIC: 132916

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desmen, family = "gaussian", data = men)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            45.2794     0.3513 128.873   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   7.5755     3.5144   2.156   0.0329 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 382.7214)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
 0.6241443 14.5267701 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = deswomen, family = "gaussian", data = women)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            45.7019     0.3266  139.94   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   3.5282     2.3364    1.51    0.133    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 371.5434)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-1.093081  8.149563 

Call:
glm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Perfluorononanoic_acid_comment * Gender, data = Fulldat_Pheno)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            49.9893     0.5579  89.608  < 2e-16 ***
Perfluorononanoic_acid_comment          7.8892     4.7586   1.658   0.0974 .  
Gender                                 -1.5518     0.3500  -4.434 9.31e-06 ***
Perfluorononanoic_acid_comment:Gender  -3.9326     2.8054  -1.402   0.1610    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 447.2937)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6647231  on 14861  degrees of freedom
AIC: 132915

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desmen, family = "gaussian", data = men)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     45.3297     0.3471 130.588   <2e-16 ***
Perfluorononanoic_acid_comment   2.2091     4.6783   0.472    0.638    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 383.2557)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-7.044485 11.462631 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = deswomen, family = "gaussian", data = women)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     45.7405     0.3180 143.833   <2e-16 ***
Perfluorononanoic_acid_comment   0.4946     2.6110   0.189     0.85    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 371.7062)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-4.669759  5.659043 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    perfluorooctanoic_acid_comment * Gender, data = Fulldat_Pheno)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                           48.74370    0.71176  68.484  < 2e-16 ***
perfluorooctanoic_acid_comment         4.49087    1.26149   3.560 0.000372 ***
Gender                                -1.88576    0.44764  -4.213 2.54e-05 ***
perfluorooctanoic_acid_comment:Gender  0.04657    0.78983   0.059 0.952982    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 441.9759)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5747455  on 13004  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 116156

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desmen, family = "gaussian", data = men)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     43.2725     0.4531  95.513  < 2e-16 ***
perfluorooctanoic_acid_comment   4.9246     0.9018   5.461 2.67e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 374.5575)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.138698 6.710459 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = deswomen, family = "gaussian", data = women)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     43.7086     0.4246 102.935  < 2e-16 ***
perfluorooctanoic_acid_comment   4.7226     0.7498   6.299 5.34e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 363.063)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.237880 6.207407 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    perfluorooctane_sulfonic_acid_comment * Gender, data = Fulldat_Pheno)

Coefficients:
                                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                   50.1601     0.5919  84.749  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment         -5.1976     8.2939  -0.627   0.5309    
Gender                                        -1.8814     0.3715  -5.064 4.16e-07 ***
perfluorooctane_sulfonic_acid_comment:Gender   8.3219     5.0406   1.651   0.0988 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 446.0821)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5800851  on 13004  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 116276

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desmen, family = "gaussian", data = men)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.9766     0.3946 113.974   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   0.4996     6.0264   0.083    0.934    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 380.0558)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-11.43428  12.43350 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = deswomen, family = "gaussian", data = women)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            45.2745     0.3559 127.219  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment  13.6893     3.0065   4.553 1.29e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 367.1354)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
 7.735561 19.643058 

#for subgroup analysis by Race


Call:
glm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Perfluorohexane_sulfonic_acid_comment * as.factor(Race), 
    data = Fulldat_Pheno)

Coefficients:
                                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                            43.582017   0.429231 101.535  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment                  -0.636612   2.636836  -0.241   0.8092    
as.factor(Race)2                                        3.025839   0.721528   4.194 2.76e-05 ***
as.factor(Race)3                                        6.958022   0.505333  13.769  < 2e-16 ***
as.factor(Race)4                                        3.791857   0.567972   6.676 2.54e-11 ***
as.factor(Race)5                                        0.250593   0.682837   0.367   0.7136    
Perfluorohexane_sulfonic_acid_comment:as.factor(Race)2  4.330658   5.139854   0.843   0.3995    
Perfluorohexane_sulfonic_acid_comment:as.factor(Race)3  6.816479   3.742669   1.821   0.0686 .  
Perfluorohexane_sulfonic_acid_comment:as.factor(Race)4 -0.002068   3.633436  -0.001   0.9995    
Perfluorohexane_sulfonic_acid_comment:as.factor(Race)5  2.161855   5.406763   0.400   0.6893    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 439.9632)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6535653  on 14855  degrees of freedom
AIC: 132676

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_Me, family = "gaussian", data = Mexican)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            39.0679     0.5558  70.294   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   1.7160     4.3609   0.393    0.695    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 316.7575)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-6.942671 10.374613 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_Hispanic, family = "gaussian", data = Other_Hispanic)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            41.3237     0.6683  61.838   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   5.4998     5.2744   1.043      0.3    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 318.7365)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-4.968521 15.968096 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_white, family = "gaussian", data = Non_Hispanic_white)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            47.0259     0.3668 128.218   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   9.6600     2.8938   3.338   0.0011 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 382.8172)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
 3.934467 15.385505 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_Black, family = "gaussian", data = Non_Hispanic_Black)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.6345     0.4675  95.477   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   3.2473     3.0323   1.071    0.286    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 376.7482)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-2.758535  9.253076 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_Other, family = "gaussian", data = Other_Race)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            43.0212     0.6248  68.852   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   5.5786     7.0048   0.796    0.427    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 344.0309)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
-8.30040 19.45766 

Call:
glm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Perfluorononanoic_acid_comment * as.factor(Race), data = Fulldat_Pheno)

Coefficients:
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      43.4997     0.4290 101.391  < 2e-16 ***
Perfluorononanoic_acid_comment                    2.5484     2.6771   0.952    0.341    
as.factor(Race)2                                  3.1816     0.7203   4.417 1.01e-05 ***
as.factor(Race)3                                  7.0265     0.5054  13.903  < 2e-16 ***
as.factor(Race)4                                  3.8726     0.5672   6.828 8.97e-12 ***
as.factor(Race)5                                  0.4164     0.6838   0.609    0.542    
Perfluorononanoic_acid_comment:as.factor(Race)2  -3.2971     5.7775  -0.571    0.568    
Perfluorononanoic_acid_comment:as.factor(Race)3   3.1815     3.5410   0.898    0.369    
Perfluorononanoic_acid_comment:as.factor(Race)4  -3.2791     3.8936  -0.842    0.400    
Perfluorononanoic_acid_comment:as.factor(Race)5  -6.1725     4.8127  -1.283    0.200    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 439.9162)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6534956  on 14855  degrees of freedom
AIC: 132674

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_Me, family = "gaussian", data = Mexican)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      39.227      0.551  71.188   <2e-16 ***
Perfluorononanoic_acid_comment   -3.563      3.350  -1.064     0.29    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 316.4162)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
-10.213385   3.088235 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_Hispanic, family = "gaussian", data = Other_Hispanic)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     41.4010     0.6702  61.778   <2e-16 ***
Perfluorononanoic_acid_comment   1.6022     6.2037   0.258    0.797    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 319.2697)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-10.71039  13.91485 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_white, family = "gaussian", data = Non_Hispanic_white)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     47.0214     0.3636 129.321   <2e-16 ***
Perfluorononanoic_acid_comment   6.6160     3.2129   2.059   0.0415 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 383.0326)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
 0.2592421 12.9728469 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_Black, family = "gaussian", data = Non_Hispanic_Black)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      44.756      0.465  96.240   <2e-16 ***
Perfluorononanoic_acid_comment   -2.038      3.423  -0.595    0.553    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 376.9046)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-8.816886  4.741049 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_Other, family = "gaussian", data = Other_Race)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     43.2162     0.6047   71.47  < 2e-16 ***
Perfluorononanoic_acid_comment  -6.7253     2.5189   -2.67  0.00872 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 343.5059)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
-11.716222  -1.734419 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    perfluorooctanoic_acid_comment * as.factor(Race), data = Fulldat_Pheno)

Coefficients:
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      41.2150     0.5223  78.910  < 2e-16 ***
perfluorooctanoic_acid_comment                    6.4741     0.9887   6.548 6.04e-11 ***
as.factor(Race)2                                  2.6652     0.9448   2.821   0.0048 ** 
as.factor(Race)3                                  8.0469     0.6156  13.071  < 2e-16 ***
as.factor(Race)4                                  3.2272     0.7134   4.524 6.13e-06 ***
as.factor(Race)5                                  1.2008     0.9768   1.229   0.2190    
perfluorooctanoic_acid_comment:as.factor(Race)2  -0.2810     1.5931  -0.176   0.8600    
perfluorooctanoic_acid_comment:as.factor(Race)3  -1.9574     1.1809  -1.658   0.0974 .  
perfluorooctanoic_acid_comment:as.factor(Race)4   0.2736     1.2763   0.214   0.8303    
perfluorooctanoic_acid_comment:as.factor(Race)5  -3.7608     1.5168  -2.479   0.0132 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 432.6581)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5623690  on 12998  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 115885

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_Me, family = "gaussian", data = Mexican)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     36.3461     0.7321  49.649  < 2e-16 ***
perfluorooctanoic_acid_comment   5.9684     1.4647   4.075 0.000102 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 297.8325)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.056636 8.880096 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_Hispanic, family = "gaussian", data = Other_Hispanic)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     38.9313     0.9508  40.945   <2e-16 ***
perfluorooctanoic_acid_comment   5.0395     1.5628   3.225   0.0018 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 310.8837)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
1.931728 8.147371 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_white, family = "gaussian", data = Non_Hispanic_white)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.9577     0.4493 100.065  < 2e-16 ***
perfluorooctanoic_acid_comment   5.3182     0.8232   6.461 2.66e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 375.3191)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.687537 6.948911 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_Black, family = "gaussian", data = Non_Hispanic_Black)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     42.2888     0.5648  74.872  < 2e-16 ***
perfluorooctanoic_acid_comment   5.4529     1.0802   5.048 1.96e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 356.5328)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.310393 7.595503 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_Other, family = "gaussian", data = Other_Race)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      41.639      1.002  41.547   <2e-16 ***
perfluorooctanoic_acid_comment    3.221      1.328   2.426   0.0171 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 357.6396)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
0.5860641 5.8567303 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    perfluorooctane_sulfonic_acid_comment * as.factor(Race), 
    data = Fulldat_Pheno)

Coefficients:
                                                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                             42.9569     0.4476  95.970  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment                   13.0017     6.3301   2.054    0.040 *  
as.factor(Race)2                                         3.3432     0.7618   4.388 1.15e-05 ***
as.factor(Race)3                                         7.4134     0.5300  13.989  < 2e-16 ***
as.factor(Race)4                                         3.8950     0.5954   6.542 6.31e-11 ***
as.factor(Race)5                                         0.7990     0.7333   1.090    0.276    
perfluorooctane_sulfonic_acid_comment:as.factor(Race)2  -1.3286    12.2513  -0.108    0.914    
perfluorooctane_sulfonic_acid_comment:as.factor(Race)3  -1.0121     7.9520  -0.127    0.899    
perfluorooctane_sulfonic_acid_comment:as.factor(Race)4  -9.3565     7.4396  -1.258    0.209    
perfluorooctane_sulfonic_acid_comment:as.factor(Race)5  -4.7149     9.4413  -0.499    0.618    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 438.5739)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5700584  on 12998  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 116062

