LauraAnalisys

#install.packages("conflicted")
#install.packages("dplyr")

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gtsummary)
library(gt)
library(readxl)
library(conflicted)
library(dplyr)

setwd(dir = "/Users/lorandacalderonzamora/Downloads/")
data <- read_excel("ARTICULO-RAGE-DM2.xlsx")
knitr::opts_knit$set(root.dir = "/Users/lorandacalderonzamora/Downloads")
class(data)

## [1] "tbl_df"     "tbl"        "data.frame"

names(data)

##  [1] "IDGRUPOS"      "SEXO"          "EDAD"          "IMC"          
##  [5] "TFG"           "GLUCOSA"       "UREA"          "BUN"          
##  [9] "BUNCREA"       "CREATININA"    "COLESTEROL"    "HDL"          
## [13] "LDL"           "VLDL"          "TRIGLICERIDOS" "HbA1C"        
## [17] "FUMA"          "SXMET"         "RAGE624"       "RAGE625"      
## [21] "RAGE600"       "ALCOHOL"       "ACTFISICA"     "SISTOLE"      
## [25] "DIASTOLE"      "HTA"

data <- as.data.frame(data)

data <- data %>%
  mutate(
    Renal_Dysfunction = dplyr::recode(IDGRUPOS, `0` = "No Renal Dysfunction", `1` = "Renal Dysfunction"),
    Sex = dplyr::recode(SEXO, `0` = "Male", `1` = "Female"),
    Alcohol = dplyr::recode(ALCOHOL, `0` = "No", `1` = "Yes"),
    Physical_Activity = dplyr::recode(ACTFISICA, `0` = "No", `1` = "Yes"),
    Hypertension = dplyr::recode(HTA, `0` = "No", `1` = "Yes"),
    Metabolic_Syndrome = dplyr::recode(SXMET, `0` = "No", `1` = "Yes"),
    Smoking = dplyr::recode(FUMA, `0`= "No", `1` = "Yes"),
    Age = as.numeric(EDAD),  # Crear o convertir Age a numérico
    BMI = as.numeric(IMC)
  )

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `BMI = as.numeric(IMC)`.
## Caused by warning:
## ! NAs introduced by coercion

class(data)

## [1] "data.frame"

view(data)
str(data)

## 'data.frame':    435 obs. of  35 variables:
##  $ IDGRUPOS          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SEXO              : num  1 1 1 0 1 1 1 1 1 1 ...
##  $ EDAD              : num  65 74 67 66 66 64 62 65 60 67 ...
##  $ IMC               : chr  "40.6" "33.799999999999997" "22.5" "25.5" ...
##  $ TFG               : chr  "95.65" "92.35" "94.32" "98.34" ...
##  $ GLUCOSA           : chr  "94" "106" "168" "140" ...
##  $ UREA              : num  24.3 34.8 25.6 32.3 28.7 25.5 24.6 38.2 17.3 33.5 ...
##  $ BUN               : num  11.3 16.2 11.9 15.1 13.4 ...
##  $ BUNCREA           : num  18.9 23.2 19.9 21.5 22.3 ...
##  $ CREATININA        : num  0.6 0.7 0.6 0.7 0.6 0.5 0.5 0.7 0.68 0.69 ...
##  $ COLESTEROL        : num  210 222 123 99 145 ...
##  $ HDL               : num  47 53 26 29 44 39 50 43 26.4 43.9 ...
##  $ LDL               : num  131 142.4 79.2 52.6 71.6 ...
##  $ VLDL              : num  32 26.6 17.8 17.4 29.4 ...
##  $ TRIGLICERIDOS     : num  160 133 89 87 147 ...
##  $ HbA1C             : chr  "7.1" "6.8" "9.5" "7.4" ...
##  $ FUMA              : chr  "0" "0" "0" "0" ...
##  $ SXMET             : num  1 1 0 1 0 1 0 0 0 0 ...
##  $ RAGE624           : chr  "A/A" "A/T" "A/T" "A/A" ...
##  $ RAGE625           : chr  "A/A" "A/A" "A/A" "A/A" ...
##  $ RAGE600           : chr  "C/C" "C/C" "C/C" "C/C" ...
##  $ ALCOHOL           : num  0 0 0 1 0 1 0 0 1 0 ...
##  $ ACTFISICA         : chr  "0" "1" "0" "0" ...
##  $ SISTOLE           : chr  "142" "122" "149" "145" ...
##  $ DIASTOLE          : chr  "87" "68" "92" "83" ...
##  $ HTA               : chr  "1" "1" "1" "1" ...
##  $ Renal_Dysfunction : chr  "No Renal Dysfunction" "No Renal Dysfunction" "No Renal Dysfunction" "No Renal Dysfunction" ...
##  $ Sex               : chr  "Female" "Female" "Female" "Male" ...
##  $ Alcohol           : chr  "No" "No" "No" "Yes" ...
##  $ Physical_Activity : chr  "No" "Yes" "No" "No" ...
##  $ Hypertension      : chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Metabolic_Syndrome: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Smoking           : chr  "No" "No" "No" "No" ...
##  $ Age               : num  65 74 67 66 66 64 62 65 60 67 ...
##  $ BMI               : num  40.6 33.8 22.5 25.5 24.4 31 27.9 27.4 26.1 24.8 ...

data <- as.data.frame(data)

selected_data <- data %>%
  dplyr::select(
    Renal_Dysfunction, Sex, Age, BMI, Smoking, Alcohol, Physical_Activity, Hypertension, Metabolic_Syndrome
  )

summary_table <- selected_data %>%
  tbl_summary(
    by = Renal_Dysfunction,  
    missing = "no",  
    type = list(       
      Age ~ "continuous",        
      Sex ~ "categorical",       
      Smoking ~ "categorical",
      Alcohol ~ "categorical",
      Physical_Activity ~ "categorical",
      Hypertension ~ "categorical",
      Metabolic_Syndrome ~ "categorical",
      BMI ~ "continuous"
    ),
    statistic = list(   
      Age ~ "{median} ({p25} - {p75})",  
      BMI ~ "{median} ({p25} - {p75})", 
      all_categorical() ~ "{n} ({p}%)"  
    )
  ) %>%
  add_overall() %>%  
  add_p(  
    test = all_categorical() ~ "fisher.test",  
    test.args = all_categorical() ~ list(simulate.p.value = TRUE)  
  ) %>%
  modify_header(label = "**Variable**") %>%  
  modify_caption("**Table: Sociodemographic and lifestyle characteristics by Renal Dysfunction**") %>%
  as_gt() %>%  
  gt::tab_style(
    style = gt::cell_text(weight = "bold"),
    locations = gt::cells_column_labels()
  )

