2020_Sanzone.docx

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The Association Between Urbanized Diet and the Prevalence of Overweight, Diabetes, and Hypertension in China

Erin Sanzone

Honors Essay Department of Nutrition

University of North Carolina at Chapel Hill 2020

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I. Introduction

Urbanization and Diet

China is a rapidly growing population, with more than 1.4 billion people as of January

2019.1_{The rapid urbanization experienced in recent years has been paralleled with an increase in}

diet-related chronic diseases, such as hypertension, diabetes, and overweight. Between the years

1980 and 2009, China has experienced the greatest human migration from rural to urban areas in

history, climbing from 191 million to 622 million, with 51% of the population living in urban

areas.2_{This marked the first time in Chinese history in which the urban population outnumbered}

the rural sector. Not only have people moved to urban areas, but also without movement,

urbanization within regions has increased. Among many effects, the urbanization has impacted

the diet of the population of the nation.

The unprecedented urbanization has become a public health concern for the population of

China. The “nutrition transition” in Asia explains the dramatic changes in food consumption and

eating habits.3_{Du, et al. analyzed the population of China from 1982-1997 and found the start of}

the economic boom in the 1985s dramatically changed the dietary habits of the population.4 _The

percentage of energy from fat in urban residents increased from 27.3% in 1982 to 32.8% in 1997.

The urban population also had a decrease in energy expenditure and an increase in leisure time.

Zhai, et al. studied the population of China from 1989-2004, with a sample size of 5,000 and

found the rise in economy of China in 1990 influenced the change in changes in income and

diet.5_{There was a great increase in fat (19% to 28% between 1989 and 2004), with a dramatic}

decrease in carbohydrates in response. They also found nutrition deficiencies decreased while the

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Baker, et al. found processed foods have become a large concern across Asian countries

due to the foods’ increase in refined sugars, sodium, and saturated and trans-fats.6_With

urbanization in China, the population has increased consumption of foods high in fat, protein,

added sugars, and sodium. Pork is heavily consumed, and egg and poultry consumption is

rising.7_{Manzel, et al. have indicated that high-fat diets are factors that may associate with}

increased adiposity, specifically an increase in white adipose tissue and systemic inflammation.8

Furthermore, obesity is a risk factor for hypertension. The risk of cardiometabolic disease is

climbing as Chinese diets decrease in consumption of fruits of whole grains, increase in sodium

and alcohol, as well as more processed and high fat foods, among other factors that contribute to

cardiometabolic diseases.9

In parallel to the nutrition transition, there has also been a retail revolution in China.

Zhang, et al. found the rise of supermarkets from 2000-2010 in China was the fastest growth of

the supermarket industry than anywhere else in the world.10 _{Zhou, et al. analyzed the food retail}

industry in China and its impact on health across the nation.11_{The retail revolution has created a}

dramatic increase in processed and packaged foods, which are high in sugars, fat, and sodium.

Based on data from the 2011 China Health and Nutrition Survey, Zhou, et al. found the greatest

percentage of people consuming processed foods live in megacities or highly urbanized areas.12

Furthermore, in a large population-based study in China with a sample size of 3,682 spanning

many provinces, Tian, et al. found almost 13% of the sodium of the urban population is derived

from processed foods, while on average, only 6% of the sodium consumed in rural areas comes

from processed foods.13_{The transition can have an impacting effect on the health of the nation.}

Not only has the type of foods changed, the eating habits of the Chinese population has

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merely a green tea between meals.14_{Using 3-day, 24-hour recall data from 1991-2009 China}

Health and Nutrition Survey with an average of approximately n=11,000 at each year, Wang et

al. found a statistically significant increase in snacking prevalence, frequency of snacking

occasions, and total daily energy from snacks from 1991 to 2009.15_{The rate of snacking}

statistically significantly increased in level of SES and in urban compared to rural areas. Snacks

were defined as food consumed outside the “normal” set meals: breakfast, lunch, and dinner.

Common snacks were fruits, fast food, grains, sugar-sweetened beverages, and animal foods.

Fruits were found to be the most prominent snack choice.16

In terms of cooking methods, there has been a decline in boiling and baking food.

