J Nutr Health Aging

DOES LOW BODY MASS INDEX MATTER? RELATIONSHIP BETWEEN BODY MASS INDEX AND SUBJECTIVE WELL-BEING AMONG LONG-LIVED WOMEN OVER 95 YEARS OF AGE Z. LIU1, J. HUANG1, D. QIAN2, F. CHEN2, J. XU2, S. LI1, L. JIN1, X. WANG1 1. Unit of epidemiology, State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University 200433, Shanghai, China; 2. Longevity Research Institute of Rugao 226500, Jiangsu, China. Corresponding author: Xiaofeng Wang, School of Life Sciences, Fudan University, 220 Handan Rd., Shanghai 200433, China. Tel+86 21 65643714; Fax: +86 21 65642426; E-mail address: [email protected]

Abstract: Objectives: To examine the relationship between body mass index (BMI) and subjective well-being (SWB) among long-lived women over 95 years of age and evaluate whether this relationship is mediated by functional ability. Design: Retrospective cohort study. Setting: Data from the Rugao longevity cohort, a population-based study in Rugao, China. Participants: A sample of 342 long-lived women (mean age 97.4 ± 2.1, range 95–107) whose SWB and other covariates were available were included in this study. Measurements: BMI was calculated as weight in kilograms divided by height in meters-squared (kg/m2). SWB was measured by life satisfaction (LS), positive affect (PA), negative affect (NA) and affect balance (AB). Functional ability was assessed by the Katz Index of Activities of Daily Living (ADL). Results: According to BMI classification standards for China, the underweight group had lower levels of LS than the normal and overweight groups (28.62 vs. 30.51 and 31.57, respectively; p<.05). Correlation analysis showed that BMI was significantly related to LS (r = 0.166, p<.01). The strength of the BMI and LS association was diminished when ADL was included in the general linear regression models. Mediation analysis revealed that ADL mediated this relationship (effect size = 22.6%). We did not observe significant associations of BMI with other SWB components (PA, NA, and AB). Conclusion: For long-lived women, low BMI, rather than elevated BMI, is an indicator of poor psychological well-being. The findings call for public health awareness about low body weight in long-lived women, especially in those with physical disabilities when focusing on quality of life. Key words: Body mass index, subjective well-being, long-lived women, functional ability.

Introduction The relationship between body mass index (BMI)-based obesity and psychological factors (e.g., depression and positive affect [PA]) has been widely explored in general aged populations. Most studies have focused on the re-examination of the ‘jolly fat’ hypothesis suggested by Crisp (1), which posits that fat individuals jollier lean individuals (2-6). Subjective well-being (SWB), an important psychological concept proposed by Diener (7), has been widely accepted as an indicator of mental health and investigated across various populations (8). Specifically, SWB consists of cognitive and emotional facets. The cognitive facets include life satisfactory (LS) and domain-specific satisfaction (e.g., satisfaction with work). The emotional facets include PA and negative affect (NA), which were assumed to be distinct orthogonal dimensions (9, 10). Affect balance (AB), an index developed from PA and NA (PA minus NA plus a constant), is assumed to be a valid measure of well-being (11). Together, these components (LS, PA, NA and AB), solely or in combination, help evaluate well-being in psychological health research. Considering the predictive value of SWB on mortality (12, 13), identifying its related factors and developing intervention strategies are of importance for policy providers. In recent years, several studies have linked BMI to SWB or its components among general aged populations (14-18). Common to each of these studies was the relatively young

Received December 8, 2014 Accepted for publication January 26, 2015

1

age of the study subjects. However, little is known about this relationship in the fastest growing segment of the population, the oldest elderly (e.g., aged 85+ years). In this study, we focus on long-live individuals (LLIs) over 95 years of age, who represent a rare and highly selective segment of the general population (19). Other studies and our previous observations found that LLIs report high levels of well-being (20, 21). Nevertheless, it is well known that these individuals usually have a low BMI, partially due to the decrease of lean softtissue mass (22). With respect to a well-known Chinese idiom of ‘happy mind and fat body’, it is intriguing and important to examine the relationship between BMI and SWB in such a unique population for a better understanding of the ‘jolly fat’ hypothesis. Additionally, previous studies suggested that functional ability may play an important role in the relationship between BMI and mental health (14, 18, 23, 24). For example, older adults with poor physical functioning tend to report low levels of well-being (25). Functional limitations in older ages may influence how positive neighborhood perception affects BMI (24). Furthermore, Faith and colleagues suggested that results opposing the ‘jolly fat’ hypothesis might arise from inattentions to multiple covariates, which should be further explored rather than focusing on a simple association (26). Consequently, it is imperative to take into account functional ability when examining the relations between BMI and psychological factors.

