The prevalence of overweight and obesity is increasing worldwide, and high body weight is one of the risk factors for many chronic diseases, such as diabetes, cardiovascular disease, and some cancers(Reference Uzogara1). The prevalence of overweight and obesity has increased in the Chinese population in the past decade(Reference Jiang, Xu and Bi2,Reference Ouyang, Wang and Su3) . According to the results of Chinese national surveys, the prevalence of overweight increased from 37·4 % in 2000 to 41·2 % in 2014; the prevalence of obesity increased from 8·6 % in 2000 to 12·9 % in 2014 and the prevalence of abdominal obesity increased from 13·9 % in 2000 to 24·9 % in 2014(Reference Tian, Jiang and Wang4). With implications for future chronic disease, overweight and obesity have become one of the most serious public health problems in China.
Numerous studies have examined the risk factors for obesity among Chinese adults, ranging from biological to behavioural factors such as genes(Reference Yu, Han and Cao5), diet patterns(Reference Wang, Du and Wang6,Reference Zou, Zhang and Xia7) , physical activity(Reference Zhang8) and sleep patterns(Reference Sun, Huang and Wang9). These studies found that certain polymorphisms in the adipokine genes, a high-fat diet, lack of physical activity and sedentary lifestyle, and insufficient sleep were all associated with high body weight and obesity, consistent with previous studies on the risk factors for obesity worldwide(Reference Hruby, Manson and Qi10,Reference Malik, Willett and Hu11) .
In addition to these factors, stress is a daily occurrence in modern life. Several studies have found that stress is positively associated with overweight and obesity. Adults experiencing chronic stressors are more likely to be obese and have higher waist circumference compared with adults experiencing no stressors(Reference Isasi, Parrinello and Jung12–Reference Tomiyama14). Stress has been known to influence weight change through physiological processes, food intake, physical activity levels, sleep patterns and sedentary behaviours(Reference Tomiyama14–Reference Mouchacca, Abbott and Ball17). On the other hand, stress may also be associated with weight loss. For example, the Whitehall II study found that adults with the lowest quintile of BMI at baseline lost weight at follow-up if they were under high job stress(Reference Kivimäki, Head and Ferrie18).
To date, the associations between stress and obesity have mainly been examined in Western countries. In recent years, mental health problems, including perceived stress, have become increasingly common in China. Nearly 11–15 % of Chinese population experienced poor mental health and many adults report experiencing high levels of stress(Reference Xiaolan, Xuefeng and Zhiyan19). Considering the possible health effects of chronic stress on obesity and subsequent chronic disease risk, it is important to understand if there is a relationship between perceived stress and obesity in China, as has been observed in Western countries. Therefore, the objective of the present study was to examine the relationship between perceived stress and adiposity using a national sample of Chinese adults. We further examined whether these associations varied by rural or urban residential areas, as food availability may be different depending on residential areas(Reference Liao, Tan and Wu20,Reference Zhou, Du and Su21) , and mental stress levels may also be different among residential areas(Reference Wang, Yang and Yang22), both of which could influence perceived stress and adiposity outcomes.
Methods
Study sample
The China Health and Nutrition Survey (CHNS) is an international collaborative project between the Carolina Population Center at the University of North Carolina at Chapel Hill and the National Institute for Nutrition and Health at the Chinese Center for Disease Control and Prevention (CCDC). The survey began in 1989 and was repeatedly carried out every 2–4 years through 2015. Recruitment involved a multistage, random cluster process to draw respondents from 12 provinces and municipal cities that vary substantially in geography, economic development, resources and health indicators. The survey includes a variety of nutritional and health indicators of residents. Data for CHNS 2015 were released in June 2018 and is publicly available (https://www.cpc.unc.edu/projects/china/data/datasets/longitudinal). Data from the 2015 CHNS were used in the present study because it was the only survey that included the assessment of perceived stress.
