Open Access

Exploring the nonlinear relationship between body mass index and health-related quality of life among adults: a cross-sectional study in Shaanxi Province, China

  • Yongjian Xu1,
  • Zhongliang Zhou2,
  • Yanli Li2,
  • Jinjuan Yang1,
  • Xiaoyuan Guo1,
  • Jianmin Gao2Email author,
  • Ju’e Yan2 and
  • Gang Chen3
Health and Quality of Life Outcomes201513:153

https://doi.org/10.1186/s12955-015-0347-9

Received: 21 May 2015

Accepted: 12 September 2015

Published: 23 September 2015

Abstract

Introduction

China is a country facing the “double burden” of both obesity and underweight. The objective of this study was to explore the relationship between body mass index (BMI) and health-related quality of life (HRQOL) in adults from Shaanxi Province.

Methods

The data were derived from the fifth Health Service Survey of Shaanxi Province, which was part of China’s National Health Service Survey (NHSS), conducted in 2013. The HRQOL was assessed using the three-level EQ-5D questionnaire and scored based on a recently developed Chinese-specific tariff. Semiparametric regression models were adopted to explore the non-linear relationship between continuous BMI and overall HRQOL scores. Logistic regression models were further undertaken to assess the relationship between categorized BMI and five dimensions of HRQOL.

Results

Among the study sample (n = 37,902), 77 % of men and 75 % of women were assigned to normal weight, according to the WHO International classification. There were statistical significant nonlinear relationships between BMI and HRQOL, with optimal HRQOL achieved at a BMI of near 23 kg/m2 for men and 24 kg/m2 for women. Before BMI reached optimal HRQOL, the EQ-5D utility scores were increasing faster among men than the women, whilst after the BMI value reached the optimal utility scores, women showed a faster decline in utility scores than men. With adjustments for socio-demographic, physical activity and co-morbidities, obese respondents were more likely to suffer from physical rather than mental problems. Underweight respondents were significantly more likely to report having any problems in all five dimensions of the EQ-5D, whilst the magnitudes of odds ratios were consistently larger for men than women.

Conclusion

There was an inverse U-shaped association between continuous BMI and overall HRQOL scores, meaning that both underweight and obesity were associated with lower HRQOL. The relationship between BMI and HRQOL varied between sexes. Underweight respondents had a higher risk of suffering from both physical and mental problems. Interventions aimed to tackle the prevalence of underweight should be put into action in Shaanxi Province.

Keywords

Health-related quality of life Body mass index EQ-5D Underweight Obese

Introduction

Owing to numerous health risks and tremendous increases in prevalence, overweight and obesity has gained recognition as a major public health concern in most middle- and high-income countries [13]. According to the latest estimates by the World Health Organization (WHO), 38.5 % of men and 39.6 % of women aged ≥18 years in the world were overweight, and 10.7 % of men and 15.2 % of women were obese in 2014 [4]. Rates of obesity in the world have more than doubled since 1980 [4]. China is a country currently suffering from serious threat of obesity and overweight, particularly in urban adults. The prevalence of overweight and obesity aged 18 and over increased substantially between 2002 and 2014, from 21.8 to 35.4 % in China [5, 6].

Excessive body weight is largely responsible for the dramatic increase in the prevalence of various chronic diseases, such as cardiovascular diseases, type 2 diabetes, musculoskeletal diseases, and some significant cancers which cause premature death and substantial disability, and can cause large life expectancy reductions in the population [7, 8]. Although the prevalence of overweight and obesity has reached alarming proportions in China, the problem of underweight remains unresolved in some remote and mountainous areas. China is a country facing the double burden of overweight and underweight. Many health dangers, such as immune system diseases, osteoporosis and bone loss, and various pregnancy complications, are associated with being underweight [9, 10]. Being underweight is a serious and under-recognized problem in China.

Apart from higher morbidity and mortality rates, abnormal body mass index (BMI) affects the health-related quality of life (HRQOL) in many ways. HRQOL is regarded as a multidimensional and comprehensive construct that measures the perceived impact of health or disease on the physical, mental and social functioning [1114]. Previous studies revealed that obesity is associated with poorer physical functional HRQOL; however the impact of obesity on mental aspects of HRQOL is inconclusive [1517]. The relationship between underweight and HRQOL is also mixed. Some studies have suggested that underweight is negatively associated with both the physical and mental aspects of HRQOL, however, other studies indicate that the underweight is associated with impairment of physical aspects rather than mental aspects of HRQOL, and a few have even reported that there is no association between underweight and HRQOL [1820].

A few cross-sectional studies have assessed the relationship between BMI and HRQOL among adults in mainland China [21, 22]. These existing studies mostly focused on exploring the association between BMI categories and various dimensions of HRQOL. However, grouping BMI into pre-specified groups fails to reflect the nature of the relationship between BMI and HRQOL within BMI categories. This study aimed to investigate whether there is a nonlinear trend in the association between BMI and overall HRQOL using a semiparametric regression model, and to examine the relationship between BMI and five dimensions of HRQOL.

