Association between Body Mass Index and Health-Related Quality of Life: The "Obesity Paradox" in 21,218 Adults of the Chinese General Population

Abstract

Background

There was no consistent recognition of the association between high or low body mass index (BMI) and health related quality of life (HRQL). The aim of this research was to study the association between BMI and HRQL in Chinese adults, and to further explore the stability of that association in the subgroup analysis stratified by status of chronic conditions.

Methods

A total of 21,218 adults aged 18 and older were classified as underweight, normal weight, overweight, class I obese, and class II obese based on their BMI. HRQL was measured by the SF-36 Health Survey. The independent impact of each BMI category on HRQL was examined through standard least squares regression by comparing the difference of SF-36 scores and the minimum clinically important differences (MCID), which was defined as 3 points.

Results

Compared to the normal weight, the class I obese was significantly associated with better HRQL scores in the mental component summary (MCS) (75.1 vs. 73.4, P<0.001). The underweight had the lowest score in both the physical components summary (PCS) (75.4 vs. 77.5, P<0.001) and mental components summary (MCS) (71.8 vs. 73.4, P<0.001). For the MCID, the HRQL score was reduced by more than 3 points in the physical functioning for the class II obese (D=-3.43) and the general health for the underweight (D=-3.71). Stratified analyses showed a similar result in the health subjects and chronic conditions, and it was significant in the chronic conditions.

Conclusions

The class I obese showed the best HRQL, especially in the mental domain. The worst HRQL was found in the underweight. The class II obese reduced HRQL in the physical functioning only. “Obesity paradox” was more obvious in the participants with chronic conditions.

About the Authors:

Yanbo Zhu

* E-mail: [email protected]

Affiliation: School of Management, Beijing University of Chinese Medicine, Beijing, China

Qi Wang

Affiliation: School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China

Guoming Pang

Affiliation: Internal Medicine Department, Kaifeng Hospital of Traditional Chinese Medicine, Kaifeng, Henan, China

Lin Lin

Affiliation: Department of Quality Management, China-Japan Friendship Hospital, Beijing, China

Hideki Origasa

Affiliation: Division of Biostatistics and Clinical Epidemiology, School of Medicine, University of Toyama, Toyama, Japan

Yangyang Wang

Affiliation: School of Management, Beijing University of Chinese Medicine, Beijing, China

Jie Di

Affiliation: School of Management, Beijing University of Chinese Medicine, Beijing, China

Mai Shi

Affiliation: Department of Clinical Nutrition, China-Japan Friendship Hospital, Beijing, China

Chunpok Fan

Affiliation: School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China

Huimei Shi

Affiliation: School of Management, Beijing University of Chinese Medicine, Beijing, China

Abstract

Background

There was no consistent recognition of the association between high or low body mass index (BMI) and health related quality of life (HRQL). The aim of this research was to study the association between BMI and HRQL in Chinese adults, and to further explore the stability of that association in the subgroup analysis stratified by status of chronic conditions.

Methods

A total of 21,218 adults aged 18 and older were classified as underweight, normal weight, overweight, class I obese, and class II obese based on their BMI. HRQL was measured by the SF-36 Health Survey. The independent impact of each BMI category on HRQL was examined through standard least squares regression by comparing the difference of SF-36 scores and the minimum clinically important differences (MCID), which was defined as 3 points.

Results

Compared to the normal weight, the class I obese was significantly associated with better HRQL scores in the mental component summary (MCS) (75.1 vs. 73.4, P<0.001). The underweight had the lowest score in both the physical components summary (PCS) (75.4 vs. 77.5, P<0.001) and mental components summary (MCS) (71.8 vs. 73.4, P<0.001). For the MCID, the HRQL score was reduced by more than 3 points in the physical functioning for the class II obese (D=-3.43) and the general health for the underweight (D=-3.71). Stratified analyses showed a similar result in the health subjects and chronic conditions, and it was significant in the chronic conditions.

Conclusions

The class I obese showed the best HRQL, especially in the mental domain. The worst HRQL was found in the underweight. The class II obese reduced HRQL in the physical functioning only. “Obesity paradox” was more obvious in the participants with chronic conditions.