Instead, there has been a greater emphasis of frying food in excess oils. Wang, et al. found, from

1991 to 2004, the prevalence of calories/day coming from fried foods increased on average by

10% across three levels of income and education, with a larger percentage of urban population

consuming snacks than rural population.17_{Additionally a mega-analysis between 1990-2015}

presented by Wang, et al. has been an increase in eating outside the house, including at food

stands, restaurants, etc., as well as purchasing pre-prepared meals from supermarkets.18_{Tian, et}

al., using data from the CHNS, observed a positive correlation between BMI and eating out.19

Moreover, Li, et al. presented a meta-analysis of urbanization in China and its impact on

environmental and human health between 1990 and 2011. Li, et al. found the urban population is

characterized as engaging in less physical activity, more smoking, and greater alcohol

consumption than the rural population.20_{In addition, the rate of mortality from cardiometabolic}

diseases jumped from 36.6% in 1990 to 41.5% in 2011 in urban areas.21_{Barry Popkin found the}

decrease in physical activity as the widespread use of technology affected the urban population

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in the rural populations, has led to a more sedentary lifestyle.22_{The lack of physical activity has}

likely contributed to the increase in obesity and overweight individuals across the country.As for

alcohol consumption, alcohol has long been present for the Chinese population for cultural

events. However, the consumption of alcohol has dramatically increased.23_{Iona Millwood, et al.}

has found that the urban population is more likely to engage in recreational alcohol use than the

rural population.24 _{A greater consumption of alcohol was linked to higher blood pressure and}

regular smoking. Millwood, et al. found each 20g/day increase in alcohol consumption was

associated with an increase in systolic blood pressure of 2 mmHg.

Hou et al. (2008), in a population-based longitudinal study with a randomly selected

sample of 5,362 adults aged 25-65 years of age from Shangai, found that approximately one third

of the sample was overweight in 2004.25_{In a more recent publication, Wang et al}₍₂₀₁₅₎

reported the prevalence of hypertension of 23.25% in a nationally-representative sample of

China (n=451,755) between 2012-2015 .26_{Hu et al (2018) found the incidence of type II diabetes}

has rapidly increased from 1% in 1980 to 10.8% in 2013 based on a nationally-representative

cross-sectional study of China (n=170,287).27, 28

There has been an overwhelming change in the dynamics of eating habits in the Chinese

population, paralleled with the country’s swift urbanization. The shift away from traditional

cooking methods and traditional foods may play a large role in the health of the urban

population.

Thus, there is an urgent need to understand how an urbanized diet is associated with

diet-related chronic diseases, such as hypertension, diabetes, and overweight to work toward

improving the health of the nation. To this end, we used a single measure of urbanized diet

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urbanization index for overall urbanization based on a variety of factors, such as population size,

population density, type of occupations, and the number of markets that provides a nuanced

index of overall urbanization.30 _{Our team also has developed a diet urbanization index based on}

foods and eating habits considered a part of an urban diet. Using this new urbanized diet measure

for 2015, we will examine the association between urbanized diet and diet-related chronic

diseases: overweight, hypertension, and diabetes.

II. Methods

Data Collection

We used data from the China Health and Nutrition Survey. The CHNS is a longitudinal

study that was started in 1989, with subsequent surveys every 2 to 4 years for a total of 10 rounds

of surveys until 2015, collecting demographic, health, and nutritional information among the

participants. The initial data was collected using a random cluster design and utilized

questionnaires to collect the data.31 _{The initial CHNS surveys took place in nine provinces in}

China, using a multistage, random-cluster design, with three additional megacities included in

2011 and three additional provinces included in 2015. The CHNS was designed to capture

urban-rural differences in selected provinces and megacities, and it is not nationally representative.

All 15,718 adult participants (≥ 18 years old) with diet data collected in 2015 were

considered eligible participants (Figure 1). We excluded individuals who had any of the

following characteristics: pregnant at the time of exam (n=53) or individuals with missing or

unreasonable (below 500 and above 10,000) daily calorie intake (n=47). We also excluded 53

individuals as they were missing the urbanized diet measure, our main independent variable. We

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annual household income per capita, physical activity, daily calorie intake, or education (n =

1,637). After these exclusions, our final analytic sample was n= 13,981 participants for the

health outcome regressions, with different sample sizes for each outcome: overweight

(n=12,658), hypertension (13,979), and diabetes (12,019), based on the participants with data for

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Figure 1: Flow Chart of Sample from China Health and Nutrition Survey (2015) and

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activity. Table 1 lists the variables incorporated into the urbanized diet measure. Scores were

assigned to each variable using different methods depending on the nature of the variable and

frequency of positive response or rate of consumption. Continuous variables categorized on a 0-4

scale were categorized on a 0-4 scale using two different methods. The method used was

determined based on the percentage of consumers for each variable. If 80% or more of the

sample were consumers, quintiles were created to assign scores. A score of 0 corresponded to the

1st_{quintile, including non-consumers, 1 corresponded to the 2}nd_{quintile, and so on with 4}

corresponding to the 5th_{quintile. If less than 80% of the sample were consumers, scoring was}

completed using quartiles. Non-consumers were assigned a score of 0, while scores of 1-4

corresponded to the 1st_{through 4}th_{quartiles of consumption. Binary questions received either a}

“0” or “4” for “no” or “yes” depending on the urbanization of the variable. For the only

negatively associated variable included, percent of calories consumed from carbohydrates, the

highest consumption of carbohydrates was given the lowest score for diet urbanization.