Methods

Data

The data used for analysis was derived from the fifth Health Service Survey of Shaanxi Province, which was part of China’s National Health Service Survey (NHSS), conducted in 2013. NHSS is a national representative survey commissioned by the Nation Health and Family Planning Commission of China. The detailed sampling and quality assurance measures have been described in a previously published paper [23]. In brief, a four-stage, stratified, random sampling scheme was used to ensure that samples are representative of the whole population of Shaanxi Province. In the first stage, 32 counties/districts were selected in Shaanxi Province. In the second stage, 160 townships were selected in sampled counties/districts. In the third stage, 320 villages/communities were selected in sampled townships. In the last stage, 20,700 households were identified. After verbal informed consent was obtained from interviewees, all members in each selected household were interviewed individually using a structured household questionnaire.

Among a total of 57,529 respondents, this study only focused on adult samples defined as those aged ≥18 years old (n = 47,151). Respondents whose HRQOL and/or BMI data were missing or not self-reported are further excluded in this analysis, leaving a study sample of 37,902 adult respondents.

Ethical considerations

Approval for the fifth NHSS was given by the National Bureau of Statistics of China (license number 2013(65)). Approval for this cross-sectional study was obtained from the Ethics Committee of Xi’an Jiaotong University Health Science Center (approval number 2015–401).

Variables

Body mass index

All respondents were asked to report their height and weight. BMI was calculated as the weight (in kilograms) divided by the square of the height (in meters) (kg/m2). In our study we explored the association between BMI and overall HRQOL scores by using continuous BMI, and assessed the association between BMI and various dimensions of HRQOL by grouping BMI into categories based on WHO International BMI classification criteria [24, 25]. Respondents were categorized into four groups: underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 25.0 kg/m2), overweight (25.0 ≤ BMI < 30.0 kg/m2), obese (BMI ≥ 30.0 kg/m2).

Health-related quality of life

The Chinese version EQ-5D-3 L, is preselected for the measurement of an individual’s HRQOL in the NHSS. The EQ-5D-3L consists of five dimensions: mobility (MO), self-care (SC), usual activity (UA), pain/discomfort (PD), and anxiety/depression (AD). Each dimension has 3 response levels, ranging from 1 (no health problems), 2 (moderate health problems), to 3 (extreme health problems). Overall, the EQ-5D-3L descriptive system defines a total of 243 unique health states. Although the EQ-5D-3L is the most widely used preference-based HRQOL measure in the world, the application of the EQ-5D-3L to measure HRQOL was restricted in China over the past few decades as the lack of Chinese population preference weights [26]. In this study, a recently developed Chinese-specific tariff (ranging from −0.149 for the worst health status, to 1 for the full health), based on the Chinese general population using the time trade-off method, has been adopted to score the EQ-5D-3L [27].

Other variables

Social-demographic characteristics considered in this study include sex, age, marital status (unmarried, married, divorced or widowed), education attainment (illiteracy, elementary, middle school, high school or university), residential areas (urban, rural), and economic status (grouped equally into the poorest, poorer, middle, richer, and the richest quintiles according to the per capita net household expenditure). Net household expenditure was calculated as total household expenditure in the last year minus household health expenditure [28]. In addition, physical activity (if respondents engaged in physical activities at least once a week in the last 6 months) and co-morbidities (i.e. a set of dummies indicating whether respondents had doctor diagnosed hypertension, diabetes, heart problems, musculoskeletal problems, respiratory disease or cancer) were also considered.

Statistical analyses

Mean and standard deviation or proportions were used to describe the characteristics of the study samples, where appropriate.

To explore the potential non-linear relationship between continuous BMI and overall HRQOL in general adults, semiparametric regression models were adopted for analysis in our study [29]. Semiparametric regression model is a flexible model which allows us to mix parametric terms with nonparametric terms in the same model, and takes the following form:
$$ HRQOL=\alpha +f(BMI)+{\beta}_1^{\hbox{'}}{X}_1+{\beta}_2^{\hbox{'}}{X}_2+\varepsilon $$

where HRQOL denotes the EQ-5D-3L utility score, BMI is the BMI value, f(.) indicates that BMI variable is assumed to have a nonlinear effect on HRQOL and is fitted with nonparametric smoothers, X1 is a vector that contains socio-demographic characteristics and physical activity, X2 is a vector that refers to co-morbidities, β1, β2 are unknown coefficients to be estimated, α is the intercept and ε is the error term. In Model 1, only vector X1 was controlled, whilst in Model 2, vector X2 was further controlled. Men and women were analyzed separately.