J Nutr Health Aging

RELATIONSHIP BETWEEN BODY MASS INDEX AND SUBJECTIVE WELL-BEING Based on cross-sectional data from the Rugao longevity cohort, we examined the relationships between BMI and SWB components (LS, PA, NA, and AB; the computation of SWB is described later in this paper) among long-lived women and the effects of functional ability on these relationships. Considering the relatively high proportion of underweight subjects in this age group, it is of interest to address whether low BMI has a negative effect on SWB. We hope that this perspective of focusing on BMI in this special age group will provide clues for exploring the underlying mechanisms and, eventually, finding the means to improve their quality of life. Methods Study subjects Data of 342 long-lived women from the Rugao longevity cohort, a population-based association study conducted between Dec 24, 2007 and Feb 29, 2008 in Rugao, Jiangsu Province, China, were analyzed. The design of this cohort had been described in detail elsewhere (21, 27). Briefly, according to a strict four-step verification of age, 705 people over 95 years of age in Rugao (149 males and 556 females), including 102 centenarians (18 males and 84 females), were identified. Of these individuals, 463 individuals (360 women and 103 men) were recruited with a response rate of 71.6%. No significant difference in age and gender ratio was found between nonresponders and responders (all p>.05). Of the 463 LLIs, 342 females whose SWB and other covariates were available were included in this study (The negative results of men with only 100 subjects did not preclude the possibility of false negative, and thus were not provided in this paper). As previously mentioned (21, 27), the data were collected by trained field staff using a structured questionnaire, which included demographic characteristics, histories of chronic diseases (e.g., tuberculosis, chronic obstructive pulmonary disease, stroke, coronary heart disease, and malignant tumor), functional ability (assessed by the Katz Index of Activities of Daily Living [ADL]), mental health appraisals, etc. All interviews were tape-recorded, and 5% of the recorded interviews were evaluated for interviewing quality. Approximately 3–5% of the subjects were re-contacted by phone to evaluate the interviewers’ work. Written informed consent was obtained from each participant or a member of his/ her immediate family. The Human Ethnics Committee of Fudan University School of Life Sciences approved the research. Measures Body mass index (BMI) Weight and height were measured with participants in a fasting state by trained personnel using a stadiometer. BMI was calculated as weight in kilograms divided by height in meters-squared (kg/m 2). Individuals’ BMI was categorized as underweight (<18.5 kg/m 2 ), normal (18.5-23.9 kg/ 2

