Sweet Preference in US-born East Asians vs East Asian Immigrants 35 years or younger

Author

May

Published

November 8, 2024

Hana Research Questions

1: To determine the differences in the added sugars intake between East Asian immigrants and U.S. Born East Asians

2: To determine the difference in in sweet foods and beverages liking between East Asian immigrants and U.S. born East Asians

3: To determine the difference in sweet taste liking between immigrants and U.S. born East Asians

Load packages and call data

Decoding survey

Code

## Liking score
liking.df <- survey.df %>% 
  select("ID", "FL-01_3":"FL-60_3") %>%
  mutate(veg.liking = rowMeans(select(., "FL-01_3":"FL-06_3"), na.rm = TRUE), 
         fruit.liking = rowMeans(select(., "FL-07_3":"FL-11_3"), na.rm = TRUE), 
         saltyfat.liking = rowMeans(select(., "FL-12_3":"FL-15_3"), na.rm = TRUE), 
         hfprotein.liking = rowMeans(select(., "FL-16_3":"FL-20_3"), na.rm = TRUE), 
         alcohol.liking = rowMeans(select(., "FL-22_3":"FL-25_3"), na.rm = TRUE), 
         wg.liking = rowMeans(select(., "FL-26_3":"FL-29_3"), na.rm = TRUE), 
         carbs.liking = rowMeans(select(., "FL-30_3":"FL-34_3"), na.rm = TRUE), 
         healthyfat.liking = rowMeans(select(., "FL-35_3":"FL-38_3"), na.rm = TRUE), 
        sweets.liking = rowMeans(select(., "FL-39_3":"FL-43_3"), na.rm = TRUE),
        ssb.liking = rowMeans(select(., "FL-44_3":"FL-47_3"), na.rm = TRUE),
        sfbl.liking = rowMeans(select(., "FL-39_3":"FL-47_3"), na.rm = TRUE),
         unhealthyfat.liking = rowMeans(select(., "FL-48_3":"FL-52_3"), na.rm = TRUE),
         protein.liking = rowMeans(select(., "FL-53_3":"FL-57_3"), na.rm = TRUE), 
         spicy.liking = rowMeans(select(., "FL-58_3":"FL-60_3"), na.rm = TRUE)) %>%
  select(c("ID", ends_with(".liking"))) 

## Three factor questionnaire
TFQ.df <- survey.df %>% 
  select("ID", "TFQ-01":"TFQ-21") %>%
  mutate(uncontrolled = rowSums(select(., "TFQ-03", "TFQ-06", "TFQ-08", "TFQ-09", "TFQ-12", "TFQ-13", "TFQ-15", "TFQ-19", "TFQ-20"), na.rm = TRUE)) %>%
  mutate(restraint = rowSums(select(., "TFQ-01", "TFQ-05", "TFQ-11", "TFQ-17", "TFQ-18", "TFQ-21"), na.rm = TRUE)) %>%
  mutate(emotional = rowSums(select(., "TFQ-02", "TFQ-04", "TFQ-07", "TFQ-10", "TFQ-14", "TFQ-16"), na.rm = TRUE)) %>%
    select(c("ID", "uncontrolled", "restraint", "emotional")) 

## Adult eating behavior questionnaire
AEBQ.df <- survey.df %>% 
  select("ID", "AEBQ-01":"AEBQ-35") %>%
  mutate(EF = rowSums(select(., "AEBQ-01", "AEBQ-03", "AEBQ-04"), na.rm = TRUE)) %>%
  mutate(EOE = rowSums(select(., "AEBQ-05", "AEBQ-08", "AEBQ-10", "AEBQ-16"), na.rm = TRUE)) %>%
  mutate(EUE = rowSums(select(., "AEBQ-15", "AEBQ-20", "AEBQ-27", "AEBQ-35"), na.rm = TRUE)) %>%
  mutate(FF = rowSums(select(., "AEBQ-02", "AEBQ-07", "AEBQ-12", "AEBQ-19", "AEBQ-24"), na.rm = TRUE)) %>%
  mutate(FR = rowSums(select(., "AEBQ-13", "AEBQ-22", "AEBQ-33"), na.rm = TRUE)) %>%
  mutate(SE = rowSums(select(., "AEBQ-14", "AEBQ-25", "AEBQ-26", "AEBQ-29"), na.rm = TRUE)) %>%
  mutate(H = rowSums(select(., "AEBQ-06", "AEBQ-09", "AEBQ-28", "AEBQ-32", "AEBQ-34"), na.rm = TRUE)) %>%
  mutate(SR = rowSums(select(., "AEBQ-11", "AEBQ-23", "AEBQ-30", "AEBQ-31"), na.rm = TRUE)) %>%
  select(c("ID", "EF":"SR")) 