Given the ceiling effects of the EQ-5D-3L instrument in the general population, we re-categorized the three response levels into two categories (having no problem and having any problems), for each of the EQ-5D-3L dimensions [30]. Binary logistic regression models were undertaken to assess the association between categorized BMI and the presence of problems in five dimensions of EQ-5D. The odds ratios (ORs) and corresponding 95 % confidence intervals (95 % CIs) were reported. Data analyses were carried out using the Stata software (version 10.0), and R statistical software with the mgcv package.

Results

The characteristics of the study population are summarized in Table 1. A total of 37,902 adults, including 18,382 (48.50 %) men and 19,520 (51.50 %) women, participated in the study with the mean (standard deviation, SD) age of 50.01 (14.91) years and 48.71 (14.76) years in men and women, respectively. The mean (SD) BMI of respondents were 22.37 (2.82) kg/m2 and 21.98 (2.95) kg/m2 for men and women, respectively. According to the WHO International BMI cut-off, 7.20 %/10.92 % of men/women were underweight, 15.02 %/13.30 % of men/women were overweight, and 1.03 %/l.05 % of men/women were obese.
Table 1

Socio-demographic and clinical characteristics of the sample population

Characteristic

Men (n = 18,382)

Women (n = 19,520)

Age, years

  

 Mean (SD)

50.01 (14.91)

48.71 (14.76)

BMI, kg/m2

  

 Mean (SD)

22.37 (2.82)

21.98 (2.95)

EQ-5D utility score

  

 Mean (SD)

0.96 (0.11)

0.95 (0.12)

BMI WHO international categories

  

 Underweight, n(%)

1323 (7.20)

2132 (10.92)

 Normal, n(%)

14,108 (76.75)

14,587 (74.73)

 Overweight, n(%)

2761 (15.02)

2596 (13.30)

 Obese, n(%)

190 (1.03)

205 (1.05)

Marital status

  

 Unmarried, n(%)

1654 (9.00)

864 (4.43)

 Married, n(%)

15,560 (84.65)

16,782 (85.97)

 Divorced or widowed, n(%)

1168 (6.35)

1874 (9.60)

Educational attainment

  

 Illiteracy, n(%)

1841 (10.02)

4214 (21.59)

 Elementary, n(%)

4677 (25.44)

5210 (26.69)

Middle school, n(%)

7983 (43.43)

6964 (35.68)

 High school, n(%)

2806 (15.26)

2140 (10.96)

 University, n(%)

1075 (5.85)

992 (5.08)

Residential areas

  

 Urban, n(%)

5932 (32.27)

6839 (35.04)

 Rural, n(%)

12,450 (67.73)

12,681 (64.96)

Physical activity

  

 No, n(%)

14,487 (79.04)

14,677 (75.52)

 Yes, n(%)

3842 (20.96)

4757 (24.48)

Co-morbidities

  

 Hypertension, n(%)

2220 (12.08)

2903 (14.87)

 Diabetes, n(%)

434 (2.36)

502 (2.57)

 Heart problems, n(%)

359 (1.95)

500 (2.56)

 Respiratory problems, n(%)

302 (1.64)

231 (1.18)

 Musculoskeletal problems, n(%)

602 (3.27)

892 (4.57)

 Cancer, n(%)

18 (0.10)

29 (0.15)

BMI body mass index

WHO World Health Organization

SD standard deviation

Table 2 shows the proportion of respondents reporting any problems in each of five dimensions of EQ-5D by weight status (BMI). As can be seen, among the four BMI groups underweight men reported the highest proportion of having problems in all five dimensions of EQ-5D. For women being obese reported the highest proportion of having problems in three EQ-5D dimensions (mobility, usual activity and pain/discomfort dimensions), whilst being underweight was identified for the left two dimensions (self-care and anxiety/depression). Among five EQ-5D dimensions, consistent results from unhealthy weight respondents suggest that pain/discomfort is the most severely impacted dimension, whilst self-care is the least impacted dimension.
Table 2

The proportion of respondents having any problems in each dimension of the EQ-5D-3L (%)

 

Men

Women

Underweight

Normal weight

Overweight

Obese

Underweight

Normal weight

Overweight

Obese

MO

16.25

5.85

5.87

7.89

10.88

5.81

7.74

15.61

SC

8.62

3.13

2.57

2.11

6.19

3.13

4.20

5.85

UA

14.29

4.77

3.98

6.32

10.18

5.09

5.89

11.71

PD

27.59

12.50

10.65

14.21

21.95

14.36

19.07

28.29

AD

13.30

6.28

5.94

6.84

12.38

7.72

10.05

11.22

MO mobility, SC self-care, UA usual activity, PD pain/discomfort, AD anxiety/depression