m2), overweight (24.0-27.9 kg/m2), or obese (≥28.0 kg/m2) according to the recommended classification standards for China (28). Overweight and obese were grouped together (≥24.0 kg/m2) due to the small sample size and non-significant differences. We also categorized BMI into 3 groups using tertile cutoff points: Tertile 1 (<19.8 kg/m2), Tertile 2 (19.822.4 kg/m2) and Tertile 3 (≥22.4 kg/m2). Functional ability Functional ability was assessed by the Katz Index of ADL, which was modified from the original scale used by Lawton (29). The Katz Index of ADL was based on six daily tasks (eating, dressing, bathing, indoor transferring, going to the toilet and cleaning oneself afterwards) and has been widely used in research to evaluate the physical functioning of older adults (30). Three response alternatives (strongly independent, somewhat dependent, and strongly dependent) were assigned to each task with a score of 1, 2, and 3 points, respectively; thus, the total scores ranged from 6 to 18, with higher scores indicating worse functional ability (ADL dependent). Subjective well-being (SWB) SWB, a new psychosocial factor proposed by Diener (7), includes cognitive and emotional facets. More specifically, the cognitive facet was assessed by the 20 items of the Life Satisfaction Index A (LSIA), and the emotional facet was assessed by the 10 items of Bradburn’s Affect Balance Scale (ABS). The descriptions of the two scales were provided in detail elsewhere (21). Briefly, LS scores measured by the LSIA scale ranged from 0 to 40; PA and NA scores measured by the ABS scale ranged from 0 to 5. In addition, the AB score is computed as PA minus NA plus a constant of 5 (to avoid negative values), resulting in a range from 0 to 10. The Cronbach’s alpha for the LSIA and ABS scale indicated a satisfactory internal consistency. Higher scores of LS, PA, NA, and AB indicated higher levels of LS, PA, NA, and AB, respectively. Covariates Covariates in this study, including age, marital status, education levels, smoking status, drinking status, history of chronic diseases, and perceived overall health status (fair, good, excellent) were collected from the structured questionnaire. Marital status was categorized as currently married or other. Education level was categorized as illiterate or literate. A participant was categorized as regular smoker (ever) if he/ she responded ‘Yes’ to the question ‘Have you ever smoked continuously for more than six months?’ or non-smoker if he/ she responded ‘No’. Defining drinking status is the same. The history of chronic diseases was coded as ‘none’ if a subject had none of the chronic diseases listed in the questionnaire and as ‘yes’ if a subject had one or more of the diseases.

J Nutr Health Aging

THE JOURNAL OF NUTRITION, HEALTH & AGING© Table 1 Descriptive characteristics of the study subjects according to BMI Diagnostic criteria in BMI Characteristics (n [%], or mean ± SD)

All (n = 342)

Underweight (<18.5) 68 (19.9)

212 (62.0)

62 (18.1)

-

125 (36.6)

108 (31.6)

109 (31.9)

-

97.4 ± 2.1

97.5 ± 2.2

97.4 ± 2.1

97.3 ± 2.1

.789

97.5 ± 2.0

97.2 ± 2.2

97.6 ± 2.3

.276

Number Age

Normal (18.5-24.0)

Tertile of BMI Overweight (≥24.0)

P Valuec

Tertile 1 (<19.8)

Tertile 2 (19.8-22.4)

Tertile 3 (≥22.4)

P Valuec

Currently married

9 (2.6)

0 (0)

6 (2.8)

3 (4.8)

.201

1 (0.8)

4 (3.7)

4 (3.7)

.225

Illiterate

316 (92.4)

60 (88.2)

198 (93.4)

58 (93.6)

.351

114 (91.2)

101 (93.5)

101 (92.7)

.795

Regular smoker (ever)

40 (11.7)

13 (19.1)

23 (10.9)

4 (6.5)

.066

21 (16.8)

12 (11.1)

7 (6.4)

.047*

Regular drinker (ever)a

114 (33.9)

29 (42.7)

63 (30.6)

22 (35.5)

.183

39 (31.5)

41 (39.1)

34 (31.8)

.409

BMI

21.4 ± 4.2

16.8±1.1

21.0±1.5

27.9±4.5

<.001***

17.9±1.4

20.9±0.8

25.9±4.1

<.001***

History of chronic diseases (None)

286 (83.6)

55 (80.9)

177 (83.5)

54 (87.1)

.631

103 (82.4)

88 (81.5)

95 (87.2)

.474

ADL

8.6 ± 3.5

9.6 ± 4.0

8.5 ± 3.4

8.0 ± 3.2

.045*

9.4 ± 3.9

8.2 ± 3.2

8.2 ± 3.2

.024*

92/185/65

25/33/10

55/114/43

12/38/12

.225

44/59/22

20/68/20

28/58/23

.057

30.33 ± 4.99

28.62±5.69

30.51±4.88

31.57±4.06

.006**

29.30±5.79

30.71±4.64

31.13±4.11

.056

Perceived overall health status Poor/good/excellent SWBb LS PA

3.85 ± 1.24

3.81±1.39

3.80±1.22

4.09±1.13

.183

3.78±1.42

3.82±1.13

3.96±1.14

.579

NA

1.02 ± 1.23

1.29±1.38

0.98±1.23

0.87±1.03

.135

1.18±1.36

0.84±1.10

1.02±1.19

.150

AB

7.83 ± 1.85

7.52±1.91

7.82±1.88

8.22±1.58

.074

7.61±2.11

7.98±1.70

7.94±1.64

.492

Notes: BMI: body mass index; ADL: Activities of Daily Living; SWB: subjective well-being; LS: life satisfaction; PA: positive affect; NA: negative affect; AB: affect balance; a. The number of the study subjects was 336; b. The range of scores was as follows: LS, 8–40; PA, 0–5; NA, 0–5; AB, 0–10; c. For comparisons of non-normal and categorical variables, the Kruskal-Wallis test and the Chi-square test were used, respectively; *p<.05. ** p<.01. ***p<.001.