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_Me, family = "gaussian", data = Mexican)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            38.5204     0.6214  61.994   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   1.7761     6.6979   0.265    0.792    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 305.851)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-11.53876  15.09101 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_Hispanic, family = "gaussian", data = Other_Hispanic)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            41.1010     0.7349  55.924   <2e-16 ***
perfluorooctane_sulfonic_acid_comment  16.6690    10.4415   1.596    0.114    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 315.6058)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-4.095018 37.433070 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_white, family = "gaussian", data = Non_Hispanic_white)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            46.6326     0.3983 117.093   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   9.9772     5.4680   1.825   0.0707 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 381.1805)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
-0.8549404 20.8093738 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_Black, family = "gaussian", data = Non_Hispanic_Black)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.2479     0.5092  86.895   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   3.8682     4.9353   0.784    0.435    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 363.2051)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-5.920955 13.657403 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_Other, family = "gaussian", data = Other_Race)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            43.0031     0.6924  62.110   <2e-16 ***
perfluorooctane_sulfonic_acid_comment  22.3559    14.0744   1.588    0.115    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 358.1666)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-5.577865 50.289573 

##for subgroup analysis by BMI


Call:
glm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Perfluorohexane_sulfonic_acid_comment * BMI_cat, data = Fulldat_Pheno)

Coefficients:
                                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                              43.0120     0.3065 140.330   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment                     3.1676     2.2767   1.391    0.164    
BMI_catobesity                                            7.4623     0.4202  17.758   <2e-16 ***
BMI_catoverweight                                         6.0562     0.4333  13.978   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment:BMI_catobesity     -0.9553     3.1912  -0.299    0.765    
Perfluorohexane_sulfonic_acid_comment:BMI_catoverweight  -6.1578     3.4226  -1.799    0.072 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 437.6957)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6503720  on 14859  degrees of freedom
AIC: 132595

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desBMI_1, family = "gaussian", data = BMI_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_1)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            40.8380     0.4358  93.710   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   8.3574     4.1683   2.005    0.047 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 414.6196)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
 0.1127562 16.6020444 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desBMI_2, family = "gaussian", data = BMI_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_2)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            46.5760     0.3829 121.624   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   1.0491     3.4245   0.306     0.76    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 352.3768)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-5.724532  7.822662 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desBMI_3, family = "gaussian", data = BMI_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_3)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            48.7070     0.3223  151.14   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   5.7139     2.5743    2.22   0.0281 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 335.0812)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
 0.6220289 10.8057844 

Call:
glm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Perfluorononanoic_acid_comment * BMI_cat, data = Fulldat_Pheno)

Coefficients:
                                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                       42.9201     0.3060 140.255  < 2e-16 ***
Perfluorononanoic_acid_comment                     9.3189     2.4180   3.854 0.000117 ***
BMI_catobesity                                     7.6780     0.4199  18.285  < 2e-16 ***
BMI_catoverweight                                  6.0932     0.4330  14.074  < 2e-16 ***
Perfluorononanoic_acid_comment:BMI_catobesity    -14.1647     3.2306  -4.385 1.17e-05 ***
Perfluorononanoic_acid_comment:BMI_catoverweight  -8.5603     3.4422  -2.487 0.012899 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 437.2314)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6496822  on 14859  degrees of freedom
AIC: 132579

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desBMI_1, family = "gaussian", data = BMI_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_1)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     40.7718     0.4327  94.216   <2e-16 ***
Perfluorononanoic_acid_comment  10.7582     5.1849   2.075   0.0399 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 413.6895)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
 0.5026964 21.0136639 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desBMI_2, family = "gaussian", data = BMI_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_2)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     46.5819     0.3808 122.316   <2e-16 ***
Perfluorononanoic_acid_comment   0.4331     3.4533   0.125      0.9    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 352.3865)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-6.397347  7.263462 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desBMI_3, family = "gaussian", data = BMI_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_3)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     48.8949     0.3153  155.09  < 2e-16 ***
Perfluorononanoic_acid_comment  -5.6217     2.1379   -2.63  0.00956 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 334.8954)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-9.850493 -1.392985 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    perfluorooctanoic_acid_comment * BMI_cat, data = Fulldat_Pheno)

Coefficients:
                                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                       41.7623     0.3855 108.321  < 2e-16 ***
perfluorooctanoic_acid_comment                     3.6185     0.7080   5.111 3.26e-07 ***
BMI_catobesity                                     6.8485     0.5415  12.647  < 2e-16 ***
BMI_catoverweight                                  5.4823     0.5454  10.051  < 2e-16 ***
perfluorooctanoic_acid_comment:BMI_catobesity      0.9114     0.9473   0.962    0.336    
perfluorooctanoic_acid_comment:BMI_catoverweight   0.8420     1.0013   0.841    0.400    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 433.4424)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5635618  on 13002  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 115905

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desBMI_1, family = "gaussian", data = BMI_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_1)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     39.4538     0.5573  70.792  < 2e-16 ***
perfluorooctanoic_acid_comment   3.8463     1.0733   3.584 0.000494 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 415.8231)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
1.720824 5.971806 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desBMI_2, family = "gaussian", data = BMI_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_2)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.5025     0.4225 105.320  < 2e-16 ***
perfluorooctanoic_acid_comment   4.9566     0.9666   5.128 1.16e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 340.7961)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.042462 6.870821 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desBMI_3, family = "gaussian", data = BMI_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_3)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     46.6687     0.4447 104.937  < 2e-16 ***
perfluorooctanoic_acid_comment   4.4131     0.7567   5.832 4.88e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 324.6119)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
2.914623 5.911566 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    perfluorooctane_sulfonic_acid_comment * BMI_cat, data = Fulldat_Pheno)

Coefficients:
                                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                              42.7616     0.3255 131.369  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment                    13.8486     4.4680   3.100  0.00194 ** 
BMI_catobesity                                            7.4438     0.4462  16.682  < 2e-16 ***
BMI_catoverweight                                         5.8166     0.4603  12.636  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment:BMI_catobesity     -5.2170     5.8058  -0.899  0.36889    
perfluorooctane_sulfonic_acid_comment:BMI_catoverweight -15.9345     6.6587  -2.393  0.01672 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 436.8523)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5679954  on 13002  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 116006

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desBMI_1, family = "gaussian", data = BMI_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_1)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            40.6041     0.4843  83.841   <2e-16 ***
perfluorooctane_sulfonic_acid_comment  13.6869     6.3044   2.171   0.0319 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 418.3034)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
 1.20249 26.17135 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desBMI_2, family = "gaussian", data = BMI_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_2)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            46.1228     0.4109 112.259   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   1.6086     3.4558   0.465    0.642    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 346.2278)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-5.234864  8.452098 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desBMI_3, family = "gaussian", data = BMI_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = BMI_3)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            48.3637     0.3434 140.827   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   8.1242     7.1488   1.136    0.258    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 328.8425)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-6.032375 22.280868 

#for subgroup analysis by income


Call:
glm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Perfluorohexane_sulfonic_acid_comment * as.factor(income_cat), 
    data = Fulldat_Pheno)

Coefficients:
                                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                                      48.8271     0.2522 193.589  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment                             0.5351     1.9214   0.278   0.7807    
as.factor(income_cat)poor                                        -3.1858     0.4109  -7.753 9.54e-15 ***
as.factor(income_cat)rich                                        -1.1093     0.4429  -2.504   0.0123 *  
Perfluorohexane_sulfonic_acid_comment:as.factor(income_cat)poor   1.4593     2.8818   0.506   0.6126    
Perfluorohexane_sulfonic_acid_comment:as.factor(income_cat)rich  -0.3310     5.0075  -0.066   0.9473    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 446.2597)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6630973  on 14859  degrees of freedom
AIC: 132883

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desincome_1, family = "gaussian", data = income_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_1)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            43.7980     0.5854  74.824   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   4.8465     3.1771   1.525     0.13    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 441.2123)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-1.437604 11.130584 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desincome_2, family = "gaussian", data = income_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_2)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            46.0645     0.3354 137.324   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   4.7764     2.8314   1.687    0.094 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 416.3376)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
-0.8241086 10.3768716 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desincome_3, family = "gaussian", data = income_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_3)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            45.7808     0.4082 112.153  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment  10.7196     2.5963   4.129  6.4e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 282.1085)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
 5.584299 15.854995 

Call:
glm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Perfluorononanoic_acid_comment * as.factor(income_cat), data = Fulldat_Pheno)

Coefficients:
                                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                               48.8430     0.2524 193.543  < 2e-16 ***
Perfluorononanoic_acid_comment                            -0.3606     1.8558  -0.194   0.8459    
as.factor(income_cat)poor                                 -3.2382     0.4107  -7.885 3.37e-15 ***
as.factor(income_cat)rich                                 -1.1353     0.4432  -2.562   0.0104 *  
Perfluorononanoic_acid_comment:as.factor(income_cat)poor   4.3165     2.9166   1.480   0.1389    
Perfluorononanoic_acid_comment:as.factor(income_cat)rich   1.8975     4.6286   0.410   0.6818    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 446.1901)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6629939  on 14859  degrees of freedom
AIC: 132881

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desincome_1, family = "gaussian", data = income_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_1)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     43.7367     0.5623  77.780   <2e-16 ***
Perfluorononanoic_acid_comment   6.0499     5.1099   1.184    0.239    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 440.735)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-4.057274 16.157071 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desincome_2, family = "gaussian", data = income_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_2)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     46.1588     0.3387 136.265   <2e-16 ***
Perfluorononanoic_acid_comment  -1.5335     2.4450  -0.627    0.532    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 416.6043)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-6.369658  3.302675 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desincome_3, family = "gaussian", data = income_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_3)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      45.820      0.409 112.023   <2e-16 ***
Perfluorononanoic_acid_comment    1.258      2.977   0.423    0.673    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 282.6104)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-4.629578  7.145410 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    perfluorooctanoic_acid_comment * as.factor(income_cat), data = Fulldat_Pheno)

Coefficients:
                                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                               47.5521     0.3193 148.931  < 2e-16 ***
perfluorooctanoic_acid_comment                             3.2695     0.5723   5.713 1.14e-08 ***
as.factor(income_cat)poor                                 -4.5009     0.5280  -8.525  < 2e-16 ***
as.factor(income_cat)rich                                 -1.6840     0.5661  -2.975  0.00294 ** 
perfluorooctanoic_acid_comment:as.factor(income_cat)poor   3.5816     0.9205   3.891  0.00010 ***
perfluorooctanoic_acid_comment:as.factor(income_cat)rich   1.2359     1.0058   1.229  0.21919    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 440.4061)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5726160  on 13002  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 116112