Table 3 presents the estimated regression coefficients from semiparametric regression models for men and women, respectively. Among all the socio-demographic characteristics studied, age, marital status, educational attainment and economic status were all significantly associated with EQ-5D utility scores in both models. Being physically active was associated with better HRQOL. All co-morbidities were significantly associated with a decreased HRQOL (Model 2). Among them, respondents with cancer reported the largest decrement in EQ-5D utility score, with more than 0.176 and 0.157 point declines for men and women, respectively. Considering co-morbidities further improve the model fit that an adjusted R2 in Model 2 increased from 0.114 to 0.149 for men, and 0.141 to 0.188 for women.
Table 3

Estimated regression coefficients from the semiparametric models

 

Men

 

Women

Estimate

Std. Error

 

Estimate

Std. Error

Model 1 (Adj R2 = 0.114)

  

Model 1 (Adj R2 = 0.141)

  

Socio-demographic and physical activity

  

Socio-demographic and physical activity

  

Intercept

1.010**

0.00521

Intercept

1.005**

0.00610

BMI

Estimated with 6.753 edf, see left plot of Figure 1

BMI

Estimated with 6.791 edf, see left plot of Figure 2

Age

−0.0020**

0.00007

Age

−0.0021**

0.00007

Marital status

  

Marital status

  

 Unmarried(ref)

  

Unmarried(ref)

  

 Married

0.0249**

0.00308

Married

0.0192**

0.00436

 Divorced or widowed

−0.0026

0.00453

Divorced or widowed

−0.0154**

0.00541

Educational attainment

  

Educational attainment

  

 Illiteracy(ref)

  

Illiteracy(ref)

  

 Elementary

0.0141**

0.00304

Elementary

0.0283**

0.00248

 Middle school

0.0223**

0.00305

Middle school

0.0333**

0.00261

 High school

0.0241**

0.00354

High school

0.0342**

0.00349

 University

0.0248**

0.00471

University

0.0320**

0.00478

Residential areas

  

Residential areas

  

 Urban(ref)

  

Urban(ref)

  

 Rural

0.0001

0.00023

Rural

0.0002

0.00024

Economic status

  

Economic status

  

 Poorest(ref)

  

Poorest(ref)

  

 Poorer

0.0057*

0.00251

Poorer

0.0067*

0.00265

 Middle

0.0119**

0.00254

Middle

0.0107**

0.00265

 Richer

0.0093**

0.00261

Richer

0.0061*

0.00270

 Richest

0.0126**

0.00270

Richest

0.0066*

0.00278

Physical activity

  

Physical activity

  

 No(ref)

  

No(ref)

  

 Yes

0.0039*

0.00216

Yes

0.0130**

0.00207

Model 2 (Adj R2 = 0.149)

  

Model 2 (Adj R2 = 0.188)

  

Socio-demographic and physical activity

  

Socio-demographic and physical activity

  

Intercept

0.9979**

0.00519

Intercept

0.9975**

0.00603

BMI

Estimated with 6.693 edf, see right plot of Figure 1

BMI

Estimated with 6.750 edf,

see right plot of Figure 2

Age

−0.0016**

0.00007

Age

−0.0017**

0.00007

Marital status

  

Marital status

  

 Unmarried(ref)

  

Unmarried(ref)

  

 Married

0.0227**

0.00305

Married

0.0144**

0.00430

 Divorced or widowed

−0.0044

0.00448

Divorced or widowed

−0.0181**

0.00534

Educational attainment

  

Educational attainment

  

 Illiteracy(ref)

  

Illiteracy(ref)

  

 Elementary

0.0154**

0.00301

Elementary

0.0276**

0.00245

 Middle school

0.0238**

0.00302

Middle school

0.0320**

0.00257

 High school

0.0265**

0.00351

High school

0.0334**

0.00344

 University

0.0268**

0.00466

University

0.0308**

0.00471

Residential areas

  

Residential areas

  

 Urban(ref)

  

Urban(ref)

  

 Rural

−0.0001

0.00023

Rural

0.0001

0.00023

Economic status

  

Economic status

  

 Poorest(ref)

  

Poorest(ref)

  

 Poorer

0.0063*

0.00248

Poorer

0.0067*

0.00260

 Middle

0.0123**

0.00251

Middle

0.0104**

0.00261

 Richer

0.0096**

0.00258

Richer

0.0058*

0.00266

 Richest

0.0130**

0.00267

Richest

0.0061*

0.00274

Physical activity

  

Physical activity

  

 No(ref)

  

No(ref)

  

 Yes

0.0071**

0.00214

Yes

0.0147**

0.00205

Co-morbidities

  

Co-morbidities

  