Carbohydrates are readily replaced by fats or proteins, and thus, we found a decrease in

carbohydrates as urbanized diet increases. The quintile method was used, dividing the percent of

calories consumed from carbohydrates into five levels. The highest quintile was assigned a score

of 0, the next highest was assigned a score of 1, and so on, with the lowest consumption assigned

a score of 4, correlating to the highest diet urbanization. The total urbanized diet measure of each

individual was found by adding the variable scores together. The mean urbanized diet measure

was 19.65 units, with a standard deviation of 8.13 units, and a median of 20 units, with a range

from 0 - 44 units, as seen in Figure 2.

Figure 2:Distribution of the Urbanized Diet Measure in Analytical Sample from China

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Outcome Measures

Whelton, et al. (2017) wrote a meta-analysis of observational studies regarding blood

pressure and the definition of hypertension. After collecting blood pressures and analyzing the

risk of cardiometabolic diseases across dozens of samples, hypertension is defined as having

blood pressure greater than 130/80 mmHg.33_{Wang, et al. (2013) used a cross-sectional survey}

with 170,287 participants to find the prevalence of diabetes in China. Researchers measured

fasting plasma glucose and hemoglobin levels; for those without diagnosed diabetes, a 2-hour

oral glucose tolerance test was conducted.34 _{Undiagnosed diabetes was defined by fasting plasma}

glucose levels of 126 mgl/dL or greater, 2-hour plasma glucose level of 200 mg/dL or greater.

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measured height, weight, waist-to-hip ratio, and body fat percentage. Those with a BMI ≥24kg

m2

were considered overweight..35_{The hypertension sample has 13,979 participants, the diabetes}

sample has 12,019 participants, and the overweight sample has 12,658 participants.

Covariates

Age, annual household income per capita, and highest education level attained were

covariates in all statistical models. Smoking and sex were also included although in models

where both smoking and sex were included, due to strong correlation between smoking status

and sex, (men are much more likely to smoke than women) they were included as a combination

variable to control for both factors. Specifically, we defined five categories: (1) man, never

smoker (2) man, past smoker, (3) man, current smoker, (4) woman, never smoker, or (5) woman,

ever smoker. We controlled for smoking, as it may be a confounding variable in the overall

health of the individuals. Furthermore, we controlled for household income per capita could

influence access to food. We also controlled for education as we predicted it may influence food

choices. In addition, we controlled for physical activity, as this is a confounder of the association

between diet and health. Sodium and potassium, measured from a household weighed inventory

in combination with three consecutive 24-hour recalls, were included as covariates in the

hypertension models.

Data Analysis

All data analysis was completed using STATA version 16. We quantified the association

between the urbanized diet measure and cardiometabolic risk, measuring outcomes

(hypertension, overweight, and diabetes) using adjusted odds ratios. We ran mixed effect

logistics models, analyzing odds ratios and their complementary confidence intervals, accounting

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we used ANOVA and chi-squared testing to determine the significance of differences in

continuous and categorical variables by quartiles of urbanized diet measure.

We used two sequential mixed effects logistic regression models for regressions for

diabetes and overweight, and three sequential models for hypertension. The first model for all

outcomes included only the crude and sociodemographic covariates sex, age (in years), education

(none, primary school or less, completed primary school, college or more), per capita household

income inflated to 2015 value (k), and region. In the second model, we controlled for all of those

covariates and additionally controlled for lifestyle behaviors: 3-day average energy intake (kcal),

sex-smoking bivariable (female never smoke, male never smoke, female ever smoke, male

former smoke, male current smoke), and physical activity (total METs/week). For the

hypertension model, we included an additional third model, which controlled for all of the

sociodemographic and lifestyle behavior data but also added sodium, and potassium as

covariates. In all the models, to ensure a meaningful effect size, all continuous measures,

including the urbanized diet measure, were scaled by their standard deviation. We present

adjusted odds ratios and confidence intervals for all models.

III. Results

Sociodemographic

The sociodemographic data for the analytic samples is described in Table 2. The sample

had a mean age of 51.5, with slightly more women than men. About the same amount individuals

were found in Central and South regions, with fewer from the North region. A majority of

participants had completed primary school, while only 14.5% completed college or more.