Figures

Table 1

Table 2

Table 3

Table 1

Table 2

Table 3

Table 1

Table 2

Table 3

Citation: Zhu Y, Wang Q, Pang G, Lin L, Origasa H, Wang Y, et al. (2015) Association between Body Mass Index and Health-Related Quality of Life: The "Obesity Paradox" in 21,218 Adults of the Chinese General Population. PLoS ONE 10(6): e0130613. https://doi.org/10.1371/journal.pone.0130613

Academic Editor: Marta Letizia Hribal, University of Catanzaro Magna Graecia, ITALY

Received: June 27, 2014; Accepted: May 21, 2015; Published: June 18, 2015

Copyright: © 2015 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Funding: This work was supported by grants from the National Program on Key Basic Research Project of China (973 Program) (No. 2011CB505403;2005CB523501) and the National Natural Science Foundation of China (No. 30873256). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Background

Overweight and obesity have reached epidemic proportions throughout the world [1–4]. In China, 21.8 percent of adults are overweight or obese [5]. As a major contributor to a variety of chronic diseases [6–9], obesity has become one of the important global public health issues.

The association between obesity and a poorer health-related quality of life (HRQL) has been exploredpreviously. Kortt [10] conducted a study to investigate the correlation between HRQL and BMI in a representative sample of the Australian’s general population and found that BMI was negatively associated with the SF-6D questionnaire score. Another research conducted by Jia [11] showed that HRQL decreased with increasing levels of obesity. Compared with normal weight respondents, persons with severe obesity had significantly lower scores on the PCS-12, MCS-12, EQ-5D index, and EQ VAS, at 4.0, 1.1, 0.073, and 4.8 points lower, respectively. Persons with moderate obesity or who are overweight also had significantly poorer HRQL. McDonough et al. [12] provided further evidence for the association between increasing BMI and poorer HRQL in a mixed population of white European (WE) and South Asian (SA) ethnicity, whilst suggested that SA ethnicity modify this relationship. Oreopoulos [13] reported that BMI was inversely associated with the physical function and overall HRQL in coronary artery disease (CAD) patients, especially those with severe obesity.

However, some data hasaccumulated suggesting survival benefit or improved HRQL in elderly or patients with existing chronic diseases who are overweight and moderately obese[14–19], especially in the mental domain quality of life [20–24]. Some research presented underweight associated with poorer HRQL[25,26]. We had previously proved that, to the middle-aged and the old, higher BMI was associated with better mental HRQL, while the physical and mental components of the HRQL were poorer in the population that was underweight [27].

There was no consistent recognition of the association between high or low body mass index and health related quality of life. Thus, the objective of this research was 1) to study the association between BMI and HRQL in the general adult population in Chinese after adjusting for potential confounders, and 2) to further explore the stability of that association between BMI and HRQL subgroup by status of chronic conditions.

Methods

Data Sources

Data collected from a cross-sectional survey of 21,948 casesover-15s in China from December 2005 to January 2007, of nine provinces and municipalities (East: Beijing, Jiangsu, and Fujian; Central: Anhui, Jiangxi, Jilin, and Henan; West: Gansu and Qinghai) [27]. The subjects were selected by tri-phase stratified sampling. Firstly, the 9 provinces and municipalities of China were randomly selected by the SAS program. Secondly, communities, colleges and health examination centers were selected by convenience sampling. Thirdly, community residents, students and subjects were drawn out from each community, college and health examination center by systemic sampling. Quality control of the program was incorporated into the design, implementation and the data processing under the organization and supervision of the Beijing University of Chinese Medicine. All the study subjects signed a written informed consent. It was supported by the National Program on Key Basic Research Project of China (973 Program) in 2005, which was organized by the Ministry of Science and Technology of the People’s Republic of China.

Information of participants was collected by responding to standard questionnaire including gender, age, marital status, educational level, exercise habit, use of tobacco and alcohol, districts of residence, chronic conditions, weight, height, and HRQL. The chronic diseases included hypertension, hyperlipidemia, diabetes, cerebral apoplexy, cardiopathy (myocardial infarction, coronary artery disease), hepatopathy (fatty liver, alcoholic hepatitis, hepatocirrhosis), gastric ulcer, Cancer, osteoporosis, bronchial asthma, which were collected from the self-reported results by respondents.110 individuals (0.5 percent) with missing values distributed in the value of educational level, use of tobacco and alcohol, exercise habit and chronic conditions. The imputation model was supplied to handle the missing values, and 21,218 cases were included in this study after excluding persons under 18 years of age.

Measurement of HRQL

HRQL was measured using Chinese version 1.0 of the SF-36 Health Survey [28] developed in 1997. The SF-36 [29,30] is made up of 36 questions that correspond to eight dimensions, including physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), and role emotional(RE) and mental health (MH). All dimensions can be further classified into the physical domain (PF, RP, BP, and GH) and the mental domain (VT, SF, RE and MH). The score scale for each category assigns scores from 0 to 100, with 100 representing the best health status while0 representing the worst. The SF-36 scores can also be expressed as two summary measures, the physical component summary (PCS) and the mental component summary (MCS), which provides a measure of the overall effect of physical and mental impairment.