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desincome_1, family = "gaussian", data = income_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_1)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     40.7676     0.7468  54.589  < 2e-16 ***
perfluorooctanoic_acid_comment   7.4070     1.2751   5.809 5.43e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 423.0061)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
4.882005 9.932084 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desincome_2, family = "gaussian", data = income_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_2)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.7972     0.4143 108.128  < 2e-16 ***
perfluorooctanoic_acid_comment   3.0188     0.8252   3.658 0.000381 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 412.2822)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
1.384649 4.652869 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desincome_3, family = "gaussian", data = income_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_3)

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     43.3087     0.4862  89.072  < 2e-16 ***
perfluorooctanoic_acid_comment   5.7081     0.9182   6.217 7.93e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 271.4329)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.889829 7.526388 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    perfluorooctane_sulfonic_acid_comment * as.factor(income_cat), 
    data = Fulldat_Pheno)

Coefficients:
                                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                                      48.5178     0.2672 181.553  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment                             8.5620     3.4333   2.494  0.01265 *  
as.factor(income_cat)poor                                        -3.1935     0.4360  -7.325 2.53e-13 ***
as.factor(income_cat)rich                                        -1.2161     0.4715  -2.579  0.00992 ** 
perfluorooctane_sulfonic_acid_comment:as.factor(income_cat)poor   0.8386     5.5913   0.150  0.88078    
perfluorooctane_sulfonic_acid_comment:as.factor(income_cat)rich  -7.0113     7.2396  -0.968  0.33283    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 445.2048)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5788553  on 13002  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 116253

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desincome_1, family = "gaussian", data = income_1)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_1)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            43.4566     0.6436  67.525   <2e-16 ***
perfluorooctane_sulfonic_acid_comment  10.4627     5.6939   1.838   0.0686 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 435.1673)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
-0.8127987 21.7382678 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desincome_2, family = "gaussian", data = income_2)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_2)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            45.7366     0.3736 122.433   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   8.1083     6.7070   1.209    0.229    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 413.8825)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-5.173391 21.389953 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desincome_3, family = "gaussian", data = income_3)

Survey design:
svydesign(id = ~psu, strata = ~Strata, weights = ~weight_2, nest = TRUE, 
    data = income_3)

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            45.3364     0.4265 106.293   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   6.0052     4.9527   1.213    0.228    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 278.937)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-3.802421 15.812870 

#for subgroup analysis by cancer


Call:
glm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Perfluorohexane_sulfonic_acid_comment * had_cancer, data = Fulldat_Pheno)

Coefficients:
                                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                       75.7111     0.9614  78.752  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment             41.8556    10.2958   4.065 4.82e-05 ***
had_cancer                                       -13.6394     0.4957 -27.516  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment:had_cancer -20.9700     5.2801  -3.972 7.18e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 387.2293)

    Null deviance: 5710471  on 13949  degrees of freedom
Residual deviance: 5400300  on 13946  degrees of freedom
  (因為不存在,915 個觀察量被刪除了)
AIC: 122723

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_cancer, family = "gaussian", data = cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            63.0996     0.6524  96.722  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment  24.6134     5.1921   4.741  5.7e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 296.0554)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
14.33766 34.88908 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.6245     0.2667 167.301   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   4.8407     2.0529   2.358   0.0198 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.6811)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
0.780117 8.901293 

Call:
glm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Perfluorononanoic_acid_comment * had_cancer, data = Fulldat_Pheno)

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                75.8496     0.9632  78.749   <2e-16 ***
Perfluorononanoic_acid_comment             19.4063     8.9582   2.166   0.0303 *  
had_cancer                                -13.7103     0.4966 -27.611   <2e-16 ***
Perfluorononanoic_acid_comment:had_cancer  -9.5740     4.6298  -2.068   0.0387 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 387.5565)

    Null deviance: 5710471  on 13949  degrees of freedom
Residual deviance: 5404863  on 13946  degrees of freedom
  (因為不存在,915 個觀察量被刪除了)
AIC: 122734

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_cancer, family = "gaussian", data = cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     63.1997     0.6538  96.663   <2e-16 ***
Perfluorononanoic_acid_comment   6.0164     3.0687   1.961   0.0522 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 299.935)

Number of Fisher Scoring iterations: 2

      2.5 %      97.5 % 
-0.05692562 12.08966778 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.6663     0.2648 168.688   <2e-16 ***
Perfluorononanoic_acid_comment   1.1880     2.6807   0.443    0.658    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.9529)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-4.114385  6.490335 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    perfluorooctanoic_acid_comment * had_cancer, data = Fulldat_Pheno)

Coefficients:
                                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                74.8563     1.2378  60.477   <2e-16 ***
perfluorooctanoic_acid_comment              0.8729     2.1144   0.413    0.680    
had_cancer                                -13.9648     0.6377 -21.899   <2e-16 ***
perfluorooctanoic_acid_comment:had_cancer   1.4983     1.0887   1.376    0.169    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 383.9667)

    Null deviance: 4981260  on 12199  degrees of freedom
Residual deviance: 4682858  on 12196  degrees of freedom
  (因為不存在,2665 個觀察量被刪除了)
AIC: 107225

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_cancer, family = "gaussian", data = cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     60.5950     0.8363  72.455  < 2e-16 ***
perfluorooctanoic_acid_comment   6.5519     1.3921   4.706 7.32e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 291.0506)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.793370 9.310522 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     42.8717     0.3396 126.252  < 2e-16 ***
perfluorooctanoic_acid_comment   4.0902     0.6417   6.374 3.71e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 326.0826)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
2.819480 5.360832 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    perfluorooctane_sulfonic_acid_comment * had_cancer, data = Fulldat_Pheno)

Coefficients:
                                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                       75.0095     1.0088  74.352   <2e-16 ***
perfluorooctane_sulfonic_acid_comment             27.7201    17.7856   1.559    0.119    
had_cancer                                       -13.4243     0.5196 -25.835   <2e-16 ***
perfluorooctane_sulfonic_acid_comment:had_cancer -10.9960     9.1375  -1.203    0.229    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 386.8113)

    Null deviance: 4981260  on 12199  degrees of freedom
Residual deviance: 4717550  on 12196  degrees of freedom
  (因為不存在,2665 個觀察量被刪除了)
AIC: 107315

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_cancer, family = "gaussian", data = cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            63.0113     0.7144  88.202   <2e-16 ***
perfluorooctane_sulfonic_acid_comment  11.6881    10.3676   1.127    0.262    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 300.3849)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-8.856116 32.232264 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.2510     0.2882 153.558   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   7.7446     3.8173   2.029   0.0447 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 329.6064)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
 0.1851965 15.3039686 

#for subgroup analysis by smoking


Call:
glm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Perfluorohexane_sulfonic_acid_comment * now_smoke, data = Fulldat_Pheno)

Coefficients:
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      43.5632     0.2101 207.302  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment            -1.4932     1.6368  -0.912  0.36164    
now_smoke                                         4.4584     0.1366  32.648  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment:now_smoke   2.8820     1.0975   2.626  0.00865 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 416.8448)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6194730  on 14861  degrees of freedom
AIC: 131867

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = descurrent_smokers, family = "gaussian", data = current_smokers)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                             44.160      0.417  105.90   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment    9.247      5.674    1.63    0.106    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 274.3357)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-1.978179 20.473070 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desformer_smokers, family = "gaussian", data = former_smokers)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            54.0914     0.5969  90.616  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment  11.0210     4.0342   2.732  0.00715 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 367.2045)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
 3.041471 19.000500 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = desnon_smokers, family = "gaussian", data = non_smokers)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.0550     0.3043 144.799   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   0.5409     2.2402   0.241     0.81    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 356.7822)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-3.890134  4.972002 

Call:
glm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Perfluorononanoic_acid_comment * now_smoke, data = Fulldat_Pheno)

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                               43.5222     0.2102 207.052   <2e-16 ***
Perfluorononanoic_acid_comment             1.0292     1.6315   0.631    0.528    
now_smoke                                  4.4963     0.1366  32.913   <2e-16 ***
Perfluorononanoic_acid_comment:now_smoke   0.4225     1.0860   0.389    0.697    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 417.0192)

    Null deviance: 6658180  on 14864  degrees of freedom
Residual deviance: 6197322  on 14861  degrees of freedom
AIC: 131874

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = descurrent_smokers, family = "gaussian", data = current_smokers)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.1930     0.4224 104.619   <2e-16 ***
Perfluorononanoic_acid_comment   5.2015     5.3250   0.977     0.33    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 274.9869)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-5.333347 15.736332 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desformer_smokers, family = "gaussian", data = former_smokers)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     54.2537     0.5881  92.249   <2e-16 ***
Perfluorononanoic_acid_comment  -1.9596     4.2898  -0.457    0.649    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 368.6172)

Number of Fisher Scoring iterations: 2

     2.5 %     97.5 % 
-10.444699   6.525441 

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = desnon_smokers, family = "gaussian", data = non_smokers)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.0493     0.3003 146.692   <2e-16 ***
Perfluorononanoic_acid_comment   0.7363     3.6336   0.203     0.84    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 356.7769)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-6.450841  7.923374 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    perfluorooctanoic_acid_comment * now_smoke, data = Fulldat_Pheno)

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)                               41.3974     0.2699 153.392   <2e-16 ***
perfluorooctanoic_acid_comment             5.3391     0.4712  11.331   <2e-16 ***
now_smoke                                  4.8071     0.1734  27.715   <2e-16 ***
perfluorooctanoic_acid_comment:now_smoke  -0.6292     0.3086  -2.039   0.0415 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 410.168)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5333824  on 13004  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 115185

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = descurrent_smokers, family = "gaussian", data = current_smokers)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     42.2697     0.5136  82.303  < 2e-16 ***
perfluorooctanoic_acid_comment   5.3199     1.0814   4.919 2.92e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 265.9013)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
3.177818 7.462036 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desformer_smokers, family = "gaussian", data = former_smokers)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     52.5025     0.7457  70.403  < 2e-16 ***
perfluorooctanoic_acid_comment   3.7237     1.1678   3.189  0.00183 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 368.4706)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
1.411135 6.036187 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = desnon_smokers, family = "gaussian", data = non_smokers)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     42.5075     0.3886 109.376  < 2e-16 ***
perfluorooctanoic_acid_comment   3.4130     0.7474   4.566 1.23e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 348.2842)

Number of Fisher Scoring iterations: 2

   2.5 %   97.5 % 
1.932894 4.893039 

Call:
glm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    perfluorooctane_sulfonic_acid_comment * now_smoke, data = Fulldat_Pheno)