Respondents with missing BMI (n 1066), implausible BMI (>65 kg/m2 or <10 kg/m2) (n 5), missing waist circumference (n 35) and implausible waist circumference (<20 cm) (n 36) were excluded from the analytic sample. Respondents with missing or implausible educational level and marital status (n 20) were also excluded from the analytic sample. The primary analytic sample was comprised of 8385 adults aged 18–99 years.
Perceived stress
Perceived stress was measured by the 14-item perceived stress scale(Reference Cohen, Kamarck and Mermelstein23), a measure of perceived psychological stress that has been translated into Chinese, with demonstrated good validity and reliability in the Chinese population(Reference Leung, Lam and Chan24). The 14-item scale includes seven positive questions and seven negative questions. Positive questions assessed the ability to cope with existing stress, while negative questions assessed lack of control in life and negative affective responses(Reference Leung, Lam and Chan24). Adults were asked to rate the frequencies of their feelings and thoughts during the last month using a five-point Likert scale (never, almost never, sometimes, fairly often and very often). Response choices for positive questions were reverse-coded, and all responses were summed to create an overall perceived stress score. The overall perceived stress score ranged from 0 to 56 (Cronbach’s α is 0·81 in the present study), and higher scores indicate greater perceived stress. Similar to prior studies, perceived stress was further categorised into quintiles based on population cut-points (quintile 1: range 0–17; quintile 2: range 18–22; quintile 3: range 23–25; quintile 4: range 26–27 and quintile 5: range 28–52).
BMI
Adults’ weight and height were measured by trained CHNS staff. Height was measured, without shoes or cap, to the nearest 0·2 cm, and weight was measured to the nearest 0·1 kg. BMI was calculated as weight (kg) divided by the square of height (m). BMI was categorised using WHO cut-points: underweight, <18·5 kg/m2; normal weight, 18·5–24·9 kg/m2; overweight but not obese, 25·0–29·9 kg/m2 and obese, ≥30·0 kg/m2. BMI was further categorised using cut-points established by the National Health and Family Planning Commission of China (NHFPC) for Chinese adults: underweight, <18·5 kg/m2; normal weight, 18·5–23·9 kg/m2; overweight but not obese, 24·0–27·9 kg/m2 and obese, ≥28·0 kg/m2(25).
Waist circumference
Waist circumference was also measured by trained CHNS staff at the horizontal position of the lower margin of the rib arch and the midline of the iliac crest. Waist circumference was measured to the nearest 0·1 cm. In the present study, waist circumference was categorised using WHO cut-points: moderate abdominal obesity was defined as >94 cm and ≤102 cm for males and >80 cm and ≤88 cm for females; abdominal obesity was defined as >102 cm for males and >88 cm for females(26). We also used Chinese-specific cut-points for waist circumference: moderate abdominal obesity was defined as ≥85 cm and <90 cm for males and ≥80 cm and <85 cm for females; abdominal obesity was defined as ≥90 cm for males and ≥85 cm for females(25).
Covariates
Sociodemographic covariates in the analysis included age, sex, education level, marital status, residential area, employment status and personal income level. All covariates were self-reported by adults in CHNS. Age was categorised as 18–34, 35–44, 45–54, 55–65 and >65 years; sex was classified as female and male; education level included primary school or less than primary school, middle school, high school, and college or higher than college; marital status included married or not married. Residential areas of adults were constructed from the CHNS original sampling unit. Sampling units, including cities, county towns, suburban villages and rural villages, are officially identified by the National Bureau of Statistics of China. CHNS classifies suburban villages and rural villages as rural areas and classifies cities and county towns as urban areas. Employment status included employed or not employed at the time of the survey, and personal income level was categorised into three tertiles (low: ≤16 000 RMB; medium: 16 000·01–32 000 RMB and high: >32 000 RMB) based on adults’ income.
Statistical analysis
Descriptive analyses were conducted using chi-squared test for categorical variables and ANOVA for continuous variables to analyse differences in demographic characteristics by perceived stress quintiles, BMI and waist circumference among adults. Mean (sd) was reported for continuous variables and per cent (%) was reported for categorical variables.