Categorization of BMI

In this study, the data of weight and height was self-reported by the participants. Body weight was classified by WHO guidelines for the Asian Pacific population(WHO/IASO/IOTF, 2000) [31] into five BMI (kg/m2)categories: underweight (<18.5), normal weight (18.5–22.9), overweight (23–24.9),class I obese (25–29.9) and class II obese (≥30.0).

Analysis of the Data

We have previously proved that age, gender, marital status, educational level, exercise habit, BMI and chronic diseases were the main predictive factors of HRQL in general population of China[32]. The use of tobacco [33,34], alcohol [35,36] or socio-economic condition [37,38] were associated factors on HRQL. Therefore, standard least squares regression (adjusted for gender, age, marital status, educational level, exercise habit, use of tobacco and alcohol, districts of residence, chronic conditions and BMI-chronic condition interaction) was used to estimate the independent impact of each BMI category on overall HRQL. We also verified that chronic disease was one of the first two predictive factors in HRQL [32]. Thus, the stratified model was run subgroup by chronic conditions to further explore the stability of that association.

Some studies have suggested that a difference of 3 to 5 points may be regarded as a minimum clinically important difference (MCID) [39,40]. In this study, we used Bonferonni correction for multiple comparisons and interpreted the results based on statistical significance (P<0.05* (1/4) = 0.0125≈0.01) as well as MCID defined as 3 points. All the statistical analyses were performed using JMP 10.0 (SAS Institute Inc.).

Results

Characteristics of the Subjects

The mean age of the 21,218 participants was 35.4 (SD 14.7) years, ranged from 18 to 92 years, 46.8% were male, 58.2% were married, 55.4%were college or above education level, and 27.5% had at least one chronic disease. Categorized by BMI, the proportions of the subjects were 11.4%, 55.8%, 17.1%, 14.5%, and 1.3% for the underweight, normal weight, overweight, class I obese and class II obese. The characteristics of the participants did not equally distribute among the BMI categories (P<0.001). For example, higher proportions of overweight and obesity were found in older males and people who had primary or below educational level, use of tobacco and alcohol, and one chronic disease. (Table 1).

[Figure omitted. See PDF.]

Table 1. Characteristics of subjects by BMI categories.

https://doi.org/10.1371/journal.pone.0130613.t001

Correlation between BMI and HRQL

Overall, the results showed the mean scores of HRQL increased with increasing body mass from the underweight to the class I obese in both the physical and the mental component summary. Compared to the normal weight group, the class I obese had significantly higher scores in the mental component summary (MCS) (75.1 vs. 73.4, P<0.001). In contrast, the underweight group had the lowest scores in both the physical component summary (75.4vs. 77.5, P<0.001) and the mental component summary (71.8 vs. 73.4, P<0.001).

For eight dimensions, compared to the normal weight, the class I obese had significantly higher scores in the role-physical, general health, vitality, social functioning, role-emotional and mental health dimensions, whilst the class II obese had significantly lower scores in the physical functioning dimension with more than 3-point difference. The underweight had significantly lower scores in six dimensions (except physical functioning and role-emotional) and there was more than 3 points in the general health dimension. The overweight had the similar scores with normal weight in eight dimensions (Table 2). There was a small but discernible pattern of correlation between BMI and HRQL.

[Figure omitted. See PDF.]

Table 2. The SF-36 subscale scores by BMI categories.

https://doi.org/10.1371/journal.pone.0130613.t002

Correlation of BMI and HRQL Stratified by Status of Chronic Conditions

In the group of healthy, the BMI was associated with increasing scores in both the physical and the mental component summary from the underweight to the class I obese. Compared to the normal weight group, the class I obese had significantly higher scores in the mental component summary (MCS) (77.3 vs. 75.8, P = 0.004), and the underweight group had significantly lower scores in both the physical component summary (79.9 vs. 81.6, P<0.001) and the mental component summary (74.6 vs. 75.8, P = 0.009). In the group of chronic conditions, the SF-36 scores increased with increasing BMI from the underweight to the class I obese in the physical component summary, whilst from the underweight to the class II obese, it was associated with increasing scores in the mental component summary. The underweight had more than 3-point difference and significantly lower scores in both the physical component summary (67.9 vs. 71.8, P<0.001) and mental component summary (67.0 vs. 70.5, P<0.001). This counterintuitive association of BMI and HRQL was similar in the healthy and those with chronic conditions, but it was significant in the participants with chronic conditions.