 Hypertension

−0.0343**

0.00261

Hypertension

−0.0363**

0.00250

 Diabetes

−0.0387**

0.00534

Diabetes

−0.0253**

0.00523

 Heart problems

−0.0574**

0.00619

Heart problems

−0.0680**

0.00516

 Respiratory disease

−0.0588**

0.00626

Respiratory disease

−0.0340**

0.00752

 Musculoskeletal problems

−0.0821**

0.00441

Musculoskeletal problems

−0.0928**

0.00386

 Cancer

−0.1767**

0.02517

Cancer

−0.1577**

0.02098

BMI Body mass index

ref reference category

edf effective degrees of freedom

*P < 0.05; ** P < 0.01

The nonlinear relationship between BMI and HRQOL for men and women are plotted in Figs. 1 and 2, respectively. The left plot in both figures refers to the model (Model 1) controlling for socio-demographic characteristics and physical activity; the right plot in both figures refers to the model (Model 2) which further controls for co-morbidities. With adjustment for socio-demographic and physical activity factors only, curves in the left plot showed that these are nonlinear relationships between BMI and HRQOL. When further controlled for co-morbidities, the nonlinear relationship between BMI and HRQOL remains, with optimal HRQOL achieved at a BMI of near 23 kg/m2 for men and 24 kg/m2 for women. Along with the increasing BMI values, EQ-5D utility scores decreased, especially in women. Between the BMI values 24 and 35, the mean EQ-5D utility scores decreased around 0.06 points for women in Model 1, whilst utility scores decreased around 0.04 points for the same BMI range when further controlled for co-morbidities. On the other hand, being underweight is also associated with a lower EQ-5D utility score. After controlling for co-morbidities, for BMI values 24 to 15, the mean EQ-5D utility scores decreased around 0.08 points for men, but 0.05 points for women.
Fig. 1

Nonparametric estimates for semiparametric regression model of EQ-5D for men. Estimates in the left plot are adjusted for age, marital status, educational attainment, residential area, economic status and physical activity. Estimates in the right plot further adjusted for co-morbidities. The dashed lines represent the 95 % confidence intervals. 10 participants with highest and lowest BMI values are not displayed because of estimation uncertainly for outliners

Fig. 2

Nonparametric estimates for semiparametric regression model of EQ-5D for women. Estimates in the left plot are adjusted for age, marital status, educational attainment, residential area, economic status and physical activity. Estimates in the right plot further adjusted for co-morbidities. The dashed lines represent the 95 % confidence intervals. 7 participants with highest and lowest BMI values are not displayed because of estimation uncertainly for outliners

Table 4 presents the results of binary logistic regression analyses of the five dimensions of the EQ-5D. Being underweight significantly increased the risk of suffering from problems in all five dimensions of HRQOL for both men and women, whilst the magnitudes of ORs were consistently larger for men than women. Overweight and obese men were significantly more likely to suffer from mobility problems, whilst overweight and obese women were significantly increased the risk of suffering from mobility and pain/discomfort problems.
Table 4

Association between BMI and HRQOL from logistic regression models [OR (95 % confidence interval)]a

 

Men

Women

Underweight vs normal weight

Overweight vs normal weight

Obese vs normal weight

Underweight vs normal weight

Overweight vs normal weight

Obese vs normal weight

MO

2.47**(2.07–2.94)

1.24* (1.02–1.50)

1.76*(1.00–3.14)

1.37**(1.14–1.63)

1.30**(1.09–1.56)

2.03**(1.31–3.17)

SC

2.18**(1.74–2.75)

1.06(0.80–1.39)

0.85(0.30–2.37)

1.29**(1.03–1.61)

1.29(0.99–1.59)

1.14(0.59–2.20)

UA

2.51**(2.08–3.03)

1.03(0.82–1.29)

1.76(0.93–3.34)

1.45**(1.21–1.74)

1.09(0.89–1.33)

1.57 (0.96–2.57)

PD

2.17**(1.88–2.50)

0.92(0.79–1.06)

1.36(0.86–2.15)

1.39**(1.22–1.59)

1.23**(1.08–1.39)

1.49*(1.05–2.13)

AD

1.74**(1.45–2.09)

1.11(0.92–1.33)

1.19(0.65–2.16)

1.42**(1.22–1.66)

1.17 (0.99–1.35)

0.96(0.60–1.53)

BMI Body mass index

MO mobility, SC self-care, UA usual activity, PD pain/discomfort, AD anxiety/depression

OR odds ratio

*P < 0.05; **P < 0.01

aThe relationship between BMI and HRQOL was analysed by logistic regression, adjusted for age, marital status, educational attainment, residential area, economic status, physical activity and co-morbidities

Discussion

Using the world’s most widely used EQ-5D-3L instrument, this study estimated the relationship between continuous BMI and overall HRQOL scores among adults in Shaanxi Province by means of the semiparametric regression models. As expected, results revealed that the association between BMI and HRQOL is nonlinear (inverse U-shaped). More specifically, our study found that the EQ-5D-3L utility scores increased initially as BMI increased and achieved maximum at a BMI of around 23 kg/m2 for men and 24 kg/m2 for women, and then showed a decline trend with further increases of BMI. Before BMI reached optimal HRQOL, the EQ-5D utility scores were increasing faster among men than the women, whilst after the BMI value reached the optimal utility scores, women showed a faster decline in utility scores than men. The BMI value achieving optimal HRQOL in Shaanxi Province was lower than that value in the UK general population, which was estimated to be 24.5 in women and 27.5 in men [7].