Statistical analyses For descriptive statistics, the Kruskal-Wallis test (Tukey’s HSD was used in multiple comparisons), the Chi-square test or Fisher’s exact were used for the comparison of continuous variables and categorical variables between different BMI categories, respectively. Subsequently, bivariate correlation analysis was used between BMI and SWB components (LS, PA, NA and AB). In addition, to further examine the associations between BMI and SWB components, we performed general linear regression analysis to assess the partial correlation coefficients by means of ordinary least squares (OLS) evaluation. Finally, to test functional ability as a possible mediator in the relationship between BMI and SWB components, mediation analysis was performed. According to the casual steps approach (31), all variables were mean-centered (so that intercepts can be omitted in the model), and a significant association between the independent variable (i.e., BMI in this study) and the dependent variable (i.e., SWB components in this study) must be identified first. Next, the association between the independent variable and the potential mediator (i.e., ADL in this study) and the association between the mediator and the dependent variable after adjustment of the independent variable must be significant. Finally, the association between the independent variable and the

dependent variable when including the mediator must be reduced, which indicates a significant mediating effect. Simultaneously, an indirect effect and an effect size can be calculated from the coefficients of the above associations (30). All statistical analyses were completed using SAS software (version 9.3; SAS Institute, Cary, NC). A two-tailed p-value of .05 was considered statistically significant. Results The descriptive statistics (e.g., means and proportions) were presented in Table 1. The mean age of the 342 longlived women was 97.4 years with a range of 95-107 years. Approximately 19.9% (n = 68) and 18.1% (n = 62) of the study sample were categorized as underweight (BMI<18.5 kg/m2) and overweight (BMI ≥24.0 kg/m2), respectively. According to the recommended cutoff criteria for BMI, we performed the descriptive statistics of the study subjects by BMI categories. We found that the three BMI subgroups differed significantly on LS (underweight: 28.62; normal weight: 30.51; overweight: 31.57, p=.006). After the correction for multiple comparisons, we observed that subjects who were underweight had a significantly lower level of LS than the other two groups (p<.05, data not shown). We failed to observe significant differences in the levels of PA, NA and AB between 3

J Nutr Health Aging

RELATIONSHIP BETWEEN BODY MASS INDEX AND SUBJECTIVE WELL-BEING Table 2 The associations between BMI and SWB components among the study subjects by general liner regression analysis LS

Model 1

Model 2

Model 3a

Model 4a

Model 5a

PA

Coeff.

P Value

Coeff.

P Value

0.206

.002**

0.024

.136

0.200

0.197

0.147

0.132

.002**

.003** .017*

.027*

0.021

0.024

0.017

0.012

.193

.143

NA

Coeff.

P Value

-0.025

.123

-0.025

-0.021

.301

-0.016

.433

-0.014

.113

.181

.323

.379

AB

Coeff.

P Value

0.049

.043*

0.047

0.046

0.033

0.026

.053

.061

.170

.251

Notes: BMI: body mass index; ADL: Activities of Daily Living; SWB: subject well-being; LS: life satisfaction; PA: positive affect; NA: negative affect; AB: affect balance; Model 1: unadjusted; Model 2: adjusted for age, marital status, and education levels; Model 3: add smoking/drinking status to Model 2; Model 4: add history of chronic diseases and ADL to Model 3. Model 5: add perceived overall health status to Model 4; aThe number of the sample is 336; *p<. 05. ** p<.01.