Moreover, in the eight dimensions, compared to the normal weight group, the SF-36scores of the healthy were reduced by 3 points or more in the bodily pain dimension of the class II obese, and in the general health dimension of the underweight group. For subjects with chronic conditions, the SF-36 scores of the class II obese reduced more than 3 points in the physical functioning dimension and increased more than 3 points in the social functioning dimension. HRQL of the underweight group was reduced more than 3 points in most of the dimensions, with a maximum reduction of 5.43points (Table 3).

[Figure omitted. See PDF.]

Table 3. The SF-36 subscale scores by BMI categories and status of chronic conditions.

https://doi.org/10.1371/journal.pone.0130613.t003

Discussion

The study evaluated the association between BMI and HRQL in Chinese adults. Potential confounders were adjusted including gender, age, marital status, educational level, exercise habit, use of tobacco and alcohol, districts of residence and chronic conditions. Overall, the class I obese had better HRQL in both the physical and the mental domains than the normal weight, especially in the mental well-being. The class II obese mainly had an adverse association in the physical functioning activities such as showering, dressing, and lifting heavy objects, but not all of the difference of SF-36 scores were significant in the physical and the mental dimensions. By contrast, the HRQL scores of the underweight were the lowest in both the physical and the mental component summary. This "obesity-HRQL paradox" phenomenon was more obvious in the participants with chronic conditions.

Generally considered overweight and obesity were negatively associated with worse outcome. Nevertheless, a new review of some research showed that overweight and moderately obese(mainly refers to the class I obese) elderly or patients who were overweight or obese lived longer and responded better to treatment, It's called the “obesity paradox”. Recent studies have shown the “obesity paradox” phenomenon still existed between BMI and HRQL. A cross-sectional study of 3,605 subjects conducted by López-García [20] to examine the correlation between body weight and HRQL in the population aged 60 and over in Spain found that, compared with normal weight subjects, obesity in fact correlated with higher HRQL on the SF-36 mental scales. Tsai [21] conducted a study to explore the impact of obesity in Taiwanese people, the result indicated that only the physical functioning dimension was significantly negatively associated with obesity, and was limited to class II obese (BMI≥30kg/m2). In a recent meta-analysis, Ul-Haq et al. [23] reported that different patterns were observed for the physical and mental HRQL. The physical HRQL significantly reduced in adults according to the degree of obesity. Compared to the normal weight the mental HRQL also reduced in adults with the most severe obesity, and the mild and moderate obesity had no difference in the mental HRQL, while the overweight significantly increased. Further evidence had been provided by Zhu [27] which intended to estimate the association between BMI and HRQL in middle-aged or older Chinese adults, and the observation showed that “obesity paradox” existed in middle-aged and older Chinese adults. Underweight subjects reported poorer HRQL in both the physical and the mental domain, while obese people had poorer physical function but a better mental health condition. Our results were consistent with these previous reports.

Our results also indicated that underweight group had the lowest HRQL scores in the physical and the mental domain, which were consistent with previous studies [26,41–43]. However, some studies emphasized the association between obesity and HRQL [10–13], and failed to include a comparative analysis of the underweight group relative to other body weight groups [44,45]. There was substantial percentage of underweight persons [22,46] in Asian countries, including China. In view of this, the neglected underweight persons need as much attention as obesity.

Strengths and Limitations

Most evidence of the “obesity paradox” presented in previous studies had sparked lively discussions on how to explain this mechanism [47–49] and what have to do with the correlation between BMI and mortality [16,17,19,50]. The “obesity paradox” may be explained by early detection of diseases and initiation of treatments [51–54], greater metabolic reserves [17,47], and a mistaken definition of obesity based on the BMI [55]. Ul-Haq et al.[56] explored the “healthy obesity” by using data from a Scotland-wide survey by comparing the HRQL across the BMI category of people in the presence and absence of metabolic comorbidity, and addressed the question that there was a significant interaction with metabolic comorbidity (p = 0.007). The present study has extended the “obesity paradox” to HRQL outcomes in the Chinese adults. Combining with this study, it could be concluded that the class I obese group had the best quality of life with previous studies of HRQL as an independent predictor of death in patients [57–60]. Our results suggested that HRQL might be the intervening variable between obesity and prognosis. It means that obesity not only affects the prognosis directly but also influences the prognosis indirectly through HRQL. This notion might be a new perspective for the research.

Obese elderly were less likely to suffer from depressive symptoms than those of normal weight; this was confirmed in a study from Hong Kong in China [61]. Our results also supported obesity had better HRQL in the mental component summary. In China, the lifetime prevalence of major depressive disorder (MDD)was 3.5% [62] and the 12-month MDD prevalence was 2% [63]. As our results of “obesity paradox”, the research on body mass management of depressive disorder Chinese is very important.