Although this is the first study to explore whether there is nonlinear relationship between BMI and overall HRQOL scores in China using semiparametric regression models, earlier studies from other countries and specific populations provided evidence for nonlinear association between BMI and HRQOL. Soltoft et al. observed a nonlinear relationship between BMI and HRQOL in the general population of the UK [7]. Hunger et al. found the relationship between BMI and EQ-5D utilities were inverse U-shaped in individuals with type 2 diabetes [29]. After adjusting for covariates, Heo et al. observed J-shaped associations between BMI and HRQOL [31].

This study also explored whether the relationship between BMI and HRQOL could be mediated by co-morbidities. Our study found that regardless of whether adjusting for co-morbidities, the non-linear relationship is evident between HRQOL and BMI, although the magnitude of relationship decreased. This finding suggests unhealthy BMI is associated with decrements in HRQOL in nature, rather than this relationship just mediated through higher rates of co-morbidity. However, an exploratory study conducted in Singapore concluded that being obese no longer exhibited poor HRQOL after an adjustment for co-morbidities [18].

Previous studies revealed that being obese is associated with a lower physical HRQOL; however, the relationship between the obesity and mental domain of HRQOL are debated [3, 15, 21]. Consistent with many Asian studies, our study confirmed that, for both men and women, obesity negatively associated with physical rather than mental domain of HRQOL [18, 21]. Cultural and social difference could well explain why obese men were not associated with decrements in mental HRQOL. In Chinese traditional culture, excess weight is considered to be a symbol of happiness and wealthy for men [32]. The reason why obese women were not associated with decrements in mental HRQOL is that, although obese women may not satisfied with their fat body image, the mental health is not serious influenced by this weight-related distress. The relationship between BMI and physical domain of HRQOL varied between sexes. While obese women had higher risk of suffering from MO and PD problems, obese men just had higher risk of suffering from MO problem. The potential explanation is that women have higher pain sensitivity compared to men [33].

A lot of attention in the world is paid to dealing with the epidemic of overweight and obesity, however little attention is paid to assessing the relationship between underweight and HRQOL [34, 35]. Consistent with a previous study targeted at middle aged or older Chinese adults, our study found that underweight adults were significantly more likely to report having problems on all five EQ-5D-3L dimensions than those in the normal weight range [22]. In contrast to our study, another cross-sectional study, conducted in five major cities of China, reported that underweight respondents had similar physical and mental HRQOL compared to normal weight residents [21]. This discordant result may be due to sample selection limitations. Many studies in other Asian countries totally or partially supported our findings. One multiethnic study conducted in Singapore found that underweight reported the worst HRQOL compared to the pre-obese and obese [18]. Another study conducted in Japan found that being underweight was associated with impairment of physical aspects of HRQOL [19]. In view of the serious consequence of underweight (impairment of both physical and mental of HRQOL among adults in Shaanxi Province), interventions striving to tackle the prevalence of underweight should be put into action.

There are several strengths in this study. Firstly, the data we used were drawn from a large-scale cross-sectional survey conducted in Shaanxi Province. The large sample size of the Shaanxi’ NHSS gave our analysis excellent statistical power. Secondly, for the computing of EQ-5D utility scores, we used preference weights derived from the general Chinese population. Finally, an innovative statistical approach, semiparametric regression model, was used to explore the relationship between BMI and HRQOL without imposing any prior constraints of functional form.

There are some limitations that deserve consideration. Firstly, as the data we used was from a cross-sectional household survey, as such causal inferences about the relationship between BMI and HRQOL cannot be determined. Secondly, using the EQ-5D-3L instruments to assess the HRQOL has some limitations. As a generic preference-based instrument, EQ-5D-3L questionnaire may be less sensitive than an obesity-specific questionnaire to measure HRQOL. In addition, there is a notable ceiling effect for the EQ-5D-3L, particularly when it is used in the general population. However, a previous study has demonstrated that the EQ-5D-3L is not less sensitive to measuring HRQOL compared to the SF-6D, which is derived from the SF-36 [36]. Future studies could consider adopting the latest developed 5-level EQ-5D (EQ-5D-5L) to measure HRQOL. So far, the Chinese-specific tariff for EQ-5D-5L is unavailable. Thirdly, the weights and heights that were used to calculate BMI were self-reported. There is evidence suggesting that women tended to under-report their weight, whilst men tended to over-report their height [37]. Consequently, calculated BMI through self-reported data could be biased. However, self-reported height and weight were a major source of study on weight status in the large-scale household survey. In addition, many studies have also demonstrated that there is a strong correlation and high level of agreement between measured and self-reported BMI [3840].