summary_table

**Table: Sociodemographic and lifestyle characteristics by Renal Dysfunction**
Variable	Overall N = 435¹	No Renal Dysfunction N = 217¹	Renal Dysfunction N = 218¹	p-value²
Sex				0.10
Female	257 (59%)	137 (63%)	120 (55%)
Male	178 (41%)	80 (37%)	98 (45%)
Age	61 (54 - 67)	57 (51 - 64)	64 (59 - 70)	<0.001
BMI	29.9 (26.7 - 34.1)	30.1 (27.1 - 34.4)	29.9 (26.4 - 33.1)	0.2
Smoking				0.6
NA	2 (0.5%)	0 (0%)	2 (0.9%)
No	359 (83%)	180 (83%)	179 (82%)
Yes	74 (17%)	37 (17%)	37 (17%)
Alcohol				0.002
No	306 (70%)	138 (64%)	168 (77%)
Yes	129 (30%)	79 (36%)	50 (23%)
Physical_Activity				0.003
NA	8 (1.8%)	1 (0.5%)	7 (3.2%)
No	239 (55%)	135 (62%)	104 (48%)
Yes	188 (43%)	81 (37%)	107 (49%)
Hypertension				<0.001
NA	4 (0.9%)	0 (0%)	4 (1.8%)
No	123 (28%)	77 (35%)	46 (21%)
Yes	308 (71%)	140 (65%)	168 (77%)
Metabolic_Syndrome				0.4
No	119 (27%)	55 (25%)	64 (29%)
Yes	316 (73%)	162 (75%)	154 (71%)
¹ n (%); Median (Q1 - Q3)
² Fisher’s exact test; Wilcoxon rank sum test; Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates)

# Seleccionar solo las variables deseadas y asegurarse de que son numéricas
selected_data <- data %>%
  mutate(
    TFG = as.numeric(TFG),
    UREA = as.numeric(UREA),
    CREATININA = as.numeric(CREATININA),
    BUN = as.numeric(BUN),
    BUNCREA = as.numeric(BUNCREA),
    GLUCOSA = as.numeric(GLUCOSA),
    HbA1C = as.numeric(HbA1C),
    TRIGLICERIDOS = as.numeric(TRIGLICERIDOS),
    COLESTEROL = as.numeric(COLESTEROL),
    HDL = as.numeric(HDL),
    LDL = as.numeric(LDL),
    VLDL = as.numeric(VLDL)
  ) %>%
  # Seleccionar solo las variables de interés
  select(TFG, UREA, CREATININA, BUN, BUNCREA, GLUCOSA, HbA1C, TRIGLICERIDOS, COLESTEROL, HDL, LDL, VLDL, Renal_Dysfunction)

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `TFG = as.numeric(TFG)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

# Convertir Renal_Dysfunction a factor si no lo es
selected_data$Renal_Dysfunction <- as.factor(selected_data$Renal_Dysfunction)

# Crear la tabla de resumen
summary_table <- selected_data %>%
  tbl_summary(
    by = Renal_Dysfunction,  # Agrupar por la variable 'Renal_Dysfunction'
    missing = "no", # Excluir valores faltantes
    type = all_continuous() ~ "continuous", # Especificar que todas las variables son continuas
    statistic = all_continuous() ~ "{mean} ({p25}, {p75})" # Mostrar la media y desviación estándar
  ) %>%
  add_overall() %>%  # Añadir una columna con el resumen general
  add_p() %>% # Añadir columna con valores p
  modify_header(label = "**Variable**") %>%
  as_gt() %>%
  gt::tab_header(
    title = gt::md("**Table: Clinical and Biochemical Parameters**")
  )

# Mostrar la tabla
summary_table

Variable	Overall N = 435¹	No Renal Dysfunction N = 217¹	Renal Dysfunction N = 218¹	p-value²
Table: Clinical and Biochemical Parameters
TFG	83 (65, 103)	110 (97, 113)	56 (38, 79)	<0.001
UREA	52 (30, 58)	36 (26, 43)	68 (36, 83)	<0.001
CREATININA	1.48 (0.60, 1.03)	0.59 (0.50, 0.70)	2.36 (0.86, 1.65)	<0.001
BUN	24 (14, 26)	17 (12, 20)	32 (17, 39)	<0.001
BUNCREA	27 (16, 29)	34 (20, 36)	19 (13, 24)	<0.001
GLUCOSA	178 (108, 225)	199 (121, 256)	158 (101, 188)	<0.001
HbA1C	7.73 (6.30, 8.90)	8.13 (6.70, 9.30)	7.33 (6.00, 8.47)	<0.001
TRIGLICERIDOS	160 (100, 191)	162 (106, 187)	157 (95, 197)	0.2
COLESTEROL	196 (148, 226)	203 (152, 223)	189 (146, 227)	0.4
HDL	44 (33, 53)	41 (30, 50)	47 (35, 56)	<0.001
LDL	121 (78, 148)	130 (87, 149)	112 (69, 146)	0.090
VLDL	32 (20, 38)	32 (21, 37)	32 (19, 41)	0.2
¹ Mean (Q1, Q3)
² Wilcoxon rank sum test

#install.packages(c("ggpubr", "cowplot"))

library(ggpubr)
library(cowplot)
library(ggplot2)

data <- data %>%
  mutate(
    TFG = as.numeric(TFG),
    UREA = as.numeric(UREA),
    CREATININA = as.numeric(CREATININA),
    BUN = as.numeric(BUN),
    BUNCREA = as.numeric(BUNCREA),
    GLUCOSA = as.numeric(GLUCOSA),
    HbA1C = as.numeric(HbA1C),
    TRIGLICERIDOS = as.numeric(TRIGLICERIDOS),
    COLESTEROL = as.numeric(COLESTEROL),
    HDL = as.numeric(HDL),
    LDL = as.numeric(LDL),
    VLDL = as.numeric(VLDL)
  )

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `TFG = as.numeric(TFG)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

 custom_colors <- c("No Renal Dysfunction" = "#1f77b4", "Renal Dysfunction" = "#ff7f0e")

# Crear un tema personalizado
theme_custom <- theme_pubr() + 
  theme(axis.title.x = element_blank(), 
        axis.text = element_text(size = 10), 
        legend.position = "none")

# Función para crear gráficos de boxplot para cada variable
create_boxplot <- function(data, variable, var_label, y_label, y_lim) {
  ggplot(data, aes(x = Renal_Dysfunction, y = !!sym(variable), fill = Renal_Dysfunction)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width = 0.2, alpha = 0.5, color = "black") +
    stat_compare_means(aes(label = ..p.signif..), method = "t.test", label.x = 1.5, label.y = y_lim - 0.1*y_lim) +
    labs(title = var_label, y = y_label, fill = " ") +
    scale_x_discrete(labels = c("No Renal Dysfunction", "Renal Dysfunction")) +
    scale_fill_manual(values = custom_colors) +
    ylim(0, y_lim) +  # Ajustar la escala del eje y según la variable
    theme_custom
}