Many chronic diseases are common in south Asians who have a lower BMI. For many Asian populations, additional trigger points for public health action were identified as 23 kg/m2 or higher, representing increased risk, and 27.5 kg/m2 or higher, as high risk. We conducted our research using the BMI classification endorsed by the WHO guidelines for the Asian Pacific population so that our results were useful for the situation in Asian. The validity of the BMI as measurement of obesity has been questioned in recent researches on the subject. Litwin [64] suggested that waist-to-height ratio (WHtR) had a stronger gradient of correlation with the incidence of CVD than other indexes (BMI, %BF, WC, WHR). A cross-sectional study by Romero-Corral [55] showed that the accuracy of the BMI in diagnosing obesity was limited, particularly for individuals in the intermediate BMI ranges, men, and older persons. In the intermediate BMI range (25–29.9 kg/m2), this measurement failed to distinguish between %BF and lean mass in either gender. Still, some studies had shown that the Pearson correlation coefficient between the BMI and percentage of body fat (%BF) was as high as 0.7–0.8 [65,66] in both males and females. A systematic review and meta-analysis of studies that have assessed the use of the BMI to detect body adiposity have shown that the commonly used BMI cutoff values in diagnosing male and female obesity have high specificity [67]. A longitudinal study of 13,155 patients with cardiovascular disease (CVD) conducted by the Department of Human Performance and Sport Sciences at Winston-Salem State University found that the risk for mortality from CVD correlated with BMI, and the researchers also observed a similar pattern for %BF [68]. Therefore, further researches are needed in order to determine the best method for quantifying adiposity.

In population studies, the BMI is usually calculated on the basis of self-reported body weight and height. The self-reporting of anthropometrics has been known to be biased resulting in the misclassification of BMI status [69]. However, Dekkers [70] suggested that self-reported BMI was sufficiently accurate to assess the prevalence of overweight/obesity in the middle-aged overweight working population. In this study, we collected self-reported weight and height from the participants and we did not attempt to determine whether self-reporting had any effect on accuracy.

Conclusions

In this large population-based study, we found that the class II obese mainly had an adverse association on physical functioning, such as showering, dressing, and lifting heavy objects. The class I obese had the best HRQL in the physical domain and it also had the highest score in the mental domain. By contrast, the underweight group had the lowest HRQL scores in both the physical domain and the mental domain. This paradoxical association was significant in persons with chronic conditions.

Acknowledgments

This work was supported by grants from the National Program on Key Basic Research Project of China (973 Program) (NO.2011CB505403; 2005CB523501) and the National Natural Science Foundation of China (No. 30873256)

Author Contributions

Conceived and designed the experiments: YBZ. Performed the experiments: YBZ QW GMP. Analyzed the data: YBZ HO LL HMS. Contributed reagents/materials/analysis tools: YBZ. Wrote the paper: YBZ QW LL HO YYW JD MS CPF HMS.

Citation: Zhu Y, Wang Q, Pang G, Lin L, Origasa H, Wang Y, et al. (2015) Association between Body Mass Index and Health-Related Quality of Life: The "Obesity Paradox" in 21,218 Adults of the Chinese General Population. PLoS ONE 10(6): e0130613. https://doi.org/10.1371/journal.pone.0130613

References

1. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307(5):491–7. pmid:22253363.

2. Public Health Agency of Canada and the Canadian Institute for Health Information. Obesity in Canada: a joint report from the Public Health Agency of Canada and the Canadian Institute for Health Information; 2012. Available: http://www.phac-aspc.gc.ca/hp-ps/hl-mvs/oic-oac/assets/pdf/oic-oac-eng.pdf. Accessibility verified December 6.

3. National Health Service Information Centre. Health survey for England: 2010 trend tables; 2012. Available: http://www.ic.nhs.uk/pubs/hse10trends. Accessed April 9.

4. Ma GS, Li YP, Wu YF, Zhai FY, Cui ZH, Hu XQ, et al. The prevalence of body overweight and obesity and its changes among Chinese people during 1992 to 2002. Zhonghua Yu Fang Yi Xue Za zhi. 2005;39(5):311–5 pmid:16266539.

5. Wang Y, Mi J, Shan XY, Wang QJ, Ge KY. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. International journal of obesity (Lond). 2007;31(1):177–88. pmid:16652128.

6. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983;67(5):968–77 pmid:6219830.