As seen in Table 3, only about 4% of females ever smoked, while majority have never

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average total METs/week was 162.1, with a standard deviation of 171.5 METs/week, indicating

a wide range in amount of physical activity.

As seen in Table 4, approximately half the sample was hypertensive and half were

overweight. Diabetes prevalence was approximately nine percent.

Table 5 presents the differences in covariates across quartiles of the urbanized diet

measure: low urbanized diet measure (0 -14), low-moderate urbanized diet measure (15-20),

moderate-high urbanized diet measure (21-25), and high urbanized diet measure (26-44). There

were overall differences in cardiometabolic disease prevalence (Chi-squared p<0.05). Prevalence

of overweight and diabetes tended to be higher across quartiles of urbanized diet; however,

hypertension had a negative correlation with urbanized diet measure. There were overall

differences in means in age, sex, education, region, sex-smoke, urbanization index, household

income, and physical activity based on ANOVA and chi-squared testing (p<0.05). For example,

in the lowest urbanized diet quartile, 5% of individuals were college graduates, while in the

highest urbanized diet measure quartile 25.4% of individuals had graduated college.

Furthermore, average age was 53.4 years (standard deviation (15.0)) for the first quartile of

urbanized diet measure and was 51.3 years (standard deviation (15.1)) for the fourth quartile. On

the other hand, physical activity decreases with increasing urbanized diet measure. We saw

evidence of differences in patterns of smoking by sex across quartiles, with the highest quartile

having slightly higher percent of non-smokers (58.3% female never smokers, 16.4% male never

smokers) as compared to the first quartile (54.1% female never smokers, 13.3% male never

smokers). Average three-day average kcal/day did not differ across quartiles of urbanized diet

measure (p>0.05).

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In Table 6, we estimated the effect for a 1-standard deviation (8.14) difference in

urbanized diet relative to each of the cardiometabolic disease measures. We found a 1 standard

deviation increase in our urbanized diet measure was associated with higher odds of being

overweight (OR= 1.08; 95% CI:1.03, 1.13) and diabetic (OR=1.15; 95% CI:1.07, 1.25). This

association was robust to the inclusion of each set of covariates. We did not find a statistically

significant association between urbanized diet score and hypertension in any of the models. Most

notably, the odds ratio was not statistically significant with hypertension in Model III, which

controlled for sodium and potassium. Increased sodium and lowered potassium levels have been

associated with an increased risk for hypertension for decades.36 _{It was interesting that these did}

not play a larger role in our analysis.

In Table 7, we present the odds ratios covariates for each cardiometabolic outcome. Male

never smoker and former smoker were both associated with higher risk of cardiometabolic

outcomes as compared to male current smokers, while female never and ever smokers were

associated with lower risk of cardiometabolic outcomes as compared to male current smokers.

Higher education was inversely related to the hypertension and overweight (Model I) with an

odds ratio of 0.70 (0.56, 0.88) and 0.73 (0.59, 0.90) respectively for college or more as compared

to those without any education. However, for diabetes, no education was associated with lower

risk of cardiometabolic outcome as compared with all other levels of education. Average calories

per day were positively associated with urbanized diet measure in hypertension Models II and III

and overweight Model II with odds ratios of 1.22 (1,06, 1.40), 1.18 (1.00, 1.40), and 1.42 (1.25,

1.62) respectfully. However, average kcal/day was not statistically significant for the diabetes

models. Sodium was positively associated with hypertension in Model III with an odds ratio of

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income and physical activity were not statistically significantly associated with cardiometabolic

outcomes.

IV. Discussion

Our findings suggest a positive association between urbanized diet with overweight and

diabetes, even after controlling for a wide range of sociodemographic and lifestyle factors related

to overall urbanization. As such, consuming an urbanized diet, composed of high fats, processed

foods, sweet snacks, and eating out was associated with higher overweight and diabetes

prevalence. However, we did not find a similar association for hypertension. In addition,

education and smoking were two variables that were associated with the cardiometabolic

outcomes.

Previous research has indicated that a Western diet has been associated with an increased

in cardiometabolic diseases across China.37_{Specifically the increase in animal food}

consumption, fats, alcohol, and decreased whole grains has contributed to an increase in

cardiometabolic diseases of the urban population of Chinese adults.38_{Our results for overweight}

and diabetes were similar to these previous studies, but we did not find the same positive

association between urbanized diet and hypertension.39 _{However, none of these previous studies}

looked at urbanized diet overall as we did in this study. Though former studies have analyzed

associations between specific dietary items and cardiometabolic diseases, our urbanized diet

score provided a more robust look of urban diet by including both eating habits and specific

foods together. A possible reasoning for the lack of statistically significant results for

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than environmental accumulation. Thus, the urbanized diet measure would be less likely to have

a strong association with hypertension without addressing genetic predisposition.