## Body appreciation scale 
BAS.df <- survey.df %>% 
  select("ID", starts_with("BAS")) %>%
  mutate(BAS = rowSums(select(., 2:11), na.rm = TRUE))%>%
  select(c("ID", "BAS")) 

## Intuitive eating scale 
IES.df <- survey.df %>% 
  select("ID", starts_with("IES")) %>%
  mutate(IES = (rowSums(select(., 2:24), na.rm = TRUE))/23) %>%
  select(c("ID", "IES"))

## sHEI score
HEI.df <- survey.df %>%
  select("ID", "sex", "Fruit":"Water") %>%
  mutate(fruit.1 = case_when(
    Fruit == 1 ~ 0,
    Fruit == 2 ~ 2, 
    Fruit == 3 ~ 3.5,
    Fruit >= 4 ~ 5)) %>%
  mutate(fruit.2 = case_when (
    Juice == 1 ~ 0,
    Juice == 2 ~ 2,
    Juice == 3 ~ 3.5,
    Juice >= 4 ~ 5)) %>% 
  mutate(tfruitHEI = case_when(
    fruit.1 + fruit.2 <5 ~ 0,
    fruit.1 + fruit.2 >= 5 ~ 5)) %>%
  mutate(wfruitHEI = case_when(
    Fruit == 1 ~ 0,
    Fruit == 2 ~ 2.5, 
    Fruit >= 3 ~ 5)) %>%
  mutate(vegHEI = case_when(
    `Green veg` == 1 ~ 1.6,
    `Green veg` == 2 & `Starchy veg` >= 2 ~ 2.46,
    `Green veg` >= 2 & `Starchy veg` >= 2 ~ 3.24,
    `Green veg` >= 2 & `Starchy veg` == 1 ~ 3.56)) %>%
  mutate(bean.1 = case_when(
    `Green veg` == 1 ~ 0,
    `Green veg` >= 2 ~5)) %>%
  mutate(bean.2 = case_when(
    Beans == 1 ~ 0,
    Beans >= 2 ~ 5)) %>%
  mutate(beanHEI = case_when(
    bean.1 + bean.2 < 5 ~ 0,
    bean.1 + bean.2 >= 5 ~ 5)) %>%
  mutate(wgHEI = case_when(
    `Whole grains.day` == 1 ~ 0.51,
    sex == 1 & `Whole grains.day` >= 2 ~ 2.97,
    sex == 2 & `Whole grains.day` >= 2 & `Whole grains.day` <= 3 ~ 5.20,
    sex == 2 & `Whole grains.day` >= 4 ~ 6.94)) %>%
  mutate(dairyHEI = case_when(
    sex == 1 & `Milk.day` <= 3 ~ 3.22,
    sex == 2 & `Milk.day` <= 3 & `Low-fat milk.day` == 1 ~ 3.32,
    sex == 2 & `Milk.day` <= 3 & `Low-fat milk.day` >= 2 ~ 4.81,
    `Milk.day` >= 4 ~ 6.51)) %>%
  mutate(tproteinHEI = case_when(
    sex == 1 & `Seafood.freq` <= 4 ~ 4.11,
    sex == 1 & `Seafood.freq` >= 5 ~ 4.98,
    sex == 1 & is.na(`Seafood.freq`) ~ 4.11,
    sex == 2 ~ 4.97)) %>%
  mutate(sfpproteinHEI = case_when(
    sex == 1 & Nuts <= 2 ~ 0.49,
    sex == 2 & Nuts <= 2 ~ 1.50,
    Nuts >= 3 ~ 4.20)) %>%
  mutate(fatHEI = case_when(
    `Milk.day` >= 4 ~ 2.56,