Coefficients:
                                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                      43.1166     0.2231 193.281   <2e-16 ***
perfluorooctane_sulfonic_acid_comment             6.3177     3.1663   1.995    0.046 *  
now_smoke                                         4.5665     0.1447  31.566   <2e-16 ***
perfluorooctane_sulfonic_acid_comment:now_smoke   0.7731     1.9496   0.397    0.692    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 414.9806)

    Null deviance: 5817331  on 13007  degrees of freedom
Residual deviance: 5396408  on 13004  degrees of freedom
  (因為不存在,1857 個觀察量被刪除了)
AIC: 115336

Number of Fisher Scoring iterations: 2


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = descurrent_smokers, family = "gaussian", data = current_smokers)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            43.7170     0.4515  96.828   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   7.9841     8.9871   0.888    0.376    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 271.2259)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-9.817562 25.785748 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desformer_smokers, family = "gaussian", data = former_smokers)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            53.7430     0.6469  83.079   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   6.9414     7.0781   0.981    0.329    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 371.3435)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-7.075226 20.957953 

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = desnon_smokers, family = "gaussian", data = non_smokers)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            43.7724     0.3362 130.191   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   6.8880     4.6262   1.489    0.139    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 350.8561)

Number of Fisher Scoring iterations: 2

    2.5 %    97.5 % 
-2.273276 16.049191 

#sensitivity analysis without cancer patient only for pfas

#subset cancer
cancer <- Fulldat_Pheno[Fulldat_Pheno$had_cancer == 1, ]
cancer$had_cancer[is.na(cancer$had_cancer)] <- 0
cancer <- cancer[cancer$had_cancer != 0, , drop = FALSE]

non_cancer <- Fulldat_Pheno[Fulldat_Pheno$had_cancer == 2, ]
non_cancer$had_cancer[is.na(non_cancer$had_cancer)] <- 0
non_cancer <- non_cancer[non_cancer$had_cancer != 0, , drop = FALSE]

des_cancer <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = cancer) 
des_non_cancer <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = non_cancer) 

#sensitivity Perfluorohexane_sulfonic_acid


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.6245     0.2667 167.301   <2e-16 ***
Perfluorohexane_sulfonic_acid_comment   4.8407     2.0529   2.358   0.0198 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.6811)

Number of Fisher Scoring iterations: 2

                                          2.5 %    97.5 %
(Intercept)                           44.096876 45.152048
Perfluorohexane_sulfonic_acid_comment  0.780117  8.901293

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + 
    Gender + Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            47.9338     1.3134  36.496  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment   4.6886     1.9332   2.425  0.01668 *  
Gender                                  0.3061     0.4137   0.740  0.46064    
Race                                    1.0849     0.1700   6.381 2.90e-09 ***
Marital_Status                         -2.3801     0.3331  -7.145 5.91e-11 ***
Ratio_income_poverty                   -0.5599     0.1439  -3.890  0.00016 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 307.343)

Number of Fisher Scoring iterations: 2

                                           2.5 %     97.5 %
(Intercept)                           45.3352022 50.5324081
Perfluorohexane_sulfonic_acid_comment  0.8637622  8.5134489
Gender                                -0.5123234  1.1245417
Race                                   0.7485283  1.4213326
Marital_Status                        -3.0391992 -1.7210311
Ratio_income_poverty                  -0.8446808 -0.2751171

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_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            3.996e+01  2.583e+00  15.469  < 2e-16 ***
Perfluorohexane_sulfonic_acid_comment  7.679e+00  2.650e+00   2.897 0.004691 ** 
Gender                                 1.525e-01  4.727e-01   0.323 0.747713    
Race                                   7.169e-01  1.769e-01   4.053 0.000105 ***
Marital_Status                        -2.189e+00  3.863e-01  -5.666 1.62e-07 ***
Ratio_income_poverty                  -2.312e-01  1.806e-01  -1.280 0.203647    
BMI                                    2.349e-01  3.485e-02   6.740 1.31e-09 ***
sleep_disorders                       -4.991e+00  5.049e-01  -9.885 3.58e-16 ***
Smoked_days                            9.967e-01  8.127e-01   1.226 0.223145    
now_smoke                              2.434e+00  3.993e-01   6.096 2.45e-08 ***
quit_smoking                           2.912e-04  6.706e-05   4.342 3.59e-05 ***
Avg_alcohol_drinks                     5.371e+00  6.023e-01   8.917 3.99e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 288.0169)

Number of Fisher Scoring iterations: 2

                                              2.5 %        97.5 %
(Intercept)                           34.8328811768 45.0934463310
Perfluorohexane_sulfonic_acid_comment  2.4159859124 12.9416684175
Gender                                -0.7862627192  1.0912887563
Race                                   0.3656500544  1.0682149753
Marital_Status                        -2.9559685772 -1.4217559455
Ratio_income_poverty                  -0.5897338971  0.1274013315
BMI                                    0.1656797446  0.3041012141
sleep_disorders                       -5.9939793422 -3.9886144643
Smoked_days                           -0.6172020322  2.6107003042
now_smoke                              1.6411007966  3.2267977261
quit_smoking                           0.0001579966  0.0004243381
Avg_alcohol_drinks                     4.1750170529  6.5672457695

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   44.14480    0.32210 137.053   <2e-16 ***
Perfluorohexane_sulfonic_acid  0.25101    0.08813   2.848   0.0051 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.5312)

Number of Fisher Scoring iterations: 2

                                   2.5 %     97.5 %
(Intercept)                   43.5076977 44.7819023
Perfluorohexane_sulfonic_acid  0.0766944  0.4253203

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorohexane_sulfonic_acid + 
    Gender + Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   47.09354    1.34231  35.084  < 2e-16 ***
Perfluorohexane_sulfonic_acid  0.30086    0.08999   3.343  0.00108 ** 
Gender                         0.61227    0.43378   1.411  0.16051    
Race                           1.05462    0.16697   6.316 3.99e-09 ***
Marital_Status                -2.38623    0.33251  -7.176 5.01e-11 ***
Ratio_income_poverty          -0.59834    0.14373  -4.163 5.70e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 307.0024)

Number of Fisher Scoring iterations: 2

                                   2.5 %     97.5 %
(Intercept)                   44.4377506 49.7493295
Perfluorohexane_sulfonic_acid  0.1228207  0.4789039
Gender                        -0.2459748  1.4705115
Race                           0.7242583  1.3849762
Marital_Status                -3.0441098 -1.7283599
Ratio_income_poverty          -0.8827154 -0.3139705

Call:
svyglm(formula = Phenotypic_Age ~ 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_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   38.8731466  2.6195615  14.840  < 2e-16 ***
Perfluorohexane_sulfonic_acid  0.4177519  0.1157809   3.608 0.000499 ***
Gender                         0.5831319  0.4934330   1.182 0.240304    
Race                           0.6846621  0.1745361   3.923 0.000167 ***
Marital_Status                -2.1975299  0.3846951  -5.712 1.33e-07 ***
Ratio_income_poverty          -0.2680841  0.1799041  -1.490 0.139569    
BMI                            0.2359523  0.0353886   6.667 1.83e-09 ***
sleep_disorders               -5.0831381  0.5045544 -10.075  < 2e-16 ***
Smoked_days                    0.9952885  0.8120290   1.226 0.223414    
now_smoke                      2.4179816  0.3954564   6.114 2.26e-08 ***
quit_smoking                   0.0002883  0.0000667   4.323 3.86e-05 ***
Avg_alcohol_drinks             5.3855927  0.5877336   9.163 1.21e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 287.373)

Number of Fisher Scoring iterations: 2

                                     2.5 %        97.5 %
(Intercept)                   33.671216632 44.0750765342
Perfluorohexane_sulfonic_acid  0.187833929  0.6476698877
Gender                        -0.396728155  1.5629920410
Race                           0.338067991  1.0312562703
Marital_Status                -2.961458121 -1.4336015862
Ratio_income_poverty          -0.625338010  0.0891698428
BMI                            0.165677455  0.3062271188
sleep_disorders               -6.085083025 -4.0811930892
Smoked_days                   -0.617240144  2.6078171320
now_smoke                      1.632683518  3.2032796403
quit_smoking                   0.000155883  0.0004207742
Avg_alcohol_drinks             4.218470355  6.5527150577

#sensitivity “Perfluorononanoic_acid” “Perfluorononanoic_acid_comment”


Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     44.6663     0.2648 168.688   <2e-16 ***
Perfluorononanoic_acid_comment   1.1880     2.6807   0.443    0.658    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.9529)

Number of Fisher Scoring iterations: 2

                                   2.5 %    97.5 %
(Intercept)                    44.142540 45.190011
Perfluorononanoic_acid_comment -4.114385  6.490335

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid_comment + 
    Gender + Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     48.0352     1.3026  36.877  < 2e-16 ***
Perfluorononanoic_acid_comment   0.1751     2.7754   0.063    0.950    
Gender                           0.3206     0.4154   0.772    0.442    
Race                             1.0799     0.1685   6.411 2.50e-09 ***
Marital_Status                  -2.3806     0.3329  -7.152 5.71e-11 ***
Ratio_income_poverty            -0.5780     0.1427  -4.050 8.79e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 307.6181)

Number of Fisher Scoring iterations: 2

                                    2.5 %     97.5 %
(Intercept)                    45.4579947 50.6123579
Perfluorononanoic_acid_comment -5.3161253  5.6662909
Gender                         -0.5012299  1.1424063
Race                            0.7466336  1.4132423
Marital_Status                 -3.0392598 -1.7220375
Ratio_income_poverty           -0.8603372 -0.2956128

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_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     4.019e+01  2.592e+00  15.507  < 2e-16 ***
Perfluorononanoic_acid_comment -1.286e+00  3.342e+00  -0.385 0.701311    
Gender                          1.665e-01  4.728e-01   0.352 0.725597    
Race                            7.142e-01  1.763e-01   4.051 0.000106 ***
Marital_Status                 -2.195e+00  3.865e-01  -5.679 1.54e-07 ***
Ratio_income_poverty           -2.565e-01  1.779e-01  -1.442 0.152629    
BMI                             2.340e-01  3.575e-02   6.546 3.19e-09 ***
sleep_disorders                -4.987e+00  5.040e-01  -9.896 3.40e-16 ***
Smoked_days                     9.452e-01  8.231e-01   1.148 0.253789    
now_smoke                       2.423e+00  4.007e-01   6.049 3.02e-08 ***
quit_smoking                    2.907e-04  6.759e-05   4.302 4.18e-05 ***
Avg_alcohol_drinks              5.416e+00  6.005e-01   9.019 2.43e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 288.5362)