Ming Li conducted a longitudinal study from 1991-2011 using the CNHS data (n=2318)

and found an association between cardiometabolic diseases and lower education level, higher

income, and living in a more urbanized area.40 _{The results from this study paralleled that of our}

study. With an increase in household income, there is a greater increase in money available to

purchase food and equipment, which may also explain the slight increase in average calorie

intake per day. Furthermore, urbanization index positively correlated with urbanized diet

measure validates the urbanized diet measure as a diagnostic tool in terms of measuring

urbanization. Lastly, those with a greater education are likely more informed on nutrition and the

needs to remain healthy. Therefore, our study suggests greater education can be associated as a

preventative measure for cardiometabolic diseases, while a greater income and living in a more

urbanized area can be associated with an increased risk for cardiometabolic disease. Average

kcal/day across quartiles of urbanized diet measure was not statistically significant.

On the other hand, physical activity decreases with increasing urbanized diet measure.

These results parallel previous research. Popkin suggests that more urban areas have more access

to cars and other public transportation, which can contribute to the decrease in physical activity

as urbanization increases.41

Risk of Type II diabetes has been associated with a more urbanized diet and lifestyle.42

We hypothesized that we would find a positive association between urbanized diet measure and

prevalence of diabetes in China, which we observed. Furthermore, based on our results, people

who consumed more urbanized diets had a greater association with overweight within the

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Results from our study have important implications for intervention and prevention to

reduce cardiometabolic diseases. One of China’s greatest risks for its increasing cardiometabolic

diseases is the overproduction and overconsumption of processed foods. Our findings suggest

that to reduce risk of cardiometabolic diseases, particularly overweight and diabetes, preventive

and policy efforts to reduce the percentage of fat, sodium, and added sugars in processed foods

and drinks. Furthermore, fast food restaurants should be more strictly regulated and provide

alternative and healthier options for customers. Additionally, promoting more physical activity in

adults would prove beneficial to health, especially in more urbanized areas where leisure time

increased and technology has decreased need for physical movement.

Overall, the findings of our study are limited to the population of China and cannot be

extrapolated to other populations. However, it is a solid model relative to other urbanizing

countries in order to prevent cardiometabolic disease incidence that China has experienced as

China has led the world in its rapid urbanization and other countries are likely to follow a similar

pattern.

Limitations

Our greatest limitation is the self-report data from CNHS. There is always self-reporting

error in questionnaires. Another limitation is the lack of generalizability to other nations.

We were unable to capture alcohol consumption in our urbanized diet variable due to a

large proportion of missingness. Yet we know that alcohol consumption is associated with

chronic diseases. It should also be noted that the majority of the participants have Type II

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variable. The estimated incidence of Type I diabetes per 100,000 per year was 1.01 as of 2013.43

Therefore, those with Type I diabetes should take up around 0.001% of the diabetes sample

group.

As the data was self-reported, the actual measure of sodium and potassium may have

been incorrect and thus, skewed results. Sodium, as the main extracellular ion of the cell, and

potassium, as the main intracellular ion of the cell, studies have long indicated that sodium and

potassium have been associated with hypertension. High levels of sodium and low levels of

potassium have a positive association with hypertension, based on a meta-analysis from the New

England Journal of Medicine of 2007.44_{A low intake of potassium signals cells to increase}

sodium absorption to maintain tonicity, which in turn increases blood pressure and poses a threat

for developing hypertension. The analysis concluded that a 50-100 mmol/day excess intake of

sodium and a decrease in potassium excretion by 50 mmol are both individually associated with

a greater risk in developing hypertension, but both cases together, prove to be most detrimental.

While our sodium results were statistically significant, our results for potassium were not

statistically significant within the hypertension models.

Further Research

In the future, a longitudinal study would be an interesting extension of our research.

Since cardiometabolic diseases develop overtime and one’s diet can change throughout the years,

it would be exciting and possibly more informative to conduct a longitudinal study. Our current

study assumes the preservation of dieting habits. Therefore, by including the past few decades,

we can access how diet has both changed and affected health within urbanized versus rural

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Further research should focus on a longitudinal study, starting when the swift start of the

“nutrition transition” occurred in China to see how the existence of cardiometabolic diseases

have shaped during this period of rapid urbanization. Further research should attempt to

understand the role of food marketing and other urban lifestyle changes that have led to changes

in eating behaviors across the country.