7. Manson JE, Colditz GA, Stampfer MJ, Willett WC, Rosner B, Monson RR, et al. A prospective study of obesity and risk of coronary heart disease in women. The New England journal of medicine. 1990;322(13):882–9. pmid:2314422.

8. Boffetta P, McLerran D, Chen Y, Inoue M, Sinha R, He J, et al. Body mass index and diabetes in Asia: a cross-sectional pooled analysis of 900,000 individuals in the Asia cohort consortium. PLoS One. 2011;6(6):e19930. pmid:21731609.

9. Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nature reviews Cancer. 2004;4(8):579–91. pmid:15286738.

10. Kortt MA, Dollery B. Association between body mass index and health-related quality of life among an Australian sample. Clinical therapeutics. 2011;33(10):1466–74. pmid:21924772.

11. Jia H, Lubetkin EI. The impact of obesity on health-related quality-of-life in the general adult US population. Journal of public health (Oxford). 2005;27(2):156–64. pmid:15820993.

12. McDonough C, Dunkley AJ, Aujla N, Morris D, Davies MJ, Khunti K. The association between body mass index and health-related quality of life: influence of ethnicity on this relationship. Diabetes, obesity & metabolism. 2013;15(4):342–8. pmid:23137289.

13. Oreopoulos A, Padwal R, McAlister FA, Ezekowitz J, Sharma AM, Kalantar-Zadeh K, et al. Association between obesity and health-related quality of life in patients with coronary artery disease. International journal of obesity (Lond). 2010;34(9):1434–41. pmid:20386551.

14. Uretsky S, Messerli FH, Bangalore S, Champion A, Cooper-Dehoff RM, Zhou Q, et al. Obesity paradox in patients with hypertension and coronary artery disease. The American journal of medicine. 2007;120(10):863–70. pmid:17904457.

15. Curtis JP, Selter JG, Wang Y, Rathore SS, Jovin IS, Jadbabaie F, et al. The obesity paradox: body mass index and outcomes in patients with heart failure. Archives of internal medicine. 2005;165(1):55–61. pmid:15642875.

16. Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Allison TG,et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006;368(9536):666–78. pmid:16920472.

17. Hastie CE, Padmanabhan S, Slack R, Pell AC, Oldroyd KG, Flapan AD,et al. Obesity paradox in a cohort of 4880 consecutive patients undergoing percutaneous coronary intervention. European heart journal. 2010;31(2):222–6. pmid:19687163.

18. Kalantar-Zadeh k, kopple JD. Obesity paradox in patients on maintenance dialysis. Contributions to nephrology. 2006;151:57–69. pmid:16929133.

19. Carnethon MR, De Chavez PJ, Biggs ML, Lewis CE, Pankow JS, Bertoni AG, et al. Association of weight status with mortality in adults with incident diabetes. JAMA. 2012;308(6):581–90. pmid:22871870.

20. López-García E, Banegas Banegas JR, Gutiérrez-Fisac JL, Pérez-Regadera AG, Gañán LD, Rodríguez-Artalejo F. Relation between body weight and health-related quality of life among the elderly in Spain. International journal of obesity and related metabolic disorders. 2003;27(6):701–9. pmid:12833114.

21. Tsai WL, Yang CY, Lin SF, Fang FM. Impact of obesity on medical problems and quality of life in Taiwan. American journal of epidemiology. 2004;160(6):557–65. pmid:15353416.

22. 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. International journal of obesity(Lond). 2006;30(8):1250–9. pmid:16520814.

23. 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 adults, assessed by the SF-36. Obesity (Silver Spring). 2013;21(3):E322–7. pmid:23592685.

24. Diehr P, Thielke S, O'Meara E, Fitzpatrick AL, Newman A. Comparing years of healthy life, measured in 16 ways, for normal weight and overweight older adults. Journal of obesity. 2012;2012:894. pmid:22778920.

25. Dey M, Gmel G, Mohler-Kuo M. Body mass index and health-related quality of life among young Swiss men. BMC Public Health. 2013;13:1028. pmid:24172041.

26. 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 in Health. 2008;11 Suppl 1:S105–14. pmid:18387053.

27. 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 pmid:19957591.

28. Li L, Wang HM, Shen Y. Chinese SF-36 Health Survey: translation, cultural adaptation, validation, and normalization. Journal of epidemiology and community health. 2003;57(4):259–63 pmid:12646540.