Multivariate linear regression models were used to examine the relationship between perceived stress quintiles and continuous BMI. Results are presented as β-coefficients and 95 % CI. We tested the tolerance and autocorrelation of multivariate linear regression models and used robust linear regression to account for heteroscedasticity. We further examined a potential non-linear relationship between perceived stress and BMI using restricted cubic splines. Multinomial logistic regression models were used to examine the relationship between perceived stress quintiles and BMI categories, with normal weight as the base group. Multinomial logistic regression models were also used to examine the relationships between perceived stress quintiles and waist circumference categories, with normal waist circumference as the base group. Results from multinomial logistic regression models are presented as relative prevalence ratios and 95 % CI. All models adjusted for age, sex, education level, marital status, residential area, employment status and personal income level.
Effect modification by residential area on the associations of perceived stress and BMI and waist circumference was examined using likelihood-ratio tests. Statistical significance was considered at P < 0·05 and statistical analyses were conducted using Stata SE 16.0 (StataCorp.).
Results
Sample characteristics
The average age of the adults was 53·9 (14·1) years and 52·2 % of the adults were female. Approximately 12·9 % of adults had at least a college education and 30·2 % of adults had a primary school education or fewer years. The majority of the adults were married (88·7 %), lived in rural areas (59·6 %) and were employed at the time of the survey (52·9 %). Adults’ average personal annual income was 29 282·9 (27 911·1) RMB.
The average perceived stress score of adults was 22·7 (6·2). Figure 1 presents the distribution of perceived stress among all adults and Figure 2 shows the distributions of perceived stress by adults’ socio-economic characteristics. Table 1 shows the distribution of perceived stress quintiles by socio-demographic characteristics of the adults. Adults were more likely to be in the upper quintiles of perceived stress if they were with higher age (P = 0·002), were female (P = 0·048), had lower education levels (P < 0·001), were not married (P < 0·001), living in rural areas (P < 0·001), were not employed at the time of the survey (P = 0·007) and had lower personal annual income (P < 0·001).
Fig. 1 Distribution of perceived stress among adults in China Health and Nutrition Survey 2015 (n 8385). 
the histogram is the observed density of perceived stress; 
the dash line is the estimated density of perceived stress
Fig. 2 Distributions of perceived stress across socio-economic categories among adults in China Health and Nutrition Survey 2015 (n 8385). Box plots: box is interquartile range, the horizontal bar inside the box is the median, whiskers extend 1·5 interquartile range on each side of the box and the values outside the whiskers are not plotted. X-axis represents adults’ socio-economic characteristics
Table 1 Sociodemographic characteristics by quintiles of perceived stress among adults in China Health and Nutrition Survey 2015 (n 8385)*
* Percentages may not add to 100 due to rounding.
† Cut-points for categorical perceived stress: quintile 1: 0–17; quintile 2: 18–22; quintile 3: 23–25; quintile 4: 26–27 and quintile 5: 28–52.
‡ P value is for chi-square test of perceived stress quintiles.
Table 2 shows the distributions of BMI and waist circumference among all adults and by perceived stress quintiles. The average BMI of adults was 24·3 (3·6) kg/m2. Using the WHO BMI categories, 4·1 % of the adults were underweight, 34·5 % were overweight but not obese and 6·0 % were obese. Using the Chinese-specific BMI categories, 4·1 % of the adults were underweight, 36·1 % were overweight but not obese and 14·5 % were obese. Adults in the lower quintiles of perceived stress (P = 0·013) were more likely to be in higher BMI categories using WHO cut-points.
Table 2 Distribution of BMI and waist circumference by perceived stress quintiles among adults in the China Health and Nutrition Survey 2015 (n 8385)
* Cut-points for categorical perceived stress: quintile 1: 0–17; quintile 2: 18–22; quintile 3: 23–25; quintile 4: 26–27 and quintile 5: 28–52.
† P value is for ANOVA of continuous BMI, for chi-square test of categorical BMI and for chi2 test of categorical waist circumference.
‡ Mean (sd) was reported for continuous BMI.