Figure 1 Mediation analysis for the effects of BMI on life satisfaction (LS) via Activities of Daily Living (ADL) among longlived women. The association of BMI with LS was slightly diminished when ADL was included in the model. An indirect effect of 0.039 [=(-0.11) × (-0.35)] and an effect size of 0.226 [=(-0.11) × (-0.35)/0.17] were obtained.

the three BMI subgroups (p>.05). According to the tertile cutoff points of BMI, no difference was observed among the three tertiles (p=.056). After correction for multiple comparisons, the Tertile 1 subgroup had significantly lower level of LS than the other subgroups (p<.05, data not shown). We then examined the correlations between BMI and SWB components, and we found that BMI was positively related to LS (r = 0.166, p<.01, data not shown). No other significant correlation was observed (p>.05, data not shown). Subsequently, considering the potential factors associated with the relationship between BMI and SWB components, we performed general linear regression analysis of five models, and the results were presented in Table 2. Again, BMI was associated with LS (model 1, coeff. = 0.200, p=.002). After adjustment for factors, including age, marital status, etc., the significance of the association remained, but we observed a simultaneous lessening of the association when adjusting for the history of chronic diseases and ADL (Model 4). There were no other significant results observed (Table 2). Finally, we performed mediation analysis to examine ADL as a possible mediator in the relationship between BMI and LS. Figure 1 depicts the mediation model and the results obtained using causal steps (31). As expected, we found that BMI (the independent variable) was significantly associated with LS (the dependent variable); the regression coefficient was β = 0.17 (p=.002). Next, significant relationships between BMI and ADL (the mediator variable) (β = -0.11, p=.042) and between ADL and LS after adjustment for BMI (β = -0.35, p<.001) were observed. Meanwhile, we observed that the association of BMI with LS was slightly diminished when ADL was included in the model (β = 0.13, p = .011); this indicated a partial mediating effect. We obtained an indirect effect of 0.039 [=(-0.11) × (-0.35)] and an effect size of 0.226 [=(-0.11) × (-0.35)/0.17], indicating that the indirect effect contributed approximately 22.6% to the total effects in this relationship.

Discussion To our knowledge, this is the first population-based study to examine the relationship between BMI and SWB components (LS, PA, NA, and AB) among long-lived women over 95 year of age and could be considered unique in terms of the mediation analysis of functional ability. We found that higher BMI was associated with a high level of LS, indicating a negative impact of low BMI on psychological well-being. In addition, the association was mediated by functional ability (effect size = 22.6%). The findings call for public health awareness about low body weight in long-lived women with physical disabilities when promoting mental health. In previous studies, the relation between BMI and SWB had been investigated in general populations (14-18). Nevertheless, unique characteristics exist in the LLIs, one of which is low BMI, partially due to a decrease of lean soft-tissue mass with advancing age (22). It is well documented that for the LLIs, underweight (<18.5 kg/m2, 19.9% in this study), rather than obesity (≥30 kg/m2, 3.8% in this study), should be a key focus for public health providers. In addition, LLIs always reported 4

J Nutr Health Aging

THE JOURNAL OF NUTRITION, HEALTH & AGING© better mental states (20, 21). Our findings of the relation between BMI and LS in females, to some extent, support the so-called ‘jolly fat’ hypothesis, which states that some extra weight is associated with better mental health. However, more importantly, the opposite direction of the relationshiplower BMI is associated with low levels of LS-deserves attention, particularly in consideration of the high proportion of the underweight LLIs and the predictive role of low LS on mortality (32). We appeal to the public to direct more attention to low body weight among the long-lived women when focusing on their quality of life (33). It is notable that BMI had no association with PA, NA, or AB in this study, either in descriptive statistics or general linear regression analysis. Indeed, as emotional facets of SWB, PA and NA are assumed to be much more reactive to situational influences, whereas LS is proposed to reflect individuals’ evaluations of life circumstances over time and therefore has a high level of stability (34, 35). Interestingly, the AB index calculated from PA and NA confers a mental health advantage and was regarded as a valid measure of well-being (11). However, it shows inherent limitations in concept and content (36). From the perspective of accumulated effects of BMI on well-being, our results indicate that LS performed better than the components of emotional facets, including PA, NA, and AB. In sum, potential differences may exist between BMI and the emotional and cognitive facets of SWB, which call for further scrutiny. Mediation analysis in this study indicates that the observed relationship between BMI and LS in long-lived women is mediated by functional ability, which is consistent with the literature on other indicators of mental health in the oldest elderly (25). In fact, functional problems accumulate as age increases and, thus, are deservedly associated with psychological factors and quality of life (37, 38). In this study, our results suggested that functional ability explains approximately 22.6% of the effect of BMI on LS (indirect effect = 0.039). As far as we know, no study has evaluated the important mediating effect. Our finding, to some degree, also implies that more factors associated with mental health may exist, and mediation analysis should be considered when exploring these factors. In addition, our results indicate a possible explanation/mechanism of the identified relationship between BMI and LS, which would be discussed more below. Indeed, although BMI does not differentiate between muscle and fat tissue, higher BMI does not necessarily indicate an excess of fat tissue in the oldest elderly. On the contrary, higher BMI may indicate higher muscle mass, since sarcopenia, defined as the age-related loss of skeletal muscle mass (39), strength and function, also affects BMI. Obese is not considered here for its relation to high waist circumference (40) and the small proportion in this study. Thus, it is not surprising that no association between waist circumference and SWB components was observed in this study (data not shown). As a frail and vulnerable population, some LLIs are more susceptible