29. Ware JE, Kosinski M, Keller SD. SF-36 Physical and Mental Health Summary Scales: A User's Manual. Boston, MA: The Health Institute, Health Assessment Lab, New England Medical Cente; 1994. https://doi.org/10.1007/s11136-010-9658-9 pmid:20440565

30. Ware JE, Shebourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical care. 1992;30(6):473–83 pmid:1593914.

31. World Health Organization, International Association for the Study of Obesity, International Obesity TaskForce. The Asia-Pacific Perspective: Redefining obesity and its treatment. Sydney: Health Communications; 2000.

32. Zhu YB, Wang Q, Chen KF, Luo XX, Tang F. Predictors of health-related quality of life in the general population. Chin J Behav Med & Brain Sci. 2009;18(3):254–259. (in Chinese) pmid:22161219

33. Lyons RS, Lo SV, Littlepage BNC. Perception of health among ever-smokers and never-smokers: a comparison using the SF-36 Health Survey Questionnaire. Tobacco Control. 1994, 3:213–5.

34. Tillman M, Silcock J. (1997) A comparison of smokers’ and ex-smokers' health-related quality of life. Journal of public health medicine. 1997;19(3):268–73 pmid:9347449.

35. Foster JH, Powell JE, Marshall EJ, Peters TJ. Quality of Life in alcohol–dependent subjects–a review. Quality of life research. 1999;8(3):255–61 pmid:10472156.

36. Anja Černe, Lijana Zaletel-Kragelj, Polona Selič. Quality of life and alcohol consumption: a review of the literature. Slovenian Journal of Public Health. 2012, 51(2):147–54

37. Thumboo J, Fong KY, Machin D, Chan SP, Soh CH, Leong KH, et al.Quality of life in an urban Asian population: the impact of ethnicity and socio-economic status. Social science & medicine. 2003;56(8):1761–72 pmid:12639592.

38. XIE XM, LI BF, YU GL, Li ZL. Life Quality and Related Factors of the Chronic Elderly Patients. J Chinese Mental Health. 2003;17(2):104–112. (in Chinese)

39. Lubeck DP. Patient-reported outcomes and their role in the assessment of rheumatoid arthritis. Pharmacoeconomics. 2004;22(2 Suppl 1):27–38 pmid:15157002.

40. Mease PJ, Revicki DA, Szechinski J, Greenwald M, Kivitz A, Barile-Fabris L, et al. Improved health-related quality of life for patients with active rheumatoid arthritis receiving rituximab: Results of the Dose-Ranging Assessment: International Clinical Evaluation of Rituximab in Rheumatoid Arthritis (DANCER) Trial. The Journal of rheumatology. 2008;35(1):20–30 pmid:18050385.

41. Søltoft 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. Quality of life research. 2009;18(10):1293–9. pmid:19813103.

42. Hopman WM, Berger C, Joseph L, Barr SI, Gao Y, Prior JC, et al. The association between body mass index and health-related quality of life: data from CaMos, a stratified population study. Quality of life research. 2007;16(10):1595–603. pmid:17957495.

43. 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. International journal of obesity (Lond). 2007;31(1):189–96. pmid:16682976.

44. Vasiljevic N, Ralevic S, Marinkovic J, Kocev N, Maksimovic M, Milosevic GS, et al. The assessment of health-related quality of life in relation to the body mass index value in the urban population of Belgrade. Health and quality of life outcomes. 2008;6:106. pmid:19040759.

45. Korhonen PE, Seppälä T, Järvenpää S, Kautiainen H. Body mass index and health-related quality of life in apparently healthy individuals. Quality of life research. 2014;23(1):67–74. pmid:23686578.

46. 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. European journal of public health. 2012;22(4):497–502. pmid:21705786.

47. Doehner W, Clark A, Anker SD. The obesity paradox: weighing the benefit. European heart journal. 2010;31(2):146–8. pmid:19734553.

48. Gielen S, Sandri M. The obesity paradox—a scientific artifact?International journal of cardiology. 2013;162(3):140–2. pmid:22385729.

49. Pacchioni A, Rossi A, Benfari G, Cicoira M, Pellegrini P, Bonapace S, et al. A higher body mass index is associated with reduced prevalence of unstable atherosclerotic plaque: A possible explanation of the obesity paradox. International journal of cardiology. 2013;168(3):2912–3. pmid:23608399.

50. Lainscak M, von Haehling S, Doehner W, Anker SD. The obesity paradox in chronic disease: facts and numbers. Journal of cachexia, sarcopenia and muscle. 2012;3(1):1–4. pmid:22450395.

51. Chang VW, Asch DA, Werner RM. Quality of care among obese patients. JAMA. 2010;303(13):1274–81. pmid:20371786.

52. Oreopoulos A, McAlister FA, Kalantar-Zadeh K, Padwal R, Ezekowitz JA, Sharma AM, et al. The relationship between body mass index, treatment, and mortality in patients with established coronary artery disease: a report from APPROACH. European heart journal. 2009;30(21):2584–92. pmid:19617221.