Number of Fisher Scoring iterations: 2

                                       2.5 %       97.5 %
(Intercept)                    35.0409259637 45.333747377
Perfluorononanoic_acid_comment -7.9227808383  5.351060556
Gender                         -0.7725009841  1.105426536
Race                            0.3641059836  1.064373111
Marital_Status                 -2.9619318041 -1.427077179
Ratio_income_poverty           -0.6096681416  0.096696515
BMI                             0.1630390087  0.305024709
sleep_disorders                -5.9883255709 -3.986590238
Smoked_days                    -0.6893419887  2.579667087
now_smoke                       1.6278477767  3.219093982
quit_smoking                    0.0001565206  0.000424962
Avg_alcohol_drinks              4.2233816526  6.608133195

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             43.9516     0.3758 116.955  < 2e-16 ***
Perfluorononanoic_acid   0.6656     0.2056   3.237  0.00153 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.3964)

Number of Fisher Scoring iterations: 2

                            2.5 %    97.5 %
(Intercept)            43.2082833 44.694915
Perfluorononanoic_acid  0.2588411  1.072336

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid + Gender + 
    Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             47.4083     1.3231  35.831  < 2e-16 ***
Perfluorononanoic_acid   0.6596     0.2125   3.104  0.00234 ** 
Gender                   0.4240     0.4207   1.008  0.31542    
Race                     1.0169     0.1724   5.900 3.02e-08 ***
Marital_Status          -2.3824     0.3334  -7.145 5.91e-11 ***
Ratio_income_poverty    -0.5993     0.1440  -4.162 5.73e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 307.0567)

Number of Fisher Scoring iterations: 2

                            2.5 %    97.5 %
(Intercept)            44.7905046 50.026057
Perfluorononanoic_acid  0.2392246  1.080022
Gender                 -0.4083493  1.256300
Race                    0.6758467  1.357902
Marital_Status         -3.0420943 -1.722675
Ratio_income_poverty   -0.8842295 -0.314377

Call:
svyglm(formula = Phenotypic_Age ~ 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_non_cancer, family = "gaussian", 
    data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             3.955e+01  2.608e+00  15.164  < 2e-16 ***
Perfluorononanoic_acid  7.218e-01  3.264e-01   2.211 0.029481 *  
Gender                  2.565e-01  4.801e-01   0.534 0.594420    
Race                    6.566e-01  1.817e-01   3.613 0.000491 ***
Marital_Status         -2.194e+00  3.869e-01  -5.672 1.59e-07 ***
Ratio_income_poverty   -2.706e-01  1.800e-01  -1.503 0.136104    
BMI                     2.364e-01  3.555e-02   6.650 1.98e-09 ***
sleep_disorders        -4.988e+00  5.004e-01  -9.969 2.38e-16 ***
Smoked_days             9.496e-01  8.209e-01   1.157 0.250304    
now_smoke               2.407e+00  4.012e-01   6.000 3.75e-08 ***
quit_smoking            2.917e-04  6.735e-05   4.332 3.74e-05 ***
Avg_alcohol_drinks      5.323e+00  5.950e-01   8.946 3.47e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 288.0303)

Number of Fisher Scoring iterations: 2

                               2.5 %        97.5 %
(Intercept)            34.3742135038 44.7338950215
Perfluorononanoic_acid  0.0735412109  1.3699827539
Gender                 -0.6968949138  1.2099297246
Race                    0.2957397001  1.0175420152
Marital_Status         -2.9628093087 -1.4261331528
Ratio_income_poverty   -0.6279024296  0.0867946557
BMI                     0.1658137688  0.3070088090
sleep_disorders        -5.9819025722 -3.9946017479
Smoked_days            -0.6804663797  2.5796495655
now_smoke               1.6105129008  3.2039159863
quit_smoking            0.0001579776  0.0004254562
Avg_alcohol_drinks      4.1414970649  6.5046075139

#sensitivity “perfluorooctanoic_acid” “perfluorooctanoic_acid_comment”


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     42.8717     0.3396 126.252  < 2e-16 ***
perfluorooctanoic_acid_comment   4.0902     0.6417   6.374 3.71e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 326.0826)

Number of Fisher Scoring iterations: 2

                                  2.5 %    97.5 %
(Intercept)                    42.19925 43.544148
perfluorooctanoic_acid_comment  2.81948  5.360832

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctanoic_acid_comment + 
    Gender + Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     46.5175     1.4610  31.840  < 2e-16 ***
perfluorooctanoic_acid_comment   2.8040     0.6146   4.563 1.28e-05 ***
Gender                           0.2469     0.4574   0.540 0.590442    
Race                             1.0963     0.1843   5.947 3.05e-08 ***
Marital_Status                  -2.2790     0.3505  -6.502 2.19e-09 ***
Ratio_income_poverty            -0.5908     0.1476  -4.003 0.000112 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 302.9741)

Number of Fisher Scoring iterations: 2

                                    2.5 %     97.5 %
(Intercept)                    43.6233018 49.4117501
perfluorooctanoic_acid_comment  1.5865894  4.0214639
Gender                         -0.6592372  1.1529739
Race                            0.7311270  1.4614234
Marital_Status                 -2.9733926 -1.5845999
Ratio_income_poverty           -0.8831203 -0.2983799

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_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                     3.801e+01  2.865e+00  13.267  < 2e-16 ***
perfluorooctanoic_acid_comment  3.058e+00  7.163e-01   4.270 5.47e-05 ***
Gender                          1.948e-01  5.391e-01   0.361 0.718846    
Race                            6.683e-01  1.999e-01   3.344 0.001272 ** 
Marital_Status                 -2.066e+00  4.138e-01  -4.993 3.55e-06 ***
Ratio_income_poverty           -2.646e-01  1.853e-01  -1.428 0.157318    
BMI                             2.408e-01  3.795e-02   6.344 1.35e-08 ***
sleep_disorders                -4.786e+00  5.702e-01  -8.394 1.62e-12 ***
Smoked_days                     5.076e-01  8.455e-01   0.600 0.550020    
now_smoke                       2.275e+00  4.399e-01   5.172 1.75e-06 ***
quit_smoking                    2.561e-04  7.374e-05   3.473 0.000843 ***
Avg_alcohol_drinks              5.763e+00  6.914e-01   8.336 2.10e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 283.5154)

Number of Fisher Scoring iterations: 2

                                       2.5 %        97.5 %
(Intercept)                    32.3092126911 43.7182070367
perfluorooctanoic_acid_comment  1.6323183971  4.4845391004
Gender                         -0.8785241712  1.2681037908
Race                            0.2703821815  1.0661836237
Marital_Status                 -2.8899322263 -1.2421946625
Ratio_income_poverty           -0.6336256590  0.1043396622
BMI                             0.1652039405  0.3163146103
sleep_disorders                -5.9209561460 -3.6507536821
Smoked_days                    -1.1756193016  2.1907472244
now_smoke                       1.3992871675  3.1508405298
quit_smoking                    0.0001092944  0.0004029129
Avg_alcohol_drinks              4.3868486166  7.1396432900

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             43.9516     0.3758 116.955  < 2e-16 ***
Perfluorononanoic_acid   0.6656     0.2056   3.237  0.00153 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 332.3964)

Number of Fisher Scoring iterations: 2

                            2.5 %    97.5 %
(Intercept)            43.2082833 44.694915
Perfluorononanoic_acid  0.2588411  1.072336

Call:
svyglm(formula = Phenotypic_Age ~ Perfluorononanoic_acid + Gender + 
    Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)             47.4083     1.3231  35.831  < 2e-16 ***
Perfluorononanoic_acid   0.6596     0.2125   3.104  0.00234 ** 
Gender                   0.4240     0.4207   1.008  0.31542    
Race                     1.0169     0.1724   5.900 3.02e-08 ***
Marital_Status          -2.3824     0.3334  -7.145 5.91e-11 ***
Ratio_income_poverty    -0.5993     0.1440  -4.162 5.73e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 307.0567)

Number of Fisher Scoring iterations: 2

                            2.5 %    97.5 %
(Intercept)            44.7905046 50.026057
Perfluorononanoic_acid  0.2392246  1.080022
Gender                 -0.4083493  1.256300
Race                    0.6758467  1.357902
Marital_Status         -3.0420943 -1.722675
Ratio_income_poverty   -0.8842295 -0.314377

Call:
svyglm(formula = Phenotypic_Age ~ 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_non_cancer, family = "gaussian", 
    data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)             3.955e+01  2.608e+00  15.164  < 2e-16 ***
Perfluorononanoic_acid  7.218e-01  3.264e-01   2.211 0.029481 *  
Gender                  2.565e-01  4.801e-01   0.534 0.594420    
Race                    6.566e-01  1.817e-01   3.613 0.000491 ***
Marital_Status         -2.194e+00  3.869e-01  -5.672 1.59e-07 ***
Ratio_income_poverty   -2.706e-01  1.800e-01  -1.503 0.136104    
BMI                     2.364e-01  3.555e-02   6.650 1.98e-09 ***
sleep_disorders        -4.988e+00  5.004e-01  -9.969 2.38e-16 ***
Smoked_days             9.496e-01  8.209e-01   1.157 0.250304    
now_smoke               2.407e+00  4.012e-01   6.000 3.75e-08 ***
quit_smoking            2.917e-04  6.735e-05   4.332 3.74e-05 ***
Avg_alcohol_drinks      5.323e+00  5.950e-01   8.946 3.47e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 288.0303)

Number of Fisher Scoring iterations: 2

                               2.5 %        97.5 %
(Intercept)            34.3742135038 44.7338950215
Perfluorononanoic_acid  0.0735412109  1.3699827539
Gender                 -0.6968949138  1.2099297246
Race                    0.2957397001  1.0175420152
Marital_Status         -2.9628093087 -1.4261331528
Ratio_income_poverty   -0.6279024296  0.0867946557
BMI                     0.1658137688  0.3070088090
sleep_disorders        -5.9819025722 -3.9946017479
Smoked_days            -0.6804663797  2.5796495655
now_smoke               1.6105129008  3.2039159863
quit_smoking            0.0001579776  0.0004254562
Avg_alcohol_drinks      4.1414970649  6.5046075139

#sensitivity “perfluorooctane_sulfonic_acid” “perfluorooctane_sulfonic_acid_comment”


Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            44.2510     0.2882 153.558   <2e-16 ***
perfluorooctane_sulfonic_acid_comment   7.7446     3.8173   2.029   0.0447 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 329.6064)

Number of Fisher Scoring iterations: 2

                                           2.5 %   97.5 %
(Intercept)                           43.6803876 44.82171
perfluorooctane_sulfonic_acid_comment  0.1851965 15.30397

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + 
    Gender + Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            47.5978     1.4537  32.741  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment   6.7724     4.1106   1.648    0.102    
Gender                                  0.2341     0.4558   0.514    0.608    
Race                                    1.1319     0.1806   6.267 6.77e-09 ***
Marital_Status                         -2.3566     0.3583  -6.576 1.52e-09 ***
Ratio_income_poverty                   -0.6091     0.1499  -4.063 8.91e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 304.5168)