Conclusion

Our cross-sectional study in China of 2015 suggests an association between urbanized

diet and diabetes and overweight prevalence in Chinese adults. We found that higher education

was inversely related to hypertension and overweight, comparing college graduates to those

without an education, while for diabetes, all levels of education were positively correlated with

the cardiometabolic outcome. Average kcal/day were positively associated with urbanized diet

measure for hypertension and overweight, but not diabetes. Furthermore, male never and former

smokers had a higher risk of cardiometabolic outcomes compared to male current smokers, while

female never smokers and ever smokers were inversely related to cardiometabolic outcomes.

Overall, our study suggests a shift in current Chinese eating and physical activity habits to

decrease risk of cardiometabolic diseases in the ever-growing urban population.

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Tables and Figures

Table 1: Variables included in the Urbanized Diet Measure* _{- China Health and Nutrition}

Survey Wave 2015

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Fridge Household owns refrigerator

Microwave Household owns microwave

Fruit % calories consumed from fruit

Nuts and Seeds % calories consumed from nuts and seeds

Sweet Snacks % calories consumed from sweet snacks

All Snacks Avg # all snacks / day

Eggs % calories consumed from eggs

Dairy % calories consumed from dairy

Fried Food % calories consumed from fried foods

Eating Out % calories consumed from fruit

High-fat Meat % calories consumed from high-fat meat

Carbohydrates % calories consumed from carbohydrates (3-day avg)

Fat % calories consumed from fats (3-day avg)

Animal Products % calories consumed from animal sources

*Urbanized diet measure was constructed using food, dietary habits, and cooking tools related to urbanized diet lifestyle factors45

Table 2: Physical and SES Characteristics of Sample - China Health and Nutrition Survey Wave 2015*

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Age 51.5 15.1

Sex Male 42.6% 0.5

Female 57.4%

Education None 7.4%

Primary School or Less 24.0%

Primary school 54.1%

College or more 14.5%

Region North 17.9%

Central 40.6%

South 41.5%

* The China Health and Nutrition Survey (CNHS) is a cross-sectional study, wave 2015 (n=13,981 with exclusions)

Table 3: Lifestyle behaviors, China Health and Nutrition Survey Wave 2015*

Variable Mean Std deviation

Sex - male %† _Overall _42.6%

Current smokers 58.8%

Former smokers 7.5%

Never smokers 33.7%

Sex - female % Overall 57.4%

Ever smokers 4.3%

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Urbanization index‡ _72.5 _17.7

Urbanized Diet Measure§ _19.6 _8.1

Household income (k)| _79.0 _119.1

3-day avg kcal¶ _1842.1 _623.9

Physical Activity (Total METs/wk) 162.2 171.5

† Due to strong correlation between smoking status and gender, they were included as a combination variable to control for both factors

‡ Urbanized index defined as urbanization in China, as defined by Jones-Smith and Popkin with data from CNHS.46 § Urbanized diet measure was constructed using food, dietary habits, and cooking tools related to urbanized diet lifestyle factors47

| Per capita total gross HH income (k) inflated to 2015 ¶ 3-Day Ave.: Energy (kcal)

 Total METs/wk from all PA (5 domestic PA) with estimated value

Table 4: Percentage of Hypertension, Type II Diabetes, Overweight, China Health and Nutrition Survey Wave 2015*

Cardiometabolic Disease Percent

Hypertension† _58.1%

Diabetes‡ _9.1%

Overweight§ _48.1%

† Hypertension is defined as having blood pressure greater than 130/80 mmHg48,49

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Table 5: Sociodemographic and lifestyle factors across quartiles of urbanized diet China Health and Nutrition Survey Wave 2015*

Low urbanized diet measure (0-14)† Low-moderate urbanized diet measure quartile (15-20)‡ Moderate-high urbanized diet measure quartile (21-25)§ High urbanized diet measure quartile|_(26-44)