§ WHO cut-points for BMI: underweight, <18·5 kg/m2; normal weight, 18·5–24·9 kg/m2; overweight but not obese, 25·0–29·9 kg/m2 and obese, ≥30·0 kg/m2.
‖ Per cent of normal weight category is omitted for it can be calculated by: 100 %–underweight %–overweight but not obese %–obese %.
¶ Chinese-specific cut-points for BMI: underweight, <18·5 kg/m2; normal weight, 18·5–23·9 kg/m2; overweight but not obese, 24·0–27·9 kg/m2 and obese, ≥28·0 kg/m2
** Per cent of normal waist circumference is omitted for it can be calculated by: 100 % minus the per cents of two kinds of elevated waist circumference.
†† WHO cut-points for waist circumference: moderate abdominal obesity is defined as >94 cm and ≤102 cm for male and >80 cm and ≤88 cm for female, while abdominal obesity is defined as >102 cm for male and >88 cm for female.
‡‡ Chinese-specific cut-points for waist circumference: moderate abdominal obesity is defined as ≥85 cm and <90 cm for male and ≥80 cm and <85 m for female, while abdominal obesity is defined as ≥90 cm for male and ≥85 for female.
Using the WHO cut-points for waist circumference, 23·5 % of the adults had moderate abdominal obesity and 19·2 % had abdominal obesity. Using the Chinese-specific cut-points for waist circumference, 19·1 % of the adults had moderate abdominal obesity and 42·6 % had abdominal obesity. Adults in the lower quintiles of perceived stress (P = 0·013) were more likely to have abdominal obesity using Chinese-specific cut-points.
Associations between perceived stress and adiposity
Table 3 presents the associations between perceived stress quintiles and BMI and waist circumference. In the multivariate linear models for perceived stress quintiles and continuous BMI, higher perceived stress quintiles were significantly associated with lower continuous BMI (overall P value <0·001). Compared to adults in the lowest quintile of perceived stress, adults in the second quintile of perceived stress had a 0·35 kg/m2 lower BMI (95 % CI: −0·59, −0·11), adults in the third quintile of perceived stress had a 0·44 kg/m2 lower BMI (95 % CI: −0·69, −0·19), adults in the fourth quintile of perceived stress had a 0·48 kg/m2 lower BMI (95 % CI: −0·74, −0·21) and adults in the top quintile of perceived stress had a 0·44 kg/m2 lower BMI (95 % CI: −0·67, −0·21), after adjusting for sociodemographic characteristics. Supplemental Figure 1 shows the association between continuous perceived stress and continuous BMI using restricted cubic spline analyses, adjusted for sociodemographic characteristics. Similar to the analysis using perceived stress quintiles, continuous perceived stress scores were negatively correlated with continuous BMI, which plateaued at higher levels of perceived stress. A test of non-linearity showed that a linear relationship could not be rejected (P = 0·265).
Table 3 Associations between perceived stress quintiles and BMI and waist circumference among adults in the China Health and Nutrition Survey 2015 (n 8385)*,†,‡
* Linear regression models were used for perceived stress quintiles and continuous BMI; multinomial logistic regression models were used for perceived stress quintiles and categorical BMI and categorical waist circumference.
† Models adjusted for age, sex, education level, marital status, residential area, employment status and personal income level.
‡ *P < 0·05, **P < 0·01, ***P < 0·001.
§ Cut-points for categorical perceived stress: quintile 1: 0–17; quintile 2: 18–22; quintile 3: 23–25; quintile 4: 26–27 and quintile 5: 28–52.
‖ Overall P value is for the overall association between perceived stress quintiles and adiposity, calculated from likelihood-ratio test.
¶ Coefficient of multiple linear regression.8
** 95 % CI
†† RPR: relative prevalence ratios
‡‡ WHO cut-points for BMI: underweight, <18·5 kg/m2; normal weight, 18·5–24·9 kg/m2; overweight but not obese, 25·0–29·9 kg/m2 and obese, ≥30·0 kg/m2.