53. Schenkeveld L, Magro M, Oemrawsingh RM, Lenzen M, de Jaegere P, van Geuns RJ, et al. The influence of optimal medical treatment on the “obesity paradox,” body mass index and long-term mortality in patients treated with percutaneous coronary intervention: a prospective cohort study. BMJ Open. 2012;2:e000535. pmid:22327630.

54. Steinberg BA, Cannon CP, Hernandez AF, Pan W, Peterson ED, Fonarow GC. Medical therapies and invasive treatments for coronary artery disease by body mass: the “obesity paradox” in the Get With The Guidelines database.The American journal of cardiology. 2007;100(9):1331–5. pmid:17950785.

55. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. International journal of obesity (Lond). 2008;32(6):959–66. pmid:18283284.

56. Ul-Haq Z, Mackay DF, Fenwick E, Pell JP. Impact of metabolic comorbidity on the association between body mass index and health-related quality of life: a Scotland-wide cross-sectional study of 5,608 participants. BMC Public Health. 2012;12:143. pmid:22364437.

57. Rodríguez-Artalejo F, Guallar-Castillón P, Pascual CR, Otero CM, Montes AO, Garcia AN, et al. Health-related quality of life as a predictor of hospital readmission and death among patients with heart failure. Archives of internal medicine. 2005;165(11):1274–9. pmid:15956007.

58. Kato N, Kinugawa K, Seki S, Shiga T, Hatano M, Yao A, et al. Quality of life as an independent predictor for cardiac events and death in patients with heart failure. Circulation journal. 2011;75(7):1661–9 pmid:21532181.

59. Mapes DL, Lopes AA, Satayathum S, McCullough KP, Goodkin DA, Locatelli F, et al. Health-related quality of life as a predictor of mortality and hospitalization: the Dialysis Outcomes and Practice Patterns Study (DOPPS).Kidney international. 2003;64(1):339–49. pmid:12787427.

60. Fan VS, Curtis JR, Tu SP, McDonell MB, Fihn SD. Using quality of life to predict hospitalization and mortality in patients with obstructive lung diseases. Chest. 2002;122(2):429–36 pmid:12171813.

61. Li ZB, Ho SY, Chan WM, Ho KS, Li MP, Leung GM, et al. Obesity and depressive symptoms in Chinese elderly. International journal of geriatric psychiatry. 2004;19(1):68–74. pmid:14716701.

62. Lee S, Tsang A, Zhang MY, Huang YQ, He YL, Liu ZR, et al. Lifetime prevalence and inter-cohort variation in DSM-IV disorders in metropolitan China.Psychological medicine. 2007;37(1):61–71. pmid:17038208.

63. Shen YC, Zhang MY, Huang YQ, He YL, Liu ZR, Cheng H, et al. Twelve-month prevalence, severity, and unmet need for treatment of mental disorders in metropolitan China. Psychological medicine. 2006;36(2):257–67. pmid:16332281.

64. Litwin SE. Which measures of obesity best predict cardiovascular risk? Journal of the American College of Cardiology. 2008;52(8):616–9. pmid:18702963.

65. Schreiner PJ, Terry JG, Evans GW, Hinson WH, Crouse JR 3rd, Heiss G. Sex-specific associations of magnetic resonance imaging-derived intra-abdominal and subcutaneous fat areas with conventional anthropometric indices. The Atherosclerosis Risk in Communities Study. American journal of epidemiology. 1996;144(4):335–45 pmid:8712190.

66. Farrell SW, Fitzgerald SJ, McAuley PA, Barlow CE. Cardiorespiratory fitness, adiposity, and all-cause mortality in women. Medicine and science in sports and exercise. 2010;42(11):2006–12. pmid:20351588.

67. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. International journal of obesity (Lond). 2010;34(5):791–9. pmid:20125098.

68. McAuley PA, Sui X, Church TS, Hardin JW, Myers JN, Blair SN. The joint effects of cardiorespiratory fitness and adiposity on mortality risk in men with hypertension. American journal of hypertension. 2009;22(10):1062–9. pmid:19617881.

69. Dano P, Nielsen OV, Storgaard L. Partial reconstruction of intestinal continuity in the treatment of severe side effects following intestinal shunt operation for obesity. Scandinavian journal of gastroenterology. 1979;14(2):167–71 pmid:432538.

70. 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 medical research methodology. 2008;8:69. pmid:18957077.