P-value for test of differences

between quartiles¶

Age 53.4 (15.0) 51.8 (15.2) 51.1 (15.1) 51.3 (15.1) <0.0001

Sex Male 48.2% 48.5% 48.1% 43.3% <0.0001

Female 51.8% 51.5% 51.9% 56.7%

Education None 12.2% 7.6% 5.1% 3% <0.0001

Primary School or

Less

31.9% 27.0% 21.0% 14.2%

Primary School

50.9% 54.5% 56.6% 57.4%

College or more

5.0% 10.9% 16.5% 25.4%

Region North 17.2% 18.7% 17.0% 19.9% <0.0001

Central 45.8% 33.9% 34.6% 44.4%

South 37.0% 47.4% 48.4% 35.6%

Sex-smoke _Female

Never Smoke

54.1% 53.6% 54.2% 58.3% <0.0001

Male Never Smoke

13.3% 14.0% 13.9% 16.4%

Female Ever Smoke

3.0% 3.2% 3.7% 1.8%

Male Former

Smoke

2.8% 3.1% 3.7% 3.3%

Male Current

Smoke

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Urbanization index _{62.1 (16.8)} _{71.2 (17.2)} _{75.9 (16.1)} _{82.6 (13.1)} _<0.0001

Household income (k) _62.0

(100.4)

77.3 (142.1) 78.8 (91.7) 99.4 (125.5) <0.0001

3-day avg kcal _1,828.3

(657)

1,847.5 (641.6)

1,859.7 (617.8)

1,862.6 (578.1)

0.0546

Physical Activity (Total

METs/wk)

184.0 (197.9) 162.4 (172.1)

151.2 (158.7)

128.9 (136.9)

<0.0001

Hypertension _61.8% _58.9% _57.5% _57.2% _<0.0001

Overweight _46.2% _48.3% _50.1% _49.8% _0.003

Diabetes _7.8% _8.9% _9.8% _10.9% _<0.0001

†Urbanized diet measure score (0-14) n= 4,469

‡ Urbanized diet measure score (15-20) n= 4,056

§ Urbanized diet measure score (21-25) n= 3,304

| Urbanized diet measure score (26-44) n= 3,789

¶ ANOVA testing was used for continuous variables and chi-squared testing was used for categorical variables

Due to strong correlation between smoking status and gender, they were included as a combination variable to control for both factors

Urbanized index defined as urbanization in China, as defined by Jones-Smith and Popkin with data from CNHS.52

. Per capita total gross HH income (k) inflated to 2015 3-Day Ave.: Energy (kcal)

. Total METs/wk from all PA (5 domestic PA) with estimated value

 Hypertension is defined as having blood pressure greater than 130/80 mmHg53, 54

 For an individual to be considered overweight, the Chinese define it has having a BMI ≥24kg/m255

 Diabetes was based on fasting glucose greater than 126 mg/dL, medications, or self-diagnosis.56

Table 6: Odds ratios (95% Confidence Intervals) Cardiometabolic Outcomes Associated with a 1 Standard Deviation Change in Urbanized Diet Measure*

Model I† Model II‡ _{Model III}§

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Odds Ratio (CI) (0.95, 1.05) (0.95, 1.05) (0.95, 1.05)

Overweight¶

Odds Ratio (CI):

1.08 (1.03, 1.13) 1.07 (1.02, 1.12) ---Diabetes

Odds Ratio (CI):

1.15 (1.07, 1.25)

1.15 (1.06, 1.24)

--* Urbanized diet measure was constructed using food, dietary habits, and cooking tools related to urbanized diet lifestyle factors57

† Controlling for sex, age, region, education, per capita household income

‡ _{Controlling for smoking/gender combination, region, education, per capita household income, physical activity,}

3-day average daily kcal intake

§ Controlling for smoking, sex, and the interaction between smoking and sex, region, education, per capita household income, physical activity, 3-day average daily kcal intake, sodium, and potassium

| Hypertension is defined as having blood pressure greater than 130/80 mmHg58, 59

¶ For an individual to be considered overweight, the Chinese define it has having a BMI ≥24kg/m260

Diabetes was based on fasting glucose greater than 126 mg/dL, medications, or self-diagnosis.61

Table 7: Odds ratios (95% Confidence Intervals) Cardiometabolic Outcomes Associated with a 1 Standard Deviation Change in Urbanized Diet Measure - with Covariates

Hypertension* Odds Ratio (CI) _Overweight_†_Odds

Ratio (CI)

Diabetes‡ Odds Ratio (CI)

Model I§ Model II|

Model

III¶

Model I Model II Model I Model II

Urbanized Diet Measure (1 SD unit) 1.00 (0.95, 1.05) 1.00 (0.95, 1.05) 1.00 (0.95, 1.05) 1.08 (1.03, 1.13) 1.07 (1.02, 1.12) 1.15 (1.07, 1.24) 1.15 (1.06, 1.24)

Sex _0.59

(0.54, 0.64) 0.56 (0.48, 0.65) 0.56 (0.48, 0.65) 0.87 (0.81, 0.94) 0.66 (0.58, 0.76) 0.78 (0.68, 0.90) 0/87 (0.68, 1.10)