§§ Chinese-specific cut-points for BMI: underweight, <18·5 kg/m2; normal weight, 18·5–23·9 kg/m2; overweight but not obese, 24·0–27·9 kg/m2 and obese, ≥28·0 kg/m2.
‖‖ WHO cut-points for waist circumference: moderate abdominal obesity is defined as >94 cm and ≤102 cm for male and >80 cm and ≤88 cm for female; abdominal obesity is defined as >102 cm for male and >88 cm for female.
¶¶ Chinese-specific cut-points for waist circumference: moderate of abdominal obesity is defined as ≥85 cm and <90 cm for male and ≥80 cm and <85 cm for female; abdominal obesity is defined as ≥90 cm for male and ≥85 for female.
In multinomial logistic regression models for perceived stress quintiles and the WHO cut-points for BMI categories, higher perceived stress quintiles were significantly associated with lower relative prevalence of being obese (BMI ≥ 30 kg/m2) (overall P value = 0·015). Compared to adults in the lowest quintile of perceived stress, adults in the third quintile of perceived stress had 0·65 times the prevalence of obesity (95 % CI: 0·48, 0·87), adults in the fourth quintile of perceived stress had 0·62 times the prevalence of obesity (95 % CI: 0·45, 0·85) and adults in the top quintile of perceived stress had 0·72 times the prevalence of obesity (95 % CI: 0·55, 0·94). No overall significant associations between perceived stress quintiles and BMI categories were observed when using Chinese-specific cut-points (overall P value = 0·098), but some individual comparisons between perceived stress quintiles and obesity (BMI ≥ 28 kg/m2) were significant. Compared to adults in the lowest quintile of perceived stress, adults in the fourth quintile of perceived stress had 0·77 times the prevalence of obesity (95 % CI: 0·62, 0·96) and adults in the top quintile of perceived stress had 0·79 times the prevalence of obesity (95 % CI: 0·65, 0·96).
Multinomial logistic regression models for perceived stress quintiles and waist circumference showed that adults in the top quintile of perceived stress had a lower relative prevalence of abdominal obesity compared to adults in the lowest quintile (relative prevalence ratio = 0·73, 95 % CI: 0·61, 0·88; overall P value = 0·047). Associations were similar when using Chinese-specific cut-points for waist circumference: adults in the highest quintile of perceived stress had a lower relative prevalence of abdominal obesity (comparing top to bottom quintile, relative prevalence ratio = 0·77, 95 % CI: 0·67, 0·89; overall P value = 0·017).
No significant effect modification was found by residential area on the associations between perceived stress quintiles and the outcomes of BMI and waist circumference.
Discussion
To our knowledge, this was the first study to examine the associations between perceived stress and BMI and waist circumference in a large and national sample of Chinese adults. Results showed that adults in the higher quintiles of perceived stress had, on average, lower continuous BMI, and lower relative prevalence of obesity and abdominal adiposity using both WHO and Chinese-specific cut-points. These associations were independent of adults’ sex, age, education level, marital status, residential area, employment status and personal income level and did not differ by adults’ residential areas.
Contrary to our findings, prior studies conducted in predominantly Western countries have found that perceived stress is associated with higher obesity and greater weight gain(Reference Isasi, Parrinello and Jung12–Reference Tomiyama14). In a 5-year longitudinal study in Australia, respondents’ perceived stress and negative life events at baseline were associated with greater weight gain(Reference Harding, Backholer and Williams27). A cross-sectional study of Hispanic/Latino adults in the USA also showed that adults with higher chronic stress had higher odds of being overweight and having elevated waist circumference(Reference Isasi, Parrinello and Jung12). A 3-year follow-up study of Australian women showed that higher perceived stress was associated with higher odds of obesity both in baseline cross-sectional and longitudinal analyses, in part due to more frequent fast food consumption, less leisure-time physical activity and more television time(Reference Mouchacca, Abbott and Ball17). However, other studies have shown that stress may also be associated with lower appetite and weight loss(Reference Kivimäki, Head and Ferrie18,Reference Serlachius, Hamer and Wardle28–Reference Wardle, Chida and Gibson30) .