# Crear gráficos para cada variable
p1 <- create_boxplot(data, "TFG", " ", "GFR (mL/min/1.73 m²)", 150)
p2 <- create_boxplot(data, "UREA", "", "Urea (mg/dL)", 100)
p3 <- create_boxplot(data, "CREATININA", "", "Creatinine (mg/dL)", 5)
p4 <- create_boxplot(data, "BUN", " ", "BUN (mg/dL)", 40)
p5 <- create_boxplot(data, "BUNCREA", " ", "BUN/Creatinine Ratio", 50)
p6 <- create_boxplot(data, "GLUCOSA", "", "Glucose (mg/dL)", 400)
p7 <- create_boxplot(data, "HbA1C", "", "HbA1c (%)", 20)
p8 <- create_boxplot(data, "TRIGLICERIDOS", "", "Triglycerides (mg/dL)", 500)
p9 <- create_boxplot(data, "COLESTEROL", "", "Cholesterol (mg/dL)", 400)
p10 <- create_boxplot(data, "HDL", "", "HDL-C (mg/dL)", 100)
p11 <- create_boxplot(data, "LDL", "", "LDL-C (mg/dL)", 300)
p12 <- create_boxplot(data, "VLDL", "", "VLDL-C (mg/dL)", 100)

# Mostrar un gráfico de ejemplo
print(p1)

## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 10 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p2)

## Warning: Removed 44 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 44 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 44 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p3)

## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 37 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 37 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p4)

## Warning: Removed 53 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 53 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 53 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p5)

## Warning: Removed 31 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 31 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 31 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p6)

## Warning: Removed 18 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 18 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 18 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p7)

## Warning: Removed 13 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 13 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 13 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p8)

## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p9)

## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p10)

## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p11)

## Warning: Removed 11 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 11 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 11 rows containing missing values or values outside the scale range
## (`geom_point()`).

print(p12)

## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_compare_means()`).

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

#install.packages("https://cran.r-project.org/bin/macosx/big-sur-arm64/contrib/4.3/rms_6.8-1.tgz", repos = NULL, type = "binary")
library(rms)

## Loading required package: Hmisc

## 
## Attaching package: 'Hmisc'

## The following object is masked from 'package:gt':
## 
##     html

## The following objects are masked from 'package:dplyr':
## 
##     src, summarize

## The following objects are masked from 'package:base':
## 
##     format.pval, units

library(SNPassoc)

## Registered S3 method overwritten by 'SNPassoc':
##   method            from       
##   summary.haplo.glm haplo.stats

str(data)

## 'data.frame':    435 obs. of  35 variables:
##  $ IDGRUPOS          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SEXO              : num  1 1 1 0 1 1 1 1 1 1 ...
##  $ EDAD              : num  65 74 67 66 66 64 62 65 60 67 ...
##  $ IMC               : chr  "40.6" "33.799999999999997" "22.5" "25.5" ...
##  $ TFG               : num  95.7 92.3 94.3 98.3 95 ...
##  $ GLUCOSA           : num  94 106 168 140 94 ...
##  $ UREA              : num  24.3 34.8 25.6 32.3 28.7 25.5 24.6 38.2 17.3 33.5 ...
##  $ BUN               : num  11.3 16.2 11.9 15.1 13.4 ...
##  $ BUNCREA           : num  18.9 23.2 19.9 21.5 22.3 ...
##  $ CREATININA        : num  0.6 0.7 0.6 0.7 0.6 0.5 0.5 0.7 0.68 0.69 ...
##  $ COLESTEROL        : num  210 222 123 99 145 ...
##  $ HDL               : num  47 53 26 29 44 39 50 43 26.4 43.9 ...
##  $ LDL               : num  131 142.4 79.2 52.6 71.6 ...
##  $ VLDL              : num  32 26.6 17.8 17.4 29.4 ...
##  $ TRIGLICERIDOS     : num  160 133 89 87 147 ...
##  $ HbA1C             : num  7.1 6.8 9.5 7.4 6.9 5.5 5.3 5.7 7.6 11.8 ...
##  $ FUMA              : chr  "0" "0" "0" "0" ...
##  $ SXMET             : num  1 1 0 1 0 1 0 0 0 0 ...
##  $ RAGE624           : chr  "A/A" "A/T" "A/T" "A/A" ...
##  $ RAGE625           : chr  "A/A" "A/A" "A/A" "A/A" ...
##  $ RAGE600           : chr  "C/C" "C/C" "C/C" "C/C" ...
##  $ ALCOHOL           : num  0 0 0 1 0 1 0 0 1 0 ...
##  $ ACTFISICA         : chr  "0" "1" "0" "0" ...
##  $ SISTOLE           : chr  "142" "122" "149" "145" ...
##  $ DIASTOLE          : chr  "87" "68" "92" "83" ...
##  $ HTA               : chr  "1" "1" "1" "1" ...
##  $ Renal_Dysfunction : chr  "No Renal Dysfunction" "No Renal Dysfunction" "No Renal Dysfunction" "No Renal Dysfunction" ...
##  $ Sex               : chr  "Female" "Female" "Female" "Male" ...
##  $ Alcohol           : chr  "No" "No" "No" "Yes" ...
##  $ Physical_Activity : chr  "No" "Yes" "No" "No" ...
##  $ Hypertension      : chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Metabolic_Syndrome: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Smoking           : chr  "No" "No" "No" "No" ...
##  $ Age               : num  65 74 67 66 66 64 62 65 60 67 ...
##  $ BMI               : num  40.6 33.8 22.5 25.5 24.4 31 27.9 27.4 26.1 24.8 ...

data$RAGE624 <- as.factor(data$RAGE624)
data$RAGE625 <- as.factor(data$RAGE625)
data$RAGE600 <- as.factor(data$RAGE600)
data$Renal_Dysfunction <- factor(data$Renal_Dysfunction)
data$Sex <- factor(data$Sex, levels = c("Female", "Male"))
data$Alcohol <- factor(data$Alcohol, levels = c("No", "Yes"))
data$Physical_Activity <- factor(data$Physical_Activity, levels = c("No", "Yes"))
data$Hypertension <- factor(data$Hypertension, levels = c("No", "Yes"))
data$Metabolic_Syndrome <- factor(data$Metabolic_Syndrome, levels = c("No", "Yes"))
data$Smoking <- factor(data$Smoking, levels = c("No", "Yes"))

snp_indices <- match(c("RAGE624", "RAGE625", "RAGE600"), colnames(data))
data <- data[complete.cases(data[, snp_indices]), ]
SNPs <- setupSNP(data, colSNPs = snp_indices, sep = "/")

plot(SNPs$RAGE624)

plot(SNPs$RAGE625)

plot(SNPs$RAGE600)

plot(SNPs$RAGE624, type = pie)

plot(SNPs$RAGE625, type = pie)

plot(SNPs$RAGE600, type = pie)

names(SNPs)