Conclusion

The results from this study have demonstrated that there is a nonlinear relationship between BMI and HRQOL, regardless of whether controlling for co-morbidities. Consistent with most previous studies, our study found that obesity impaired the physical dimension rather than mental domain of EQ-5D. Underweight respondents had a high risk of suffering from both physical and mental problems. Interventions striving to tackle the prevalence of underweight should be put into action.

Declarations

Acknowledgements

The authors would like to thank all the respondents for their cooperation.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
School of Public Health, Health Science Center, Xi’an Jiaotong University
(2)
School of Public Policy and Administration, Xi’an Jiaotong University
(3)
School of Medicine, Flinders University

References

  1. Silventoinen K, Sans S, Tolonen H, Monterde D, Kuulasmaa K, Kesteloot H, et al. Trends in obesity and energy supply in the WHO MONICA Project. Int J Obesity. 2004;28(5):710–8.View ArticleGoogle Scholar
  2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81.View ArticlePubMedGoogle Scholar
  3. Huang IC, Frangakis C, Wu AW. The relationship of excess body weight and health-related quality of life: evidence from a population study in Taiwan. Int J Obes (Lond). 2006;30(8):1250–9.View ArticleGoogle Scholar
  4. WHO: http://www.who.int/mediacentre/factsheets/fs311/en/(2015). Accessed 2 Feb 2015.
  5. WHO: Global health observatory data repository. http://apps.who.int/gho/data/node.main.A897C?lang=en. Accessed 10 Feb 2015
  6. Wu Y. Overweight and obesity in China. BMJ. 2006;333(7564):362–3.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Soltoft F, Hammer M, Kragh N. The association of body mass index and health-related quality of life in the general population: data from the 2003 Health Survey of England. Qual Life Res. 2009;18(10):1293–9.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arterioscler Thromb Vasc Biol. 2006;26(5):968–76.View ArticlePubMedGoogle Scholar
  9. Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA. 2007;298(17):2028–37.View ArticlePubMedGoogle Scholar
  10. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. JAMA. 2005;293(15):1861–7.View ArticlePubMedGoogle Scholar
  11. Davalos ME, French MT. This recession is wearing me out! Health-related quality of life and economic downturns. J Ment Health Policy Econ. 2011;14(2):61–72.PubMedGoogle Scholar
  12. Riazi A, Shakoor S, Dundas I, Eiser C, McKenzie SA. Health-related quality of life in a clinical sample of obese children and adolescents. Health Qual Life Outcomes. 2010;8:134.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Chen G, Ratcliffe J, Olds T, Magarey A, Jones M, Leslie E. BMI, health behaviors, and quality of life in children and adolescents: a school-based study. Pediatrics. 2014;133(4):e868–74.View ArticlePubMedGoogle Scholar
  14. Rubin RR, Peyrot M. Quality of life and diabetes. Diabetes Metab Res Rev. 1999;15(3):205–18.View ArticlePubMedGoogle Scholar
  15. Larsson U, Karlsson J, Sullivan M. Impact of overweight and obesity on health-related quality of life--a Swedish population study. Int J Obes Relat Metab Disord. 2002;26(3):417–24.View ArticlePubMedGoogle Scholar
  16. Ul-Haq Z, Mackay DF, Fenwick E, Pell JP. Meta-analysis of the association between body mass index and health-related quality of life among children and adolescents, assessed using the pediatric quality of life inventory index. J Pediatr. 2013;162(2):280–6.View ArticlePubMedGoogle Scholar
  17. Renzaho A, Wooden M, Houng B. Associations between body mass index and health-related quality of life among Australian adults. Qual Life Res. 2010;19(4):515–20.View ArticlePubMedGoogle Scholar
  18. Wee HL, Cheung YB, Loke WC, Tan CB, Chow MH, Li SC, et al. The association of body mass index with health-related quality of life: an exploratory study in a multiethnic Asian population. Value Health. 2008;11 Suppl 1:S105–14.View ArticlePubMedGoogle Scholar
  19. Takahashi Y, Sakai M, Tokuda Y, Takahashi O, Ohde S, Nakayama T, et al. The relation between self-reported body weight and health-related quality of life: a cross-sectional study in Japan. J Public Health (Oxf). 2011;33(4):518–26.View ArticleGoogle Scholar