There are several possible explanations for the differential relationships between perceived stress and obesity between Western and Asian countries. The first explanation is the difference in the role of dietary intake and eating behaviours in response to perceived stress. Previous studies in Western countries have found that perceived stress was associated with uncontrolled eating and emotional eating, resulting in weight gain(Reference Isasi, Parrinello and Jung12,Reference Tomiyama14,Reference Järvelä-Reijonen, Karhunen and Sairanen31) . However, a study of women in urban South Asia showed that women reported a loss of appetite in response to the stress of marital fights(Reference Snell-Rood32). Another study of immigrant women in Korea showed that respondents with the highest stress scores were less likely to consume meals regularly and skipped breakfast more often. In the same study, higher stress scores were also associated with lower intakes of total energy, carbohydrates, protein and fat, and women with higher stress were more likely to be underweight(Reference Hwang, Lee and Kim33). In a study among female Chinese immigrants, results showed that positive life events were associated with higher energy intake, while migration-related stress was negatively associated with food intake(Reference Marilyn and Fang34). Similarly, among Chinese female students residing in America, perceived stressful conditions were associated with decreased dietary intake eaten(Reference Kandiah, Saiki and Dues35). These different dietary habits as a consequence of stress may lead to differential relationships between stress and obesity in Western countries compared to Asian countries.
Changes in eating behaviour in response may also be mediated by the food environment. While many Asian countries are undergoing nutrition transformations of the local food environments, individuals in Asian countries generally consumed less processed foods than individuals in Western countries(Reference Gehlhar and Regmi36–Reference Popkin38). In 2015, China was in the early stages of this nutrition transformation, processed food represented less than 1/3 of food purchases during that period(Reference Zhou, Du and Su21,Reference Popkin38,Reference Reardon, Timmer and Minten39) , and most processed foods were sold in supermarkets(Reference Gómez and Ricketts40). Individuals in less urbanised areas may have lower access to large-sized supermarkets nearby(Reference Liao, Tan and Wu20), and thus lower exposure to processed foods. Besides, fast food restaurants were mainly located in a few metropolitan cities and were just starting to emerge slowly in smaller cities of China before 2015(Reference Baker and Friel41), people had more limited options of highly processed foods during stressful times. Thus, differences in food environments may also explain the different associations between perceived stress and adiposity observed in Western countries and the present study.
Another explanation is differences in other lifestyle behaviours as a response to stress between individuals in Western and Asian countries. Cultural values and norms can influence individual stress-coping strategies. Research suggests that adults in Western countries exhibit greater unconscious eating behaviours in response to stress(Reference Isasi, Parrinello and Jung12,Reference Tomiyama14,Reference Järvelä-Reijonen, Karhunen and Sairanen31) , while research among Chinese populations suggest adults may smoke more(Reference Cui, Rockett and Yang42) or consume more alcohol(Reference Park, Anastas and Shibusawa43) in response to stress, both of which could lead to weight loss(Reference Wang44,Reference Pengpid and Peltzer45) .
Finally, there may be differences in cultural attitudes towards obesity between Western countries and in China. In Chinese culture, ‘happy mind and fat body’ is a well-known idiom describing the relationship between mood and obesity, where being ‘fat’ is a symbol of wealth and life satisfaction. This belief is still very popular in China. For example, one study found that Chinese people with obesity were less likely to suffer from psychological problems(Reference Bin Li, Yin Ho and Man Chan46), which is consistent with the jolly ‘fat’ hypothesis proposed by Crisp and McGuiness(Reference Crisp and McGuiness47). Another study examined the relationship between subjective well-being and adult BMI using the Chinese General Social Survey; their results showed that those with higher levels of happiness tended to have higher BMI(Reference Li, Chen and He48). Conversely, another well-known idiom in China is ‘No desire for eating foods or drinking tea’, which means feelings of anxiety or unhappiness are related to loss of appetite(Reference Li, Chen and He48). These cultural factors demonstrate how wealth or positive affection is connected with body weight and may explain why high levels of perceived stress are associated with lower levels of adiposity observed in the present study.