##  [1] "IDGRUPOS"           "SEXO"               "EDAD"              
##  [4] "IMC"                "TFG"                "GLUCOSA"           
##  [7] "UREA"               "BUN"                "BUNCREA"           
## [10] "CREATININA"         "COLESTEROL"         "HDL"               
## [13] "LDL"                "VLDL"               "TRIGLICERIDOS"     
## [16] "HbA1C"              "FUMA"               "SXMET"             
## [19] "ALCOHOL"            "ACTFISICA"          "SISTOLE"           
## [22] "DIASTOLE"           "HTA"                "Renal_Dysfunction" 
## [25] "Sex"                "Alcohol"            "Physical_Activity" 
## [28] "Hypertension"       "Metabolic_Syndrome" "Smoking"           
## [31] "Age"                "BMI"                "RAGE624"           
## [34] "RAGE625"            "RAGE600"

#Varaibles categoricas
data$RAGE624[data$RAGE624 == "NA"] <- NA
data$RAGE625[data$RAGE625 == "NA"] <- NA
data$RAGE600[data$RAGE600 == "NA"] <- NA
data$Renal_Dysfunction[data$Renal_Dysfunction == "NA"] <- NA
data$Smoking[data$Smoking == "NA"] <- NA
data$Alcohol[data$Alcohol == "NA"] <- NA

# Eliminar los niveles no utilizados (como "NA")
data$RAGE624 <- droplevels(data$RAGE624)
data$RAGE625 <- droplevels(data$RAGE625)
data$RAGE600 <- droplevels(data$RAGE600)
data$Renal_Dysfunction <- droplevels(data$Renal_Dysfunction)
data$Smoking <- droplevels(data$Smoking)
data$Alcohol <- droplevels(data$Alcohol)

# Verificar la estructura de los factores
str(data$RAGE624)

##  Factor w/ 3 levels "A/A","A/T","T/T": 1 2 2 1 3 2 1 2 2 1 ...

str(data$RAGE625)

##  Factor w/ 3 levels "A/A","A/G","G/G": 1 1 1 1 1 2 1 1 1 1 ...

str(data$RAGE600)

##  Factor w/ 3 levels "C/C","C/T","T/T": 1 1 1 1 1 1 1 1 1 1 ...

str(data$Renal_Dysfunction)

##  Factor w/ 2 levels "No Renal Dysfunction",..: 1 1 1 1 1 1 1 1 1 1 ...

str(data$Smoking)

##  Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...

str(data$Alcohol)

##  Factor w/ 2 levels "No","Yes": 1 1 1 2 1 2 1 1 2 1 ...

sum(is.na(data$RAGE624))

## [1] 13

sum(is.na(data$RAGE625))

## [1] 19

sum(is.na(data$RAGE600))

## [1] 25

sum(is.na(data$Renal_Dysfunction))

## [1] 0

sum(is.na(data$Smoking))

## [1] 2

sum(is.na(data$Alcohol))

## [1] 0

dim(data)

## [1] 435  35

str(data)

## 'data.frame':    435 obs. of  35 variables:
##  $ IDGRUPOS          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ SEXO              : num  1 1 1 0 1 1 1 1 1 1 ...
##  $ EDAD              : num  65 74 67 66 66 64 62 65 60 67 ...
##  $ IMC               : chr  "40.6" "33.799999999999997" "22.5" "25.5" ...
##  $ TFG               : num  95.7 92.3 94.3 98.3 95 ...
##  $ GLUCOSA           : num  94 106 168 140 94 ...
##  $ UREA              : num  24.3 34.8 25.6 32.3 28.7 25.5 24.6 38.2 17.3 33.5 ...
##  $ BUN               : num  11.3 16.2 11.9 15.1 13.4 ...
##  $ BUNCREA           : num  18.9 23.2 19.9 21.5 22.3 ...
##  $ CREATININA        : num  0.6 0.7 0.6 0.7 0.6 0.5 0.5 0.7 0.68 0.69 ...
##  $ COLESTEROL        : num  210 222 123 99 145 ...
##  $ HDL               : num  47 53 26 29 44 39 50 43 26.4 43.9 ...
##  $ LDL               : num  131 142.4 79.2 52.6 71.6 ...
##  $ VLDL              : num  32 26.6 17.8 17.4 29.4 ...
##  $ TRIGLICERIDOS     : num  160 133 89 87 147 ...
##  $ HbA1C             : num  7.1 6.8 9.5 7.4 6.9 5.5 5.3 5.7 7.6 11.8 ...
##  $ FUMA              : chr  "0" "0" "0" "0" ...
##  $ SXMET             : num  1 1 0 1 0 1 0 0 0 0 ...
##  $ RAGE624           : Factor w/ 3 levels "A/A","A/T","T/T": 1 2 2 1 3 2 1 2 2 1 ...
##  $ RAGE625           : Factor w/ 3 levels "A/A","A/G","G/G": 1 1 1 1 1 2 1 1 1 1 ...
##  $ RAGE600           : Factor w/ 3 levels "C/C","C/T","T/T": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ALCOHOL           : num  0 0 0 1 0 1 0 0 1 0 ...
##  $ ACTFISICA         : chr  "0" "1" "0" "0" ...
##  $ SISTOLE           : chr  "142" "122" "149" "145" ...
##  $ DIASTOLE          : chr  "87" "68" "92" "83" ...
##  $ HTA               : chr  "1" "1" "1" "1" ...
##  $ Renal_Dysfunction : Factor w/ 2 levels "No Renal Dysfunction",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Sex               : Factor w/ 2 levels "Female","Male": 1 1 1 2 1 1 1 1 1 1 ...
##  $ Alcohol           : Factor w/ 2 levels "No","Yes": 1 1 1 2 1 2 1 1 2 1 ...
##  $ Physical_Activity : Factor w/ 2 levels "No","Yes": 1 2 1 1 2 1 2 1 1 1 ...
##  $ Hypertension      : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 1 1 1 1 ...
##  $ Metabolic_Syndrome: Factor w/ 2 levels "No","Yes": 2 2 1 2 1 2 1 1 1 1 ...
##  $ Smoking           : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ Age               : num  65 74 67 66 66 64 62 65 60 67 ...
##  $ BMI               : num  40.6 33.8 22.5 25.5 24.4 31 27.9 27.4 26.1 24.8 ...