Number of Fisher Scoring iterations: 2

                                           2.5 %     97.5 %
(Intercept)                           44.7179130 50.4776388
perfluorooctane_sulfonic_acid_comment -1.3706028 14.9153574
Gender                                -0.6688177  1.1371125
Race                                   0.7740852  1.4897118
Marital_Status                        -3.0664531 -1.6467068
Ratio_income_poverty                  -0.9061037 -0.3121795

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_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                                        Estimate Std. Error t value Pr(>|t|)    
(Intercept)                            3.953e+01  2.814e+00  14.051  < 2e-16 ***
perfluorooctane_sulfonic_acid_comment  3.705e+00  4.435e+00   0.835 0.406105    
Gender                                 2.346e-01  5.318e-01   0.441 0.660384    
Race                                   7.220e-01  1.954e-01   3.695 0.000407 ***
Marital_Status                        -2.164e+00  4.265e-01  -5.075 2.57e-06 ***
Ratio_income_poverty                  -2.987e-01  1.905e-01  -1.568 0.120972    
BMI                                    2.515e-01  3.755e-02   6.698 2.94e-09 ***
sleep_disorders                       -4.903e+00  5.756e-01  -8.517 9.35e-13 ***
Smoked_days                            7.775e-01  8.761e-01   0.887 0.377564    
now_smoke                              2.304e+00  4.449e-01   5.179 1.70e-06 ***
quit_smoking                           2.678e-04  7.851e-05   3.412 0.001026 ** 
Avg_alcohol_drinks                     5.266e+00  6.616e-01   7.960 1.13e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 285.6733)

Number of Fisher Scoring iterations: 2

                                              2.5 %        97.5 %
(Intercept)                           33.9306843973 45.1332783819
perfluorooctane_sulfonic_acid_comment -5.1249050736 12.5339566441
Gender                                -0.8242260470  1.2933753840
Race                                   0.3329687354  1.1109959951
Marital_Status                        -3.0132009895 -1.3151539409
Ratio_income_poverty                  -0.6780677964  0.0806044187
BMI                                    0.1767467531  0.3262564670
sleep_disorders                       -6.0484691084 -3.7565571494
Smoked_days                           -0.9666376568  2.5215560905
now_smoke                              1.4183705021  3.1898613308
quit_smoking                           0.0001115446  0.0004241368
Avg_alcohol_drinks                     3.9492753760  6.5836638009

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid, 
    design = des_non_cancer, family = "gaussian", data = non_cancer)

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

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   42.39711    0.38146 111.145  < 2e-16 ***
perfluorooctane_sulfonic_acid  0.14275    0.01876   7.611 7.35e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 325.2768)

Number of Fisher Scoring iterations: 2

                                   2.5 %     97.5 %
(Intercept)                   41.6417238 43.1525040
perfluorooctane_sulfonic_acid  0.1056056  0.1798912

Call:
svyglm(formula = Phenotypic_Age ~ perfluorooctane_sulfonic_acid + 
    Gender + Race + Marital_Status + Ratio_income_poverty, design = des_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients:
                              Estimate Std. Error t value Pr(>|t|)    
(Intercept)                   45.41508    1.47519  30.786  < 2e-16 ***
perfluorooctane_sulfonic_acid  0.14235    0.01949   7.303 4.09e-11 ***
Gender                         0.92710    0.47248   1.962   0.0522 .  
Race                           0.93402    0.18073   5.168 1.02e-06 ***
Marital_Status                -2.34764    0.35687  -6.578 1.50e-09 ***
Ratio_income_poverty          -0.65878    0.14783  -4.456 1.96e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 300.3056)

Number of Fisher Scoring iterations: 2

                                     2.5 %     97.5 %
(Intercept)                   42.492734413 48.3374316
perfluorooctane_sulfonic_acid  0.103737361  0.1809591
Gender                        -0.008887653  1.8630785
Race                           0.575987110  1.2920505
Marital_Status                -3.054594756 -1.6406820
Ratio_income_poverty          -0.951636143 -0.3659303

Call:
svyglm(formula = Phenotypic_Age ~ 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_non_cancer, 
    family = "gaussian", data = non_cancer)

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

Coefficients: (1 not defined because of singularities)
                                Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    3.784e+01  2.838e+00  13.334  < 2e-16 ***
perfluorooctane_sulfonic_acid  2.058e-01  3.458e-02   5.951 7.17e-08 ***
Gender                         1.121e+00  5.384e-01   2.082  0.04067 *  
Race                           4.587e-01  1.921e-01   2.388  0.01935 *  
Marital_Status                -2.154e+00  4.234e-01  -5.089 2.43e-06 ***
Ratio_income_poverty          -3.337e-01  1.882e-01  -1.773  0.08016 .  
BMI                            2.634e-01  3.661e-02   7.196 3.33e-10 ***
sleep_disorders               -4.899e+00  5.582e-01  -8.776 2.93e-13 ***
Smoked_days                    5.631e-01  8.802e-01   0.640  0.52426    
now_smoke                      2.181e+00  4.369e-01   4.992 3.56e-06 ***
quit_smoking                   2.640e-04  7.732e-05   3.414  0.00102 ** 
Avg_alcohol_drinks             4.690e+00  6.532e-01   7.180 3.57e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 280.3996)

Number of Fisher Scoring iterations: 2

                                     2.5 %        97.5 %
(Intercept)                   32.189225501 43.4886172576
perfluorooctane_sulfonic_acid  0.136935351  0.2746181622
Gender                         0.048815800  2.1923827345
Race                           0.076327495  0.8410742090
Marital_Status                -2.997305379 -1.3115216220
Ratio_income_poverty          -0.708374254  0.0410278441
BMI                            0.190546568  0.3363054868
sleep_disorders               -6.010122389 -3.7875845326
Smoked_days                   -1.189334012  2.3154548770
now_smoke                      1.311100448  3.0508417498
quit_smoking                   0.000110048  0.0004179304
Avg_alcohol_drinks             3.389503821  5.9903518538
---
title: "R Notebook"
output:
  html_notebook: default
  pdf_document: default
  html_document:
    df_print: paged
---
```{r}
library("haven")
library("tidyverse")
library("dplyr")
library("foreign")
library("survey")
library("ggplot2")
library("car")
library("rms")
```


#list variable
```{r}
Fulldat_Pheno <- Falldat_Pheno
colnames(Fulldat_Pheno)
```
#Perfluorohexane_sulfonic_acid_comment
#Perfluorononanoic_acid_comment
#perfluorooctanoic_acid_comment
#perfluorooctane_sulfonic_acid_comment

#check effect modifiers Subgroup analysis for gender, race, BMI, income, smoking and cancer
#for subgroup analysis by gender
```{r echo=FALSE,message=FALSE,warning=TRUE}
men <- Fulldat_Pheno[Fulldat_Pheno$Gender == 1, ]
women <- Fulldat_Pheno[Fulldat_Pheno$Gender == 2, ]
desmen <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = men) #select men
deswomen <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = women) #select women

#Perfluorohexane_sulfonic_acid_comment
model_sex <- glm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Perfluorohexane_sulfonic_acid_comment*Gender, data = Fulldat_Pheno)
summary(model_sex)

model_men <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = men, design = desmen, family = "gaussian")
summary(model_men)
confint(model_men)["Perfluorohexane_sulfonic_acid_comment", ]  
model_women <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = women, design = deswomen, family = "gaussian")
summary(model_women)
confint(model_women)["Perfluorohexane_sulfonic_acid_comment", ]  

#Perfluorononanoic_acid_comment
model_sex <- glm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Perfluorononanoic_acid_comment*Gender, data = Fulldat_Pheno)
summary(model_sex)

model_men <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = men, design = desmen, family = "gaussian")
summary(model_men)
confint(model_men)["Perfluorononanoic_acid_comment", ]  
model_women <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = women, design = deswomen, family = "gaussian")
summary(model_women)
confint(model_women)["Perfluorononanoic_acid_comment", ]  

#perfluorooctanoic_acid_comment
model_sex <- glm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + perfluorooctanoic_acid_comment*Gender, data = Fulldat_Pheno)
summary(model_sex)

model_men <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = men, design = desmen, family = "gaussian")
summary(model_men)
confint(model_men)["perfluorooctanoic_acid_comment", ]  
model_women <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = women, design = deswomen, family = "gaussian")
summary(model_women)
confint(model_women)["perfluorooctanoic_acid_comment", ]  

#perfluorooctane_sulfonic_acid_comment
model_sex <- glm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + perfluorooctane_sulfonic_acid_comment*Gender, data = Fulldat_Pheno)
summary(model_sex)

model_men <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = men, design = desmen, family = "gaussian")
summary(model_men)
confint(model_men)["perfluorooctane_sulfonic_acid_comment", ]  
model_women <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = women, design = deswomen, family = "gaussian")
summary(model_women)
confint(model_women)["perfluorooctane_sulfonic_acid_comment", ]  