The study had some limitations. First, this study was cross-sectional, so a causal relationship between perceived stress and adiposity cannot be determined. Previous studies have shown that stress can be a cause of obesity through higher levels of emotional eating and lower physical activity(Reference Tomiyama14). On the other hand, greater adiposity could also increase perceived stress through weight stigma, forming a ‘vicious cycle’ where weight stigma increases psychological stress, leading to increased eating, weight gain and subsequently greater weight stigma(Reference Foss and Dyrstad49,Reference Tomiyama50) . Longitudinal data can help to better examine the effects of perceived stress on weight gain over time and account for potential reverse causation. Second, we were unable to incorporate sampling weights or variables related to sample design as this information was not provided in the CHNS data set. Although consistent with previous studies using CHNS data(Reference Zou, Su and Du51–Reference Jiang, Zhang and Du54), the lack of data on sample design and sampling weights may have impacted some of the effect estimates and standard errors and the results are not generalisable to the national Chinese population. Third, although the measurement of perceived stress in this study has been validated, it cannot identify different types of objective stressors, which may have different cognitive and physical effects(Reference Heatherton, Herman and Polivy55,Reference Torres and Nowson56) . Moreover, the duration of stress may also moderate the effects of stress on obesity(Reference Greeno and Wing57). Future research should consider incorporating perceived and objective measures of stress, as well as more detailed assessments on the type, severity and duration of individual stressors, to better understand how stress can differentially influence BMI and waist circumference. Lastly, the associations between perceived stress quintiles and adiposity showed a probable non-linear relationship, namely that the associations between perceived stress quintiles and BMI tended to plateau at the highest quintile of perceived stress. Future research should analyse the precise non-linear relationship between perceived stress and adiposity.
Despite these limitations, this study is the first research to examine the relationship between perceived stress and adiposity in Chinese adults, which is critical when obesity and mental health disorders are both increasing in China(Reference Jiang, Xu and Bi2,Reference Xiaolan, Xuefeng and Zhiyan19) . Other strengths of this study include the large study population, measured anthropometric outcomes by trained research staff, and the use of a widely used and validated measure of perceived stress adapted for the Chinese population. The present study contributes to the literature on stress and adiposity, which have mainly focused on individuals in Western countries.
Conclusion
In the present study, we found that perceived stress was inversely associated with adiposity among Chinese adults. Future studies should aim to better understand the directionality of observed associations and the potential biological and behavioural mechanisms underlying these associations. If causal, the results of this study may inform the development of health management and health intervention programmes to simultaneously promote healthy stress management and healthy body weight in the Chinese population.
Acknowledgements
Financial support: This research uses data from China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Health, China Center for Disease Control and Prevention, Carolina Population Center (P2C HD050924, T32 HD007168), the University of North Carolina at Chapel Hill, the NIH (R01-HD30880, DK056350, R24 HD050924 and R01-HD38700) and the NIH Fogarty International Center (D43 TW009077, D43 TW007709) for financial support for the CHNS data collection and analysis files from 1989 to 2015 and future surveys, and the China-Japan Friendship Hospital, Ministry of Health for support for CHNS 2009, Chinese National Human Genome Center at Shanghai since 2009, and Beijing Municipal Center for Disease Prevention and Control since 2011. The authors also acknowledge support from China Scholarship Council. The role of the funders played no role in the design of the study, analysis or interpretation of the data, or preparation/review/approval of this manuscript. Conflicts of interest: The authors have no conflicts of interest to disclose. Authorship: Study concept and design: T.T. and C.W.L.; data analysis and interpretation: T.T. and C.W.L.; drafting of the manuscript: T.T.; critical revision of the manuscript: C.W.L. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Institutional Review Boards of the University of North Carolina, Chapel Hill and the Chinese Centre for Disease Control. Written informed consent was obtained from all subjects.
Supplementary material
For supplementary material accompanying this paper visit https://doi.org/10.1017/S1368980020005054