  20. Jia H, Lubetkin EI. The impact of obesity on health-related quality-of-life in the general adult US population. J Public Health (Oxf). 2005;27(2):156–64.View ArticleGoogle Scholar
  21. Wang R, Wu MJ, Ma XQ, Zhao YF, Yan XY, Gao QB, et al. Body mass index and health-related quality of life in adults: a population based study in five cities of China. Eur J Public Health. 2012;22(4):497–502.View ArticlePubMedGoogle Scholar
  22. Zhu YB, Luo XX, Wang Q. Study on the relationship between body mass index and health-related quality of life in middle-aged or older Chinese adults. Zhonghua Liu Xing Bing Xue Za Zhi. 2009;30(7):687–91.PubMedGoogle Scholar
  23. Xu Y, Gao J, Zhou Z, Xue Q, Yang J, Luo H, et al. Measurement and explanation of socioeconomic inequality in catastrophic health care expenditure: evidence from the rural areas of Shaanxi Province. BMC Health Serv Res. 2015;15:256.PubMed CentralView ArticlePubMedGoogle Scholar
  24. WHO: BMI classification. http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed 22 Jun 2015
  25. WHO. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO consultation on Obesity. Geneva: WHO; 2001.Google Scholar
  26. Chen G, Khan MA, Iezzi A, Ratcliffe J, Richardson J. Mapping between 6 Multiattribute Utility Instruments. Med Decis Making. 2015; doi:https://doi.org/10.1177/0272989X15578127.Google Scholar
  27. Liu GG, Wu H, Li M, Gao C, Luo N. Chinese time trade-off values for EQ-5D health states. Value Health. 2014;17(5):597–604.View ArticlePubMedGoogle Scholar
  28. Chen G, Yan X. Demand for voluntary basic medical insurance in urban China: panel evidence from the Urban Resident Basic Medical Insurance scheme. Health Policy Plan. 2012;27(8):658–68.View ArticlePubMedGoogle Scholar
  29. Hunger M, Schunk M, Meisinger C, Peters A, Holle R. Estimation of the relationship between body mass index and EQ-5D health utilities in individuals with type 2 diabetes: evidence from the population-based KORA studies. J Diabetes Complications. 2012;26(5):413–8.View ArticlePubMedGoogle Scholar
  30. Tan Z, Liang Y, Liu S, Cao W, Tu H, Guo L, et al. Health-related quality of life as measured with EQ-5D among populations with and without specific chronic conditions: a population-based survey in Shaanxi Province. China Plos One. 2013;8(7):e65958.View ArticlePubMedGoogle Scholar
  31. Heo M, Allison DB, Faith MS, Zhu S, Fontaine KR. Obesity and quality of life: mediating effects of pain and comorbidities. Obes Res. 2003;11(2):209–16.View ArticlePubMedGoogle Scholar
  32. Bin Li Z, Yin Ho S, Man Chan W, Sang Ho K, Pik Li M, Leung GM, et al. Obesity and depressive symptoms in Chinese elderly. Int J Geriatr Psych. 2004;19(1):68–74.View ArticleGoogle Scholar
  33. Wiesenfeld-Hallin Z. Sex differences in pain perception. Gender Med. 2005;2(3):137–45.View ArticleGoogle Scholar
  34. Hongo M, Miwa H, Kusano M. Symptoms and quality of life in underweight gastroesophageal reflux disease patients and therapeutic responses to proton pump inhibitors. J Gastroenterol Hepatol. 2012;27(5):913–8.View ArticlePubMedGoogle Scholar
  35. Suastika K, Dwipayana P, Saraswati MR, Gotera W, Budhiarta AA, Sutanegara ND, et al. Underweight is an important risk factor for coronary heart disease in the population of Ceningan Island. Bali Diab Vasc Dis Res. 2012;9(1):75–7.View ArticlePubMedGoogle Scholar
  36. Sach TH, Barton GR, Doherty M, Muir KR, Jenkinson C, Avery AJ. The relationship between body mass index and health-related quality of life: comparing the EQ-5D, EuroQol VAS and SF-6D. Int J Obes (Lond). 2007;31(1):189–96.View ArticleGoogle Scholar
  37. Krul AJ, Daanen HA, Choi H. Self-reported and measured weight, height and body mass index (BMI) in Italy, the Netherlands and North America. Eur J Public Health. 2011;21(4):414–9.View ArticlePubMedGoogle Scholar
  38. Dekkers JC, van Wier MF, Hendriksen IJ, Twisk JW, van Mechelen W. Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Med Res Methodol. 2008;8:69.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Lucca A, Moura EC. Validity and reliability of self-reported weight, height and body mass index from telephone interviews. Cad Saude Publica. 2010;26(1):110–22.View ArticlePubMedGoogle Scholar
  40. Lv S, Su JX, Quan Y, Wu M. Analysis on the knowing rates and accuracies of self-reported height, weight and waist circumstance data of adults. Jiangsu J Prev Med. 2012;23(5):13–5.Google Scholar

Copyright

© Xu et al. 2015

Advertisement