```
#for subgroup analysis by Race
```{r echo=FALSE,message=FALSE,warning=TRUE}
Mexican <- Fulldat_Pheno[Fulldat_Pheno$Race == 1, ]
Other_Hispanic <- Fulldat_Pheno[Fulldat_Pheno$Race == 2, ]
Non_Hispanic_white <- Fulldat_Pheno[Fulldat_Pheno$Race == 3, ]
Non_Hispanic_Black <- Fulldat_Pheno[Fulldat_Pheno$Race == 4, ]
Other_Race <- Fulldat_Pheno[Fulldat_Pheno$Race == 5, ]

des_Me <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Mexican) 
des_Hispanic <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Other_Hispanic) 
des_white <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Non_Hispanic_white) 
des_Black <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Non_Hispanic_Black) 
des_Other <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = Other_Race) 

options(survey.adjust.domain.lonely=TRUE)
options(survey.lonely.psu="adjust")

#Perfluorohexane_sulfonic_acid_comment
model_race <- glm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Perfluorohexane_sulfonic_acid_comment*as.factor(Race), data = Fulldat_Pheno)
summary(model_race)

model_Me <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = Mexican, design = des_Me, family = "gaussian")
summary(model_Me)
confint(model_Me)["Perfluorohexane_sulfonic_acid_comment", ]  
model_Hispanic <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = Other_Hispanic, design = des_Hispanic, family = "gaussian")
summary(model_Hispanic)
confint(model_Hispanic)["Perfluorohexane_sulfonic_acid_comment", ]  
model_white <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = Non_Hispanic_white, design = des_white, family = "gaussian")
summary(model_white)
confint(model_white)["Perfluorohexane_sulfonic_acid_comment", ]  
model_Black <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = Non_Hispanic_Black, design = des_Black, family = "gaussian")
summary(model_Black)
confint(model_Black)["Perfluorohexane_sulfonic_acid_comment", ]  
model_Other <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = Other_Race, design = des_Other, family = "gaussian")
summary(model_Other)
confint(model_Other)["Perfluorohexane_sulfonic_acid_comment", ]  


#Perfluorononanoic_acid_comment
model_race <- glm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Perfluorononanoic_acid_comment*as.factor(Race), data = Fulldat_Pheno)
summary(model_race)

model_Me <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = Mexican, design = des_Me, family = "gaussian")
summary(model_Me)
confint(model_Me)["Perfluorononanoic_acid_comment", ]  
model_Hispanic <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = Other_Hispanic, design = des_Hispanic, family = "gaussian")
summary(model_Hispanic)
confint(model_Hispanic)["Perfluorononanoic_acid_comment", ]  
model_white <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = Non_Hispanic_white, design = des_white, family = "gaussian")
summary(model_white)
confint(model_white)["Perfluorononanoic_acid_comment", ]  
model_Black <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = Non_Hispanic_Black, design = des_Black, family = "gaussian")
summary(model_Black)
confint(model_Black)["Perfluorononanoic_acid_comment", ]  
model_Other <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = Other_Race, design = des_Other, family = "gaussian")
summary(model_Other)
confint(model_Other)["Perfluorononanoic_acid_comment", ]  

#perfluorooctanoic_acid_comment
model_race <- glm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + perfluorooctanoic_acid_comment*as.factor(Race), data = Fulldat_Pheno)
summary(model_race)

model_Me <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = Mexican, design = des_Me, family = "gaussian")
summary(model_Me)
confint(model_Me)["perfluorooctanoic_acid_comment", ]  
model_Hispanic <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = Other_Hispanic, design = des_Hispanic, family = "gaussian")
summary(model_Hispanic)
confint(model_Hispanic)["perfluorooctanoic_acid_comment", ]  
model_white <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = Non_Hispanic_white, design = des_white, family = "gaussian")
summary(model_white)
confint(model_white)["perfluorooctanoic_acid_comment", ]  
model_Black <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = Non_Hispanic_Black, design = des_Black, family = "gaussian")
summary(model_Black)
confint(model_Black)["perfluorooctanoic_acid_comment", ]  
model_Other <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = Other_Race, design = des_Other, family = "gaussian")
summary(model_Other)
confint(model_Other)["perfluorooctanoic_acid_comment", ]  

#perfluorooctane_sulfonic_acid_comment
model_race <- glm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + perfluorooctane_sulfonic_acid_comment*as.factor(Race), data = Fulldat_Pheno)
summary(model_race)

model_Me <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = Mexican, design = des_Me, family = "gaussian")
summary(model_Me)
confint(model_Me)["perfluorooctane_sulfonic_acid_comment", ]  
model_Hispanic <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = Other_Hispanic, design = des_Hispanic, family = "gaussian")
summary(model_Hispanic)
confint(model_Hispanic)["perfluorooctane_sulfonic_acid_comment", ]  
model_white <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = Non_Hispanic_white, design = des_white, family = "gaussian")
summary(model_white)
confint(model_white)["perfluorooctane_sulfonic_acid_comment", ]  
model_Black <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = Non_Hispanic_Black, design = des_Black, family = "gaussian")
summary(model_Black)
confint(model_Black)["perfluorooctane_sulfonic_acid_comment", ]  
model_Other <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = Other_Race, design = des_Other, family = "gaussian")
summary(model_Other)
confint(model_Other)["perfluorooctane_sulfonic_acid_comment", ]  

```
##for subgroup analysis by BMI
```{r echo=FALSE,message=FALSE,warning=TRUE}
Fulldat_Pheno$BMI[is.na(Fulldat_Pheno$BMI)] <- 0
BMI_1 <- Fulldat_Pheno[Fulldat_Pheno$BMI < 25, ]
BMI_2 <- Fulldat_Pheno[Fulldat_Pheno$BMI >= 25 & Fulldat_Pheno$BMI < 30, ]
BMI_3 <- Fulldat_Pheno[Fulldat_Pheno$BMI >= 30, ]

Fulldat_Pheno <- Fulldat_Pheno %>% mutate(BMI_cat = case_when(
  BMI >= 30 ~ "obesity",
  BMI <= 25 ~ "normal",
  TRUE ~ "overweight"
) )

desBMI_1 <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = BMI_1) #select BMI < 25
desBMI_2 <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = BMI_2) #select 25 =< BMI <30
desBMI_3 <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = BMI_3) #select >= 30

#Perfluorohexane_sulfonic_acid_comment
model_BMI <- glm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Perfluorohexane_sulfonic_acid_comment*BMI_cat, data = Fulldat_Pheno)
summary(model_BMI)
model_BMI_1 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = BMI_1, design = desBMI_1, family = "gaussian")
summary(model_BMI_1)
confint(model_BMI_1)["Perfluorohexane_sulfonic_acid_comment", ]  
model_BMI_2 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = BMI_2, design = desBMI_2, family = "gaussian")
summary(model_BMI_2)
confint(model_BMI_2)["Perfluorohexane_sulfonic_acid_comment", ]  
model_BMI_3 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = BMI_3, design = desBMI_3, family = "gaussian")
summary(model_BMI_3)
confint(model_BMI_3)["Perfluorohexane_sulfonic_acid_comment", ]  

#Perfluorononanoic_acid_comment
model_BMI <- glm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Perfluorononanoic_acid_comment*BMI_cat, data = Fulldat_Pheno)
summary(model_BMI)
model_BMI_1 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = BMI_1, design = desBMI_1, family = "gaussian")
summary(model_BMI_1)
confint(model_BMI_1)["Perfluorononanoic_acid_comment", ]  
model_BMI_2 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = BMI_2, design = desBMI_2, family = "gaussian")
summary(model_BMI_2)
confint(model_BMI_2)["Perfluorononanoic_acid_comment", ]  
model_BMI_3 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = BMI_3, design = desBMI_3, family = "gaussian")
summary(model_BMI_3)
confint(model_BMI_3)["Perfluorononanoic_acid_comment", ] 

#perfluorooctanoic_acid_comment
model_BMI <- glm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + perfluorooctanoic_acid_comment*BMI_cat, data = Fulldat_Pheno)
summary(model_BMI)
model_BMI_1 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = BMI_1, design = desBMI_1, family = "gaussian")
summary(model_BMI_1)
confint(model_BMI_1)["perfluorooctanoic_acid_comment", ]  
model_BMI_2 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = BMI_2, design = desBMI_2, family = "gaussian")
summary(model_BMI_2)
confint(model_BMI_2)["perfluorooctanoic_acid_comment", ]  
model_BMI_3 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = BMI_3, design = desBMI_3, family = "gaussian")
summary(model_BMI_3)
confint(model_BMI_3)["perfluorooctanoic_acid_comment", ] 

#perfluorooctane_sulfonic_acid_comment
model_BMI <- glm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + perfluorooctane_sulfonic_acid_comment*BMI_cat, data = Fulldat_Pheno)
summary(model_BMI)
model_BMI_1 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = BMI_1, design = desBMI_1, family = "gaussian")
summary(model_BMI_1)
confint(model_BMI_1)["perfluorooctane_sulfonic_acid_comment", ]  
model_BMI_2 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = BMI_2, design = desBMI_2, family = "gaussian")
summary(model_BMI_2)
confint(model_BMI_2)["perfluorooctane_sulfonic_acid_comment", ]  
model_BMI_3 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = BMI_3, design = desBMI_3, family = "gaussian")
summary(model_BMI_3)
confint(model_BMI_3)["perfluorooctane_sulfonic_acid_comment", ] 

```
#for subgroup analysis by income
```{r echo=FALSE,message=FALSE,warning=TRUE}
Fulldat_Pheno$Ratio_income_poverty[is.na(Fulldat_Pheno$Ratio_income_poverty)] <- 0
income_1 <- Fulldat_Pheno[Fulldat_Pheno$Ratio_income_poverty <= 1, ]
income_2 <- Fulldat_Pheno[Fulldat_Pheno$Ratio_income_poverty > 1 & Fulldat_Pheno$Ratio_income_poverty < 4, ]
income_3 <- Fulldat_Pheno[Fulldat_Pheno$Ratio_income_poverty >= 4, ]


Fulldat_Pheno <- Fulldat_Pheno %>% mutate(income_cat = case_when(
  Ratio_income_poverty >= 4 ~ "rich",
  Ratio_income_poverty <= 1 ~ "poor",
  TRUE ~ "average"
) )

desincome_1 <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = income_1) #select income <= 1
desincome_2 <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = income_2) #select 1 < income < 4
desincome_3 <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = income_3) #select >= 4

#Perfluorohexane_sulfonic_acid_comment
model_income <- glm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Perfluorohexane_sulfonic_acid_comment*as.factor(income_cat), data = Fulldat_Pheno)
summary(model_income)
model_income_1 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = income_1, design = desincome_1, family = "gaussian")
summary(model_income_1)
confint(model_income_1)["Perfluorohexane_sulfonic_acid_comment", ]
model_income_2 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = income_2, design = desincome_2, family = "gaussian")
summary(model_income_2)
confint(model_income_2)["Perfluorohexane_sulfonic_acid_comment", ]
model_income_3 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = income_3, design = desincome_3, family = "gaussian")
summary(model_income_3)
confint(model_income_3)["Perfluorohexane_sulfonic_acid_comment", ]

#Perfluorononanoic_acid_comment
model_income <- glm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Perfluorononanoic_acid_comment*as.factor(income_cat), data = Fulldat_Pheno)
summary(model_income)
model_income_1 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = income_1, design = desincome_1, family = "gaussian")
summary(model_income_1)
confint(model_income_1)["Perfluorononanoic_acid_comment", ]
model_income_2 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = income_2, design = desincome_2, family = "gaussian")
summary(model_income_2)
confint(model_income_2)["Perfluorononanoic_acid_comment", ]
model_income_3 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = income_3, design = desincome_3, family = "gaussian")
summary(model_income_3)
confint(model_income_3)["Perfluorononanoic_acid_comment", ]

#perfluorooctanoic_acid_comment
model_income <- glm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + perfluorooctanoic_acid_comment*as.factor(income_cat), data = Fulldat_Pheno)
summary(model_income)
model_income_1 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = income_1, design = desincome_1, family = "gaussian")
summary(model_income_1)
confint(model_income_1)["perfluorooctanoic_acid_comment", ]
model_income_2 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = income_2, design = desincome_2, family = "gaussian")
summary(model_income_2)
confint(model_income_2)["perfluorooctanoic_acid_comment", ]
model_income_3 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = income_3, design = desincome_3, family = "gaussian")
summary(model_income_3)
confint(model_income_3)["perfluorooctanoic_acid_comment", ]

#perfluorooctane_sulfonic_acid_comment
model_income <- glm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + perfluorooctane_sulfonic_acid_comment*as.factor(income_cat), data = Fulldat_Pheno)
summary(model_income)
model_income_1 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = income_1, design = desincome_1, family = "gaussian")
summary(model_income_1)
confint(model_income_1)["perfluorooctane_sulfonic_acid_comment", ]
model_income_2 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = income_2, design = desincome_2, family = "gaussian")
summary(model_income_2)
confint(model_income_2)["perfluorooctane_sulfonic_acid_comment", ]
model_income_3 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = income_3, design = desincome_3, family = "gaussian")
summary(model_income_3)
confint(model_income_3)["perfluorooctane_sulfonic_acid_comment", ]