    `Saturated fats` >= 2 & `Saturated fats` <= 3 & `Milk.freq` >= 1 & `Low-fat milk.freq` <= 2 ~ 2.63,
    `Saturated fats` >= 2 & `Saturated fats` <= 3 & is.na(`Milk.freq`) | is.na(`Low-fat milk.freq`) ~ 2.63,
    `Saturated fats` >= 2 & `Saturated fats` <= 3 & `Milk.freq` >= 1 & `Low-fat milk.freq` >= 3 ~ 4.54,
    `Saturated fats` == 1 & `Milk.freq` >= 1 ~ 5.93,
    `Saturated fats` == 1 & is.na(`Milk.freq`) ~ 5.93)) %>%
  mutate(grainHEI = case_when(
    `Green veg` == 1 ~ 2.13,
    `Grains.day` >= 3 & `Seafood.day` >= 2 & `Green veg` >= 2 ~ 2.27,
    `Grains.day` >= 3 & Nuts >= 1 & Nuts <= 2 & `Seafood.day` == 1 & `Green veg` >= 2 ~ 4.73,
    `Grains.day` >= 3 & Nuts >= 3 & `Seafood.day` == 1 & `Green veg` >= 2 ~ 8.45,
    `Grains.day` >= 1 & `Grains.day` <= 2 & `Green veg` >= 2 ~ 9.25)) %>%
  mutate(sodiumHEI = case_when(
    Fruit >= 1 & Fruit <= 2 & `Grains.day` >= 3 & Water == 3 ~ 0.70,
    Fruit >= 3 & `Grains.day` >= 3 & Water == 3 ~ 2.30,
    `Grains.day` >= 3 & Water >= 1 & Water <= 2 ~ 4.94,
    `Grains.day` >= 1 & `Grains.day` <= 2 ~ 6.07)) %>%
  mutate(ssb.cal = case_when(
    `SSB.day` == 1  ~ 0,
    `SSB.day` == 2 ~ 156,
    `SSB.day` == 3 ~ 312,
    `SSB.day` == 4 ~ 468,
    `SSB.day` == 5 ~ 624,
    `SSB.day` == 6 ~ 780,
    `SSB.day` == 7 ~ 936),
    sugar.cal = case_when(
    `Added sugars` == 1 ~ 130,
    `Added sugars` == 2 ~ 260,
    `Added sugars` == 3 ~ 520)) %>%
  mutate(sugar.intake = (ssb.cal + sugar.cal)) %>%
  mutate(sugarHEI = case_when(
    sugar.intake <= 130 ~ 10,
    sugar.intake > 130 & sugar.cal < 520 ~ 5,
    sugar.intake >= 520 ~ 0)) %>%
  mutate(sugar.tsp = case_when(
    `SSB.freq` <= 4 ~ 13.26,
    `SSB.freq` >= 5 & `SSB.freq` <=6 ~ 16.00,
    `SSB.day` ==2 ~ 16.00,
    `SSB.day` >=3 ~ 26.87)) %>%
  mutate(satfatHEI = case_when(
    `SSB.day` >= 3 ~ 1.82,
    `SSB.day` <= 2 & `Grains.day` <= 2 ~ 3.20,
    `SSB.day` <= 2 & `Grains.day` >= 3 & Nuts <= 2 ~ 4.64,
    `SSB.day` <= 2 & `Grains.day` >= 3 & Nuts >= 3 ~ 6.56)) %>%
  select("ID", "sugar.intake", "sugar.tsp", ends_with("HEI")) %>%
  mutate(sHEI = rowSums(select(., "tfruitHEI":"satfatHEI"), na.rm = TRUE)) %>%
  mutate(SuFatHEI = rowSums(select(., c("sugarHEI", "satfatHEI")))) 