SNPs <- setupSNP(data, colSNPs = match(c("RAGE624", "RAGE625", "RAGE600"), colnames(data)), sep = "/")

#Perform association analysis for each SNP
association(Renal_Dysfunction ~ RAGE624, data = SNPs)

## 
## SNP: RAGE624  adjusted by: 
##              No Renal Dysfunction    % Renal Dysfunction    %   OR lower upper
## Codominant                                                                    
## A/A                            80 37.7                94 44.8 1.00            
## A/T                           115 54.2                97 46.2 0.72  0.48  1.07
## T/T                            17  8.0                19  9.0 0.95  0.46  1.95
## Dominant                                                                      
## A/A                            80 37.7                94 44.8 1.00            
## A/T-T/T                       132 62.3               116 55.2 0.75  0.51  1.10
## Recessive                                                                     
## A/A-A/T                       195 92.0               191 91.0 1.00            
## T/T                            17  8.0                19  9.0 1.14  0.58  2.26
## Overdominant                                                                  
## A/A-T/T                        97 45.8               113 53.8 1.00            
## A/T                           115 54.2                97 46.2 0.72  0.49  1.06
## log-Additive                                                                  
## 0,1,2                         212 50.2               210 49.8 0.86  0.63  1.16
##              p-value   AIC
## Codominant                
## A/A          0.25164 588.2
## A/T                       
## T/T                       
## Dominant                  
## A/A          0.14246 586.9
## A/T-T/T                   
## Recessive                 
## A/A-A/T      0.70518 588.9
## T/T                       
## Overdominant              
## A/A-T/T      0.09781 586.3
## A/T                       
## log-Additive              
## 0,1,2        0.32414 588.0

association(Renal_Dysfunction ~ RAGE625, data = SNPs)

## 
## SNP: RAGE625  adjusted by: 
##              No Renal Dysfunction    % Renal Dysfunction    %   OR lower upper
## Codominant                                                                    
## A/A                           134 64.4               142 68.3 1.00            
## A/G                            56 26.9                52 25.0 0.88  0.56  1.37
## G/G                            18  8.7                14  6.7 0.73  0.35  1.53
## Dominant                                                                      
## A/A                           134 64.4               142 68.3 1.00            
## A/G-G/G                        74 35.6                66 31.7 0.84  0.56  1.26
## Recessive                                                                     
## A/A-A/G                       190 91.3               194 93.3 1.00            
## G/G                            18  8.7                14  6.7 0.76  0.37  1.58
## Overdominant                                                                  
## A/A-G/G                       152 73.1               156 75.0 1.00            
## A/G                            56 26.9                52 25.0 0.90  0.58  1.40
## log-Additive                                                                  
## 0,1,2                         208 50.0               208 50.0 0.86  0.64  1.17
##              p-value   AIC
## Codominant                
## A/A           0.6436 581.8
## A/G                       
## G/G                       
## Dominant                  
## A/A           0.4064 580.0
## A/G-G/G                   
## Recessive                 
## A/A-A/G       0.4612 580.2
## G/G                       
## Overdominant              
## A/A-G/G       0.6546 580.5
## A/G                       
## log-Additive              
## 0,1,2         0.3497 579.8

association(Renal_Dysfunction ~ RAGE600, data = SNPs)

## 
## SNP: RAGE600  adjusted by: 
##              No Renal Dysfunction    % Renal Dysfunction    %   OR lower upper
## Codominant                                                                    
## C/C                           174 84.5               183 89.7 1.00            
## C/T                            23 11.2                16  7.8 0.66  0.34  1.29
## T/T                             9  4.4                 5  2.5 0.53  0.17  1.61
## Dominant                                                                      
## C/C                           174 84.5               183 89.7 1.00            
## C/T-T/T                        32 15.5                21 10.3 0.62  0.35  1.12
## Recessive                                                                     
## C/C-C/T                       197 95.6               199 97.5 1.00            
## T/T                             9  4.4                 5  2.5 0.55  0.18  1.67
## Overdominant                                                                  
## C/C-T/T                       183 88.8               188 92.2 1.00            
## C/T                            23 11.2                16  7.8 0.68  0.35  1.32
## log-Additive                                                                  
## 0,1,2                         206 50.2               204 49.8 0.70  0.45  1.09
##              p-value   AIC
## Codominant                
## C/C           0.2672 571.7
## C/T                       
## T/T                       
## Dominant                  
## C/C           0.1126 569.9
## C/T-T/T                   
## Recessive                 
## C/C-C/T       0.2816 571.2
## T/T                       
## Overdominant              
## C/C-T/T       0.2504 571.1
## C/T                       
## log-Additive              
## 0,1,2         0.1074 569.8

# Reemplazar "NA" por NA en las variables numéricas
data$IMC[data$IMC == "NA"] <- NA
data$GLUCOSA[data$GLUCOSA == "NA"] <- NA
data$HbA1C[data$HbA1C == "NA"] <- NA

# Convertir columnas a numéricas
data$IMC <- as.numeric(as.character(data$IMC))
data$GLUCOSA <- as.numeric(as.character(data$GLUCOSA))
data$HbA1C <- as.numeric(as.character(data$HbA1C))

# Verificar qué filas tienen valores no numéricos en cada columna
str(data$IMC)

##  num [1:435] 40.6 33.8 22.5 25.5 24.4 31 27.9 27.4 26.1 24.8 ...

str(data$GLUCOSA)

##  num [1:435] 94 106 168 140 94 ...

str(data$HbA1C)

##  num [1:435] 7.1 6.8 9.5 7.4 6.9 5.5 5.3 5.7 7.6 11.8 ...

# Verificar cuántos NA hay en cada columna
sum(is.na(data$IMC))

## [1] 7

sum(is.na(data$GLUCOSA))

## [1] 1

sum(is.na(data$HbA1C))

## [1] 13

#install.packages("car")
library(car)

## Loading required package: carData

# Identificar las variables colineales
alias(lm(IMC ~ RAGE624 + RAGE625 + RAGE600 + GLUCOSA + HbA1C + 
         Sex + EDAD + UREA + BUN + BUNCREA + CREATININA + COLESTEROL + HDL + LDL + VLDL + 
         TRIGLICERIDOS + Alcohol + Physical_Activity + Hypertension + 
         Metabolic_Syndrome + Smoking, data = data))

## Model :
## IMC ~ RAGE624 + RAGE625 + RAGE600 + GLUCOSA + HbA1C + Sex + EDAD + 
##     UREA + BUN + BUNCREA + CREATININA + COLESTEROL + HDL + LDL + 
##     VLDL + TRIGLICERIDOS + Alcohol + Physical_Activity + Hypertension + 
##     Metabolic_Syndrome + Smoking

# Ajustar el modelo sin las variables colineales (ej. eliminando VLDL)
#Si el resultado de VIF extremadamente alto (mayores de 10) se deben eliminar del modelo
modelo_vif <- lm(IMC ~ RAGE624 + RAGE625 + RAGE600 + GLUCOSA + HbA1C + 
                 Sex + EDAD + UREA + BUN + BUNCREA + CREATININA + COLESTEROL + HDL + LDL + 
                 TRIGLICERIDOS + Alcohol + Physical_Activity + Hypertension + 
                 Metabolic_Syndrome + Smoking, data = data)
car::vif(modelo_vif)