```
#for subgroup analysis by cancer
```{r echo=FALSE,message=FALSE,warning=TRUE}
cancer <- Fulldat_Pheno[Fulldat_Pheno$had_cancer == 1, ]
cancer$had_cancer[is.na(cancer$had_cancer)] <- 0
cancer <- cancer[cancer$had_cancer != 0, , drop = FALSE]

non_cancer <- Fulldat_Pheno[Fulldat_Pheno$had_cancer == 2, ]
non_cancer$had_cancer[is.na(non_cancer$had_cancer)] <- 0
non_cancer <- non_cancer[non_cancer$had_cancer != 0, , drop = FALSE]

des_cancer <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = cancer) 
des_non_cancer <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = non_cancer) 

#Perfluorohexane_sulfonic_acid_comment
model_cancer <- glm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Perfluorohexane_sulfonic_acid_comment*had_cancer, data = Fulldat_Pheno)
summary(model_cancer)
model_cancer <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = cancer, design = des_cancer, family = "gaussian")
summary(model_cancer)
confint(model_cancer)["Perfluorohexane_sulfonic_acid_comment", ]
model_non_cancer <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_non_cancer)
confint(model_non_cancer)["Perfluorohexane_sulfonic_acid_comment", ]

#Perfluorononanoic_acid_comment
model_cancer <- glm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Perfluorononanoic_acid_comment*had_cancer, data = Fulldat_Pheno)
summary(model_cancer)
model_cancer <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = cancer, design = des_cancer, family = "gaussian")
summary(model_cancer)
confint(model_cancer)["Perfluorononanoic_acid_comment", ]
model_non_cancer <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_non_cancer)
confint(model_non_cancer)["Perfluorononanoic_acid_comment", ]

#perfluorooctanoic_acid_comment
model_cancer <- glm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + perfluorooctanoic_acid_comment*had_cancer, data = Fulldat_Pheno)
summary(model_cancer)
model_cancer <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = cancer, design = des_cancer, family = "gaussian")
summary(model_cancer)
confint(model_cancer)["perfluorooctanoic_acid_comment", ]
model_non_cancer <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_non_cancer)
confint(model_non_cancer)["perfluorooctanoic_acid_comment", ]

#perfluorooctane_sulfonic_acid_comment
model_cancer <- glm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + perfluorooctane_sulfonic_acid_comment*had_cancer, data = Fulldat_Pheno)
summary(model_cancer)
model_cancer <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = cancer, design = des_cancer, family = "gaussian")
summary(model_cancer)
confint(model_cancer)["perfluorooctane_sulfonic_acid_comment", ]
model_non_cancer <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_non_cancer)
confint(model_non_cancer)["perfluorooctane_sulfonic_acid_comment", ]
```

#for subgroup analysis by smoking
```{r echo=FALSE,message=FALSE,warning=TRUE}
Fulldat_Pheno$quit_smoking[is.na(Fulldat_Pheno$quit_smoking)] <- 0
Fulldat_Pheno$now_smoke[is.na(Fulldat_Pheno$now_smoke)] <- 0
current_smokers <- Fulldat_Pheno[Fulldat_Pheno$Smoked_days == 1 & Fulldat_Pheno$now_smoke == 1, ]
former_smokers <- Fulldat_Pheno[Fulldat_Pheno$Smoked_days == 1 & Fulldat_Pheno$quit_smoking > 1, ]
former_smokers$psu[is.na(former_smokers$psu)] <- 0
former_smokers <- former_smokers[former_smokers$psu != 0, , drop = FALSE]

non_smokers <- Fulldat_Pheno[Fulldat_Pheno$Smoked_days == 2, ]
non_smokers$Smoked_days[is.na(non_smokers$Smoked_days)] <- 0
non_smokers <- non_smokers[non_smokers$Smoked_days != 0, , drop = FALSE]

#select hose who were considered current smokers smoked on a regular basis and had smoked at least 100 cigarettes in their lifetime.
descurrent_smokers <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = current_smokers)
#select Former smokers had smoked at least 100 cigarettes and had since quit.
desformer_smokers <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = former_smokers) 
#select Non-smokers had either never smoked or smoked fewer than 100 cigarettes
desnon_smokers <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = non_smokers) 


#Perfluorohexane_sulfonic_acid_comment
model_smoke <- glm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment + Perfluorohexane_sulfonic_acid_comment*now_smoke, data = Fulldat_Pheno)
summary(model_smoke)
model_current_smokers <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = current_smokers, design = descurrent_smokers, family = "gaussian")
summary(model_current_smokers)
confint(model_current_smokers)["Perfluorohexane_sulfonic_acid_comment", ]
model_former_smokers <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = former_smokers, design = desformer_smokers, family = "gaussian")
summary(model_former_smokers)
confint(model_former_smokers)["Perfluorohexane_sulfonic_acid_comment", ]
model_non_smokers <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = non_smokers, design = desnon_smokers, family = "gaussian")
summary(model_non_smokers)
confint(model_non_smokers)["Perfluorohexane_sulfonic_acid_comment", ]

#Perfluorononanoic_acid_comment
model_smoke <- glm(Phenotypic_Age ~ Perfluorononanoic_acid_comment + Perfluorononanoic_acid_comment*now_smoke, data = Fulldat_Pheno)
summary(model_smoke)
model_current_smokers <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = current_smokers, design = descurrent_smokers, family = "gaussian")
summary(model_current_smokers)
confint(model_current_smokers)["Perfluorononanoic_acid_comment", ]
model_former_smokers <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = former_smokers, design = desformer_smokers, family = "gaussian")
summary(model_former_smokers)
confint(model_former_smokers)["Perfluorononanoic_acid_comment", ]
model_non_smokers <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = non_smokers, design = desnon_smokers, family = "gaussian")
summary(model_non_smokers)
confint(model_non_smokers)["Perfluorononanoic_acid_comment", ]

#perfluorooctanoic_acid_comment
model_smoke <- glm(Phenotypic_Age ~ perfluorooctanoic_acid_comment + perfluorooctanoic_acid_comment*now_smoke, data = Fulldat_Pheno)
summary(model_smoke)
model_current_smokers <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = current_smokers, design = descurrent_smokers, family = "gaussian")
summary(model_current_smokers)
confint(model_current_smokers)["perfluorooctanoic_acid_comment", ]
model_former_smokers <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = former_smokers, design = desformer_smokers, family = "gaussian")
summary(model_former_smokers)
confint(model_former_smokers)["perfluorooctanoic_acid_comment", ]
model_non_smokers <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = non_smokers, design = desnon_smokers, family = "gaussian")
summary(model_non_smokers)
confint(model_non_smokers)["perfluorooctanoic_acid_comment", ]

#perfluorooctane_sulfonic_acid_comment
model_smoke <- glm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment + perfluorooctane_sulfonic_acid_comment*now_smoke, data = Fulldat_Pheno)
summary(model_smoke)
model_current_smokers <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = current_smokers, design = descurrent_smokers, family = "gaussian")
summary(model_current_smokers)
confint(model_current_smokers)["perfluorooctane_sulfonic_acid_comment", ]
model_former_smokers <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = former_smokers, design = desformer_smokers, family = "gaussian")
summary(model_former_smokers)
confint(model_former_smokers)["perfluorooctane_sulfonic_acid_comment", ]
model_non_smokers <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = non_smokers, design = desnon_smokers, family = "gaussian")
summary(model_non_smokers)
confint(model_non_smokers)["perfluorooctane_sulfonic_acid_comment", ]

```






#sensitivity analysis without cancer patient only for pfas
```{r}
#subset cancer
cancer <- Fulldat_Pheno[Fulldat_Pheno$had_cancer == 1, ]
cancer$had_cancer[is.na(cancer$had_cancer)] <- 0
cancer <- cancer[cancer$had_cancer != 0, , drop = FALSE]

non_cancer <- Fulldat_Pheno[Fulldat_Pheno$had_cancer == 2, ]
non_cancer$had_cancer[is.na(non_cancer$had_cancer)] <- 0
non_cancer <- non_cancer[non_cancer$had_cancer != 0, , drop = FALSE]

des_cancer <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = cancer) 
des_non_cancer <- svydesign(id =~ psu, strata =~ Strata, weights =~ weight_2, nest = TRUE, data = non_cancer) 
```

#sensitivity Perfluorohexane_sulfonic_acid
```{r echo=FALSE,message=FALSE,warning=TRUE}
#binary Perfluorohexane_sulfonic_acid
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid_comment, data = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous Perfluorohexane_sulfonic_acid
model_X1 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ Perfluorohexane_sulfonic_acid + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ 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 = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X3)
confint(model_X3)
```

#sensitivity "Perfluorononanoic_acid"  "Perfluorononanoic_acid_comment"  
```{r echo=FALSE,message=FALSE,warning=TRUE}
#binary
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid_comment, data = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous
model_X1 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid+ Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X3)
confint(model_X3)
```


#sensitivity "perfluorooctanoic_acid"  "perfluorooctanoic_acid_comment"    
```{r echo=FALSE,message=FALSE,warning=TRUE}
#binary
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ perfluorooctanoic_acid_comment, data = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous
model_X1 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ Perfluorononanoic_acid+ Gender + Race + Marital_Status + Ratio_income_poverty 
                  + BMI + sleep_disorders + Smoked_days + now_smoke + quit_smoking + Avg_alcohol_drinks + had_cancer, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X3)
confint(model_X3)
```

#sensitivity "perfluorooctane_sulfonic_acid"     "perfluorooctane_sulfonic_acid_comment"
```{r echo=FALSE,message=FALSE,warning=TRUE}
#binary
#model_1 -- non-adjusted
model_1 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid_comment, data = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, 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 = non_cancer, design = des_non_cancer, family = "gaussian") 
summary(model_3)
confint(model_3)


#continuous
model_X1 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid, data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X1)
confint(model_X1)

model_X2 <- svyglm(Phenotypic_Age ~ perfluorooctane_sulfonic_acid + Gender + Race +  Marital_Status + Ratio_income_poverty
                   , data = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X2)
confint(model_X2)

model_X3 <- svyglm(Phenotypic_Age ~ 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 = non_cancer, design = des_non_cancer, family = "gaussian")
summary(model_X3)
confint(model_X3)
```