## Putting scores together

survey.df$ID <- as.character(survey.df$ID)
anthro.df$ID <- as.character(anthro.df$ID)
SST.df$ID <- as.character(SST.df$ID)
redjade.df$ID <- as.character(redjade.df$ID)
liking.df$ID <- as.character(liking.df$ID)
HEI.df$ID <- as.character(HEI.df$ID)
TFQ.df$ID <- as.character(TFQ.df$ID)
BAS.df$ID <- as.character(BAS.df$ID)
IES.df$ID <- as.character(IES.df$ID)
AEBQ.df$ID <- as.character(AEBQ.df$ID)

full.df <- survey.df  %>%
  select(c("ID", "sex", "age", "race", "ethn", "usb", "education", "hinc", "exec", "ageCat")) %>%
  filter(race %in% c("African", "Asian")) %>%
  left_join(anthro.df, by = "ID") %>%
  left_join(SST.df, by = "ID") %>%
  left_join(redjade.df, by = "ID") %>%
  left_join(liking.df, by = "ID") %>%
  left_join(HEI.df, by = "ID") %>%
  left_join(TFQ.df, by = "ID") %>%
  left_join(BAS.df, by = "ID") %>%
  left_join(IES.df, by = "ID") %>%
  left_join(AEBQ.df, by = "ID") %>% 
  mutate(BMIcat = case_when(
      BMI < 18.5 ~ "0",
      BMI >= 18.5 & BMI  < 25 & race != "Asian" ~ "1",
      BMI >= 25 & BMI < 30 & race != "Asian" ~ "2",
      BMI > 30 & race != "Asian" ~ "3",
      BMI >= 18.5 & BMI  < 23 & race == "Asian" ~ "1",
      BMI >= 23 & BMI < 25 & race == "Asian" ~ "2",
      BMI > 25 & race == "Asian" ~ "3"))  %>%
  mutate(BAScat = ntile(BAS, 2)) %>%
  mutate(UEcat = ntile(uncontrolled, 3)) %>%
  mutate(CRcat = ntile(restraint, 3)) %>%
  mutate(EMcat = ntile(emotional, 3)) %>%
  mutate(SFBLcat = ntile(sfbl.liking, 3)) %>%
  mutate(UHFcat = ntile(unhealthyfat.liking, 3)) %>%
  mutate(SugarCat = ntile(sugar.intake, 3)) %>%
  mutate(DQ = ntile(sHEI, 2)) 

full.df$group <- paste(full.df$race, full.df$usb) 

African.df <- full.df %>%
  filter(race == "African")
  
Asian.df <- full.df %>%
  filter(race == "Asian")

USB.df <- full.df %>%
  filter(usb == "U.S. Born")
  
Immigrant.df <- full.df %>%
  filter(usb == "Immigrant")

write_xlsx(full.df,"/Users/maycheung/Documents/Analyses/SweetLiking//full.xlsx")

Demographics and counts

Code

## Counts - by ancestry and USB
Asian.df %>%
  group_by(usb) %>%
  filter(age <= 40) %>%
  count()

# A tibble: 2 × 2
# Groups:   usb [2]
  usb           n
  <chr>     <int>
1 Immigrant    50
2 U.S. Born    61

Code

## Summary table
Asian.demo <- Asian.df %>% 
  group_by(usb) %>%
  select(-ID) %>%
  filter(age <= 40) %>%
  get_summary_stats()

write_xlsx(full.df,"/Users/maycheung/Documents/Analyses/SweetLiking//Asian.demo.xlsx")

Correlations: Corrplot, SST x diet

Code

## Corr DF
corr.df <- Asian.df %>%
  filter(age <= 35) %>%
  select(age, usb, hinc, BMI, sugar.intake, sHEI, sfbl.liking, SST) 

## All Participants
all.corr <- corr.df %>%
      select(-usb) %>%
      cor(use = "pairwise.complete.obs")

p.mat <- corr.df %>%
    select(-usb) %>%
    cor_pmat(.) %>%
  column_to_rownames("rowname")

ggcorrplot(all.corr, type = "lower", p.mat = p.mat, insig = "blank", lab = TRUE, lab_size = 2.5, colors = c("#E46726", "white", "#6D9EC1")) +
    ggtitle("All East Asians")

No id variables; using all as measure variables

Code

## Asian USB
AsiUSB.corr <- corr.df %>%
      filter(usb == "U.S. Born") %>%
      select(-usb) %>%
      cor(use = "pairwise.complete.obs")
    
p.mat <- corr.df %>%
    filter(usb == "U.S. Born") %>%
    select(-usb) %>%
    cor_pmat(.) %>%
  column_to_rownames("rowname")
    
ggcorrplot(AsiUSB.corr, type = "lower", p.mat = p.mat, insig = "blank", lab = TRUE, lab_size = 2.5, colors = c("#E46726", "white", "#6D9EC1")) +
  ggtitle("US-born East Asians")