##                          GVIF Df GVIF^(1/(2*Df))
## RAGE624              1.143950  2        1.034193
## RAGE625              1.261052  2        1.059701
## RAGE600              1.244668  2        1.056242
## GLUCOSA              1.805214  1        1.343583
## HbA1C                1.699503  1        1.303650
## Sex                  1.243185  1        1.114982
## EDAD                 1.313350  1        1.146015
## UREA               889.056250  1       29.817046
## BUN                904.666055  1       30.077667
## BUNCREA              1.553039  1        1.246210
## CREATININA           4.299430  1        2.073507
## COLESTEROL         302.770679  1       17.400307
## HDL                 11.782347  1        3.432542
## LDL                292.039712  1       17.089169
## TRIGLICERIDOS       14.923733  1        3.863125
## Alcohol              1.104113  1        1.050768
## Physical_Activity    1.102385  1        1.049945
## Hypertension         1.408572  1        1.186833
## Metabolic_Syndrome   1.440063  1        1.200026
## Smoking              1.106460  1        1.051884

# Modelo nulo 
modelo_nulo <- glm(Renal_Dysfunction ~ 1, data = data, family = binomial, na.action = na.exclude)

# Modelo completo 
modelo_completo <- glm(Renal_Dysfunction ~ RAGE624 + RAGE625 + RAGE600 + GLUCOSA + HbA1C + 
                       Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
                       Hypertension + Metabolic_Syndrome + Smoking,
                       data = data, family = binomial, na.action = na.exclude)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

OR <- exp(coef(modelo_completo))
CI <- exp(confint(modelo_completo))

## Waiting for profiling to be done...

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p_values <- summary(modelo_completo)$coefficients[, 4]
OR_completo <- cbind(OR, CI, p_values)
print(OR_completo)

##                                 OR        2.5 %       97.5 %     p_values
## (Intercept)           2.972046e-26 9.783181e-39 6.843469e-18 9.906762e-07
## RAGE624A/T            2.859967e-01 4.856785e-02 1.318830e+00 1.276636e-01
## RAGE624T/T            3.129414e+00 9.899068e-02 6.737206e+01 4.784303e-01
## RAGE625A/G            1.723430e+00 2.653582e-01 1.182592e+01 5.655929e-01
## RAGE625G/G            2.849784e-01 8.791180e-03 1.090587e+01 4.848850e-01
## RAGE600C/T            3.454271e+00 4.760231e-01 2.952137e+01 2.278095e-01
## RAGE600T/T            2.421563e-01 6.114366e-06 4.133697e+03 9.523641e-01
## GLUCOSA               1.004487e+00 9.937222e-01 1.016056e+00 4.147866e-01
## HbA1C                 7.866421e-01 4.299093e-01 1.319531e+00 3.901660e-01
## SexMale               3.966918e-04 6.291856e-06 7.657044e-03 9.864037e-06
## EDAD                  1.456307e+00 1.277406e+00 1.759164e+00 2.681540e-06
## CREATININA            3.863772e+21 5.049601e+14 8.210687e+31 4.918100e-07
## HDL                   1.020537e+00 9.627918e-01 1.085281e+00 4.985750e-01
## AlcoholYes            3.894235e+00 7.285602e-01 2.551642e+01 1.249508e-01
## Physical_ActivityYes  1.044713e+00 2.010186e-01 5.057610e+00 9.565082e-01
## HypertensionYes       4.706162e-01 5.853386e-02 3.402803e+00 4.574523e-01
## Metabolic_SyndromeYes 2.112725e+00 2.399846e-01 2.140624e+01 5.042935e-01
## SmokingYes            8.495121e-01 1.116317e-01 6.420907e+00 8.721074e-01

summary(modelo_completo)

## 
## Call:
## glm(formula = Renal_Dysfunction ~ RAGE624 + RAGE625 + RAGE600 + 
##     GLUCOSA + HbA1C + Sex + EDAD + CREATININA + HDL + Alcohol + 
##     Physical_Activity + Hypertension + Metabolic_Syndrome + Smoking, 
##     family = binomial, data = data, na.action = na.exclude)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)           -58.777962  12.011481  -4.893 9.91e-07 ***
## RAGE624A/T             -1.251775   0.821709  -1.523    0.128    
## RAGE624T/T              1.140846   1.609479   0.709    0.478    
## RAGE625A/G              0.544317   0.947373   0.575    0.566    
## RAGE625G/G             -1.255342   1.797281  -0.698    0.485    
## RAGE600C/T              1.239611   1.027852   1.206    0.228    
## RAGE600T/T             -1.418172  23.739770  -0.060    0.952    
## GLUCOSA                 0.004477   0.005489   0.815    0.415    
## HbA1C                  -0.239982   0.279271  -0.859    0.390    
## SexMale                -7.832351   1.771972  -4.420 9.86e-06 ***
## EDAD                    0.375904   0.080085   4.694 2.68e-06 ***
## CREATININA             49.705931   9.882915   5.029 4.92e-07 ***
## HDL                     0.020329   0.030040   0.677    0.499    
## AlcoholYes              1.359497   0.886058   1.534    0.125    
## Physical_ActivityYes    0.043742   0.802083   0.055    0.957    
## HypertensionYes        -0.753712   1.014351  -0.743    0.457    
## Metabolic_SyndromeYes   0.747979   1.120149   0.668    0.504    
## SmokingYes             -0.163093   1.013112  -0.161    0.872    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 515.314  on 371  degrees of freedom
## Residual deviance:  58.573  on 354  degrees of freedom
##   (63 observations deleted due to missingness)
## AIC: 94.573
## 
## Number of Fisher Scoring iterations: 13

#Se reliazo el analisis por clasificaciones de herencia (recesiva, dominante y aditiva) para el RAGE600 porque fue el que salio significativo
data$RAGE600_recesivo <- ifelse(data$RAGE600 == "T/T", 1, 0)

modelo_recesivo_RAGE600 <- glm(Renal_Dysfunction ~ RAGE600_recesivo + GLUCOSA + HbA1C + 
                               Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
                               Hypertension + Metabolic_Syndrome + Smoking,
                               data = data, family = binomial, na.action = na.exclude)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

summary(modelo_recesivo_RAGE600)