Sex- smoke _{Male current}

smoke

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Female never smoke 0.64 (0.58, 0.71) 0.65 (0.59, 0.72 1.06 (0.97, 1.17) 0.79 (0.67, 0.94) Male never smoke -- 1.08 (0.95, 1.24) 1.08 (0.94, 1.24) -- 1.42 (1.25, 1.62)

-- 0.98 (0.78, 1.23) Female ever smoke -- 0.52 (0.39, 0.69) 0.53 (0.40, 0.70) -- 0.86 (0.67, 1.10)

-- 1.08 (0.73, 1.60) Male former smoke -- 1.40 (1.06, 1.84) 1.40 (1.06, 1.84) -- 1.31 (1.04, 1.64)

-- 1.17 (0.84, 1.62) Age 1.06 (1.05, 1.06) 1.06 (1.05, 1.06) 1.06 (1.05, 1.06) 1.01 (1.00, 1.01) 1.01 (1.00, 1.01) 1.05 (1.04, 1.06) 1.05 (1.04, 1.06)

Region North Ref Ref Ref Ref Ref Ref Ref

Central 1.02 (0.82, 1.27) 0.99 (0.80, 1.24) 0.99 (0.8, 1.22) 0.71 (0.60, 0.83) 0.67 (0.57, 0.78) 0.77 (0.63, 0.95) 0.78 (0.62, 0.95) South 0.60 (0.48, 0.74) 0.58 (0.47, 0.72) 0.59 (0.48, 0.73) 0.53 (0.45, 0.62) 0.51 (0.44, 0.60) 0.68 (0.54, 0.84) 0.69 (0.55, 0.86)

Education _None _Ref _Ref _Ref _Ref _Ref _Ref _Ref

Primary school or less 1.12 (0.94, 1.35) 1.10 (0.92, 1.33) 1.12 (0.94, 1.36) 1.07 (0.91, 1.26) 1.07 (0.91, 1.25) 1.46 (1.10, 1.94) 1.47 (1.10, 1.95)

Primary school 0.93 (0.77, 1.11) 0.91 (0.76, 1.10) 0.922 (0.764, 1.11) 0.99 (0.84, 1.12) 0.97 (0.82, 1.15) 1.67 (1.25, 2.24) 1.66 (1.24, 2.22)

College or more 0.70 (0.56, 0.88) 0.69 (0.55, 0.87) 0.70 (0.56, 0.89) 0.73 (0.59, 0.90) 0.70 (0.57, 0.86) 1.35 (0.93, 1.96) 1.31 (0.90, 1.91)

HH income (k) (1 SD unit) _1.03

(0.99, 1.07) 0.99 (0.96, 1.04) 1.03 (0.98, 1.07) 0.99 (0.96, 1.04) 0.99 (0.99, 1.00) 0.97 (0.89, 1.06) 0.97 (0.89, 1.06)

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METs/wk) (1 SD unit) _(0.92, 1.00) (0.98, 1.07) (0.99, 1.00) 1.00)

Average kcal/day (1 SD unit) _-- _1.22

(1.06, 1.40) 1.18 (1.00, 1.40) -- 1.42 (1.25, 1.62)

-- 1.02 (0.81, 1.26)

Sodium (mg/day) (1 SD unit) _-- _-- _1.07

(1.02, 1.12)

-- -- --

--Potassium (mg/day) (1 SD unit)  _-- _-- _0.97

(0.92, 1.03)

-- -- --

--* Hypertension is defined as having blood pressure greater than 130/80 mmHg62, 63

† For an individual to be considered overweight, the Chinese define it has having a BMI ≥24kg/m264 ‡ _{Diabetes was based on fasting glucose greater than 126 mg/dL, medications, or self-diagnosis}65

§ Controlling for sex, age, region, education, per capita household income

| Controlling for smoking/gender combination, region, education, per capita household income, physical activity, 3-day average daily kcal intake

¶ Controlling for smoking, sex, and the interaction between smoking and sex, region, education, per capita household income, physical activity, 3-day average daily kcal intake, sodium, and potassium

Urbanized diet measure was constructed using food, dietary habits, and cooking tools related to urbanized diet lifestyle factorsa

“Male” used as reference group

. “Male current smoke”” used as reference group; due to strong correlation between smoking status and gender, they were included as a combination variable to control for both factors

North region used as reference group

No education used as reference group

Per capita total gross HH income (k) inflated to 2015

 Total METs/wk from all PA (5 domestic PA) with estimated value 3-Day Ave.: Energy (kcal)

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