No id variables; using all as measure variables

Code

## Asian immigrant
AsiIMM.corr <- corr.df %>%
      filter(usb == "Immigrant") %>%
      select(-usb) %>%
      cor(use = "pairwise.complete.obs")
    
p.mat <- corr.df %>%
    filter(usb == "Immigrant") %>%
    select(-usb) %>%
    cor_pmat(.) %>%
  column_to_rownames("rowname")
    
ggcorrplot(AsiIMM.corr, type = "lower", p.mat = p.mat, insig = "blank", lab = TRUE, lab_size = 2.5, colors = c("#E46726", "white", "#6D9EC1")) +
  ggtitle("East Asian Immigrants")

No id variables; using all as measure variables

Differences: by birth origin

Code

## Age
age.aov <- Asian.df %>%
  filter(age <= 35) %>%
  aov(age ~ as.factor(usb), .)
summary(age.aov)

                Df Sum Sq Mean Sq F value Pr(>F)
as.factor(usb)   1   11.4   11.35   0.896  0.346
Residuals      106 1343.6   12.68

Code

Asian.df %>%
  filter(age <= 35) %>%
  ggplot(aes(x = as.factor(usb), y = age)) +
  geom_violin(aes(fill = usb), trim = FALSE) + 
  geom_boxplot(width = 0.2)+
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  labs(x = "Birth Origin", y = "Age") +
  theme_bw() +
  theme(legend.position = "none")

Code

## SST
SST.aov <- Asian.df %>%
  filter(age <= 35) %>%
  aov(SST ~ as.factor(usb), .)
summary(SST.aov)

                Df Sum Sq Mean Sq F value Pr(>F)
as.factor(usb)   1    913   913.1    0.86  0.356
Residuals      106 112548  1061.8

Code

Asian.df %>%
  filter(age <= 35) %>%
  ggplot(aes(x = as.factor(usb), y = SST)) +
  geom_violin(aes(fill = usb), trim = FALSE) + 
  geom_boxplot(width = 0.2)+
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  geom_hline(yintercept=0, linetype="dashed", color = "red", size=1) +
  labs(x = "Birth Origin", y = "Sweet Taste Liking") +
  ylim(-150, 150) +
  theme_bw() +
  theme(legend.position = "none")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Code

## Sweet foods and beverages liking
sfbl.aov <- Asian.df %>%
  filter(age <= 35) %>%
  aov(sfbl.liking ~ as.factor(usb), .)
summary(sfbl.aov)

                Df Sum Sq Mean Sq F value Pr(>F)
as.factor(usb)   1    868   868.5   1.673  0.199
Residuals      106  55034   519.2

Code

Asian.df %>%
  filter(age <= 35) %>%
  ggplot(aes(x = as.factor(usb), y = sfbl.liking)) +
  geom_violin(aes(fill = usb), trim = FALSE) + 
  geom_boxplot(width = 0.2)+
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  geom_hline(yintercept=0, linetype="dashed", color = "red", size=1) +
  labs(x = "Birth Origin", y = "Sweet Foods and Beverages Liking") +
  ylim(-150, 150) +
  theme_bw() +
  theme(legend.position = "none")

Code

## Sugar intake
sugar.intake.aov <- Asian.df %>%
  filter(age <= 35) %>%
  aov(sugar.intake ~ as.factor(usb), .)
summary(sugar.intake.aov)

                Df  Sum Sq Mean Sq F value Pr(>F)
as.factor(usb)   1   91081   91081   1.825   0.18
Residuals      106 5289147   49898

Code

Asian.df %>%
  filter(age <= 35) %>%
  ggplot(aes(x = as.factor(usb), y = sugar.intake)) +
  geom_violin(aes(fill = usb), trim = FALSE) + 
  geom_boxplot(width = 0.2)+
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  geom_hline(yintercept=0, linetype="dashed", color = "red", size=1) +
    labs(x = "Birth Origin", y = "Added Sugars Intake") +
  theme_bw() +
  theme(legend.position = "none")