## 
## Call:
## glm(formula = Renal_Dysfunction ~ RAGE600_recesivo + GLUCOSA + 
##     HbA1C + Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
##     Hypertension + Metabolic_Syndrome + Smoking, family = binomial, 
##     data = data, na.action = na.exclude)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)           -56.098169  10.320765  -5.435 5.47e-08 ***
## RAGE600_recesivo        0.947286   6.085391   0.156   0.8763    
## GLUCOSA                 0.003675   0.004872   0.754   0.4507    
## HbA1C                  -0.150671   0.227105  -0.663   0.5070    
## SexMale                -7.347348   1.641857  -4.475 7.64e-06 ***
## EDAD                    0.358672   0.068469   5.239 1.62e-07 ***
## CREATININA             46.161902   8.439030   5.470 4.50e-08 ***
## HDL                     0.019306   0.024350   0.793   0.4279    
## AlcoholYes              1.600715   0.827693   1.934   0.0531 .  
## Physical_ActivityYes    0.240050   0.721354   0.333   0.7393    
## HypertensionYes        -0.306667   0.925772  -0.331   0.7405    
## Metabolic_SyndromeYes   0.396152   0.894111   0.443   0.6577    
## SmokingYes             -0.306855   0.990452  -0.310   0.7567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 535.016  on 385  degrees of freedom
## Residual deviance:  64.856  on 373  degrees of freedom
##   (49 observations deleted due to missingness)
## AIC: 90.856
## 
## Number of Fisher Scoring iterations: 12

# Modelo dominante para RAGE600: agrupar C/C vs C/T y T/T
data$RAGE600_dominante <- ifelse(data$RAGE600 == "C/C", 0, 1)

modelo_dominante_RAGE600 <- glm(Renal_Dysfunction ~ RAGE600_dominante + GLUCOSA + HbA1C + 
                                Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
                                Hypertension + Metabolic_Syndrome + Smoking,
                                data = data, family = binomial, na.action = na.exclude)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

summary(modelo_dominante_RAGE600)

## 
## Call:
## glm(formula = Renal_Dysfunction ~ RAGE600_dominante + GLUCOSA + 
##     HbA1C + Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
##     Hypertension + Metabolic_Syndrome + Smoking, family = binomial, 
##     data = data, na.action = na.exclude)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)           -56.286640  10.420771  -5.401 6.61e-08 ***
## RAGE600_dominante       1.231020   1.004151   1.226   0.2202    
## GLUCOSA                 0.003702   0.004920   0.752   0.4518    
## HbA1C                  -0.129895   0.230476  -0.564   0.5730    
## SexMale                -7.472919   1.667263  -4.482 7.39e-06 ***
## EDAD                    0.353649   0.067788   5.217 1.82e-07 ***
## CREATININA             46.605314   8.597387   5.421 5.93e-08 ***
## HDL                     0.018900   0.024599   0.768   0.4423    
## AlcoholYes              1.566438   0.844928   1.854   0.0637 .  
## Physical_ActivityYes   -0.026516   0.784222  -0.034   0.9730    
## HypertensionYes        -0.488811   0.939500  -0.520   0.6029    
## Metabolic_SyndromeYes   0.585157   0.932735   0.627   0.5304    
## SmokingYes             -0.344431   1.016006  -0.339   0.7346    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 535.016  on 385  degrees of freedom
## Residual deviance:  63.283  on 373  degrees of freedom
##   (49 observations deleted due to missingness)
## AIC: 89.283
## 
## Number of Fisher Scoring iterations: 12

data$RAGE600_aditivo <- factor(data$RAGE600, levels = c("C/C", "C/T", "T/T"))

modelo_aditivo_RAGE600 <- glm(Renal_Dysfunction ~ RAGE600_aditivo + GLUCOSA + HbA1C + 
                              Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
                              Hypertension + Metabolic_Syndrome + Smoking,
                              data = data, family = binomial, na.action = na.exclude)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

summary(modelo_aditivo_RAGE600)

## 
## Call:
## glm(formula = Renal_Dysfunction ~ RAGE600_aditivo + GLUCOSA + 
##     HbA1C + Sex + EDAD + CREATININA + HDL + Alcohol + Physical_Activity + 
##     Hypertension + Metabolic_Syndrome + Smoking, family = binomial, 
##     data = data, na.action = na.exclude)
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)           -56.287835  10.420148  -5.402 6.60e-08 ***
## RAGE600_aditivoC/T      1.242163   1.022184   1.215   0.2243    
## RAGE600_aditivoT/T      0.884744   5.855126   0.151   0.8799    
## GLUCOSA                 0.003697   0.004920   0.752   0.4523    
## HbA1C                  -0.128788   0.231497  -0.556   0.5780    
## SexMale                -7.479302   1.669408  -4.480 7.46e-06 ***
## EDAD                    0.353463   0.067846   5.210 1.89e-07 ***
## CREATININA             46.618270   8.596997   5.423 5.87e-08 ***
## HDL                     0.018878   0.024601   0.767   0.4429    
## AlcoholYes              1.564820   0.845571   1.851   0.0642 .  
## Physical_ActivityYes   -0.032334   0.790741  -0.041   0.9674    
## HypertensionYes        -0.487613   0.939262  -0.519   0.6037    
## Metabolic_SyndromeYes   0.585040   0.932267   0.628   0.5303    
## SmokingYes             -0.346485   1.016937  -0.341   0.7333    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 535.016  on 385  degrees of freedom
## Residual deviance:  63.279  on 372  degrees of freedom
##   (49 observations deleted due to missingness)
## AIC: 91.279
## 
## Number of Fisher Scoring iterations: 12

library(broom)
tidy(modelo_aditivo_RAGE600)

## # A tibble: 14 × 5
##    term                   estimate std.error statistic      p.value
##    <chr>                     <dbl>     <dbl>     <dbl>        <dbl>
##  1 (Intercept)           -56.3      10.4       -5.40   0.0000000660
##  2 RAGE600_aditivoC/T      1.24      1.02       1.22   0.224       
##  3 RAGE600_aditivoT/T      0.885     5.86       0.151  0.880       
##  4 GLUCOSA                 0.00370   0.00492    0.752  0.452       
##  5 HbA1C                  -0.129     0.231     -0.556  0.578       
##  6 SexMale                -7.48      1.67      -4.48   0.00000746  
##  7 EDAD                    0.353     0.0678     5.21   0.000000189 
##  8 CREATININA             46.6       8.60       5.42   0.0000000587
##  9 HDL                     0.0189    0.0246     0.767  0.443       
## 10 AlcoholYes              1.56      0.846      1.85   0.0642      
## 11 Physical_ActivityYes   -0.0323    0.791     -0.0409 0.967       
## 12 HypertensionYes        -0.488     0.939     -0.519  0.604       
## 13 Metabolic_SyndromeYes   0.585     0.932      0.628  0.530       
## 14 SmokingYes             -0.346     1.02      -0.341  0.733

tidy(modelo_dominante_RAGE600)

## # A tibble: 13 × 5
##    term                   estimate std.error statistic      p.value
##    <chr>                     <dbl>     <dbl>     <dbl>        <dbl>
##  1 (Intercept)           -56.3      10.4       -5.40   0.0000000661
##  2 RAGE600_dominante       1.23      1.00       1.23   0.220       
##  3 GLUCOSA                 0.00370   0.00492    0.752  0.452       
##  4 HbA1C                  -0.130     0.230     -0.564  0.573       
##  5 SexMale                -7.47      1.67      -4.48   0.00000739  
##  6 EDAD                    0.354     0.0678     5.22   0.000000182 
##  7 CREATININA             46.6       8.60       5.42   0.0000000593
##  8 HDL                     0.0189    0.0246     0.768  0.442       
##  9 AlcoholYes              1.57      0.845      1.85   0.0637      
## 10 Physical_ActivityYes   -0.0265    0.784     -0.0338 0.973       
## 11 HypertensionYes        -0.489     0.939     -0.520  0.603       
## 12 Metabolic_SyndromeYes   0.585     0.933      0.627  0.530       
## 13 SmokingYes             -0.344     1.02      -0.339  0.735

tidy(modelo_recesivo_RAGE600)

## # A tibble: 13 × 5
##    term                   estimate std.error statistic      p.value
##    <chr>                     <dbl>     <dbl>     <dbl>        <dbl>
##  1 (Intercept)           -56.1      10.3        -5.44  0.0000000547
##  2 RAGE600_recesivo        0.947     6.09        0.156 0.876       
##  3 GLUCOSA                 0.00368   0.00487     0.754 0.451       
##  4 HbA1C                  -0.151     0.227      -0.663 0.507       
##  5 SexMale                -7.35      1.64       -4.48  0.00000764  
##  6 EDAD                    0.359     0.0685      5.24  0.000000162 
##  7 CREATININA             46.2       8.44        5.47  0.0000000450
##  8 HDL                     0.0193    0.0243      0.793 0.428       
##  9 AlcoholYes              1.60      0.828       1.93  0.0531      
## 10 Physical_ActivityYes    0.240     0.721       0.333 0.739       
## 11 HypertensionYes        -0.307     0.926      -0.331 0.740       
## 12 Metabolic_SyndromeYes   0.396     0.894       0.443 0.658       
## 13 SmokingYes             -0.307     0.990      -0.310 0.757

AIC(modelo_recesivo_RAGE600, modelo_dominante_RAGE600, modelo_aditivo_RAGE600)

##                          df      AIC
## modelo_recesivo_RAGE600  13 90.85568
## modelo_dominante_RAGE600 13 89.28253
## modelo_aditivo_RAGE600   14 91.27889

# Convertir la variable RAGE600 a factor con niveles específicos
data$RAGE600 <- factor(data$RAGE600, levels = c("C/C", "C/T", "T/T"))

modelo_simple_RAGE600 <- glm(Renal_Dysfunction ~ RAGE600, data = data, family = binomial, na.action = na.exclude)

OR_simple <- exp(coef(modelo_simple_RAGE600))
CI_simple <- exp(confint(modelo_simple_RAGE600))

## Waiting for profiling to be done...

p_values <- summary(modelo_simple_RAGE600)$coefficients[, 4]

OR_simple_completo <- c(1, OR_simple)
names(OR_simple_completo)[1] <- "RAGE600C/C"

CI_simple_completo <- rbind(c(1, 1), CI_simple)
rownames(CI_simple_completo)[1] <- "RAGE600C/C"

resultados <- data.frame(
  OR = OR_simple_completo,
  CI_lower = CI_simple_completo[, 1],
  CI_upper = CI_simple_completo[, 2],
  p_value = c(NA, p_values)  
)

print(resultados)

##                    OR  CI_lower CI_upper   p_value
## RAGE600C/C  1.0000000 1.0000000 1.000000        NA
## (Intercept) 1.0517241 0.8546139 1.294766 0.6338749
## RAGE600C/T  0.6614398 0.3327802 1.285589 0.2272702
## RAGE600T/T  0.5282332 0.1596413 1.560152 0.2609505

data$RAGE624 <- factor(data$RAGE624, levels = c("A/A", "A/T", "T/T"))

modelo_simple_RAGE624 <- glm(Renal_Dysfunction ~ RAGE624, data = data, family = binomial, na.action = na.exclude)

OR_simple <- exp(coef(modelo_simple_RAGE624))
CI_simple <- exp(confint(modelo_simple_RAGE624))

## Waiting for profiling to be done...

p_values <- summary(modelo_simple_RAGE624)$coefficients[, 4]

OR_simple_completo <- c(1, OR_simple)
names(OR_simple_completo)[1] <- "RAGE624A/A"

CI_simple_completo <- rbind(c(1, 1), CI_simple)
rownames(CI_simple_completo)[1] <- "RAGE624A/A"

resultados <- data.frame(
  OR = OR_simple_completo,
  CI_lower = CI_simple_completo[, 1],
  CI_upper = CI_simple_completo[, 2],
  p_value = c(NA, p_values)  
)

print(resultados)

##                    OR  CI_lower CI_upper   p_value
## RAGE624A/A  1.0000000 1.0000000 1.000000        NA
## (Intercept) 1.1750000 0.8726444 1.585907 0.2890580
## RAGE624A/T  0.7178538 0.4793512 1.072612 0.1063637
## RAGE624T/T  0.9511890 0.4628013 1.967292 0.8915018

data$RAGE625 <- factor(data$RAGE625, levels = c("A/A", "A/G", "G/G"))

modelo_simple_RAGE625 <- glm(Renal_Dysfunction ~ RAGE625, data = data, family = binomial, na.action = na.exclude)

OR_simple <- exp(coef(modelo_simple_RAGE625))
CI_simple <- exp(confint(modelo_simple_RAGE625))

## Waiting for profiling to be done...

p_values <- summary(modelo_simple_RAGE625)$coefficients[, 4]

OR_simple_completo <- c(1, OR_simple)
names(OR_simple_completo)[1] <- "RAGE625A/A"

CI_simple_completo <- rbind(c(1, 1), CI_simple)
rownames(CI_simple_completo)[1] <- "RAGE625A/A"

resultados <- data.frame(
  OR = OR_simple_completo,
  CI_lower = CI_simple_completo[, 1],
  CI_upper = CI_simple_completo[, 2],
  p_value = c(NA, p_values)  
)

print(resultados)

##                    OR  CI_lower CI_upper   p_value
## RAGE625A/A  1.0000000 1.0000000 1.000000        NA
## (Intercept) 1.0597015 0.8368844 1.342568 0.6301783
## RAGE625A/G  0.8762575 0.5606131 1.367672 0.5608650
## RAGE625G/G  0.7339593 0.3457577 1.529281 0.4109112

table(data$RAGE625)

## 
## A/A A/G G/G 
## 276 108  32

table(data$RAGE624)

## 
## A/A A/T T/T 
## 174 212  36

table(data$RAGE600)

## 
## C/C C/T T/T 
## 357  39  14

LauraAnalisys

Loranda_Calderon

2024-09-19