to illness and chronic diseases, although survivors or delayers of diseases account for the majority (41). An adequate amount of extra weight might provide protections against energy and nutritional deficiencies, metabolic stresses and loss of muscle and bone density caused by chronic diseases (42), and thus, may be essential for dealing with daily activities, e.g., movement, eating (43), eventually affecting long-lived women’ quality of life or mental health. Kuriyama et al. reported that a stratified analysis of chronic medical conditions showed an evident inverse association between BMI and depressive symptoms among women with chronic medical conditions (23). Similar findings were observed in the stratified analysis of this study (data not shown). Notable that sarcopenia, known to be associated with important health problems, is evidently involved in the explanation above. As no direct measurement was available in this study, again, we do emphasize the importance of avoiding low body weight among the long-lived women aforementioned in an attempt to improve their quality of life (44). All these provide possible explanations of the association of BMI with LS among long-lived women, namely, functional ability. Another explanation of the relationship between BMI and LS in long-lived women may lie in the Chinese culture (2, 3). The well-known idiom ‘happy mind and fat body’ (which means that people with increased life satisfaction may eat more, etc.) and the Chinese ‘doctrine of the mean’ had a great impact on the oldest elderly, especially the LLIs in Rugao, a town famous for longevity with harmonious social environment. For instance, filial respect, a traditional virtue, has been particularly emphasized in Rugao since ancient times (21). The traditional Chinese culture endorses an appropriate amount of excess weight, but not excessive fatness and obesity. Indeed, only 3.8% of long-lived women in our study had a BMI greater than 30 kg/m2, and they might be jollier than those who are thinner. An intriguing fact of culture influences is that as suggested by Li et al., the females may be more entrenched in traditional Chinese culture and values and thus, tend to put on more weight (3). This is also the reason why we focus on long-lived women in this study and we observed consistent findings with some results of other relevant studies on the association between obesity and mental health conducted in general aged populations (5, 23). In addition, we speculate that BMI may associate with mental health in long-lived women through other mediators, for example, social support and skills (45). Obviously, the assumptions need further exploring. The strengths of the present study include the populationbased approach utilized, the reasonably large sample size of long-lived women and the mediation analysis of functional ability. However, our study also had limitations that should be considered. First, the nature of cross-sectional data of this study limits causal inferences in interpreting the results. For example, a mediator, however, goes further conceptually, and would imply in this case that BMI causes changes in ADL in this study. It is certainly possible that some of the casual impact 5

J Nutr Health Aging

RELATIONSHIP BETWEEN BODY MASS INDEX AND SUBJECTIVE WELL-BEING among these variables would flow in the reverse direction, e.g., disability causes restriction in physical activity that leads to further loss of muscle mass that leads to lower BMI. Although the residual association between BMI and LS after adjusting for ADL indicated a partial mediating effect, the analysis of a mediator (in this case ADL) in cross-sectional models is essentially an extension of the analysis for a confounder, and longitudinal studies are particularly needed. Second, the evaluation of cognitive status and presence of chronic pain, which may represent important components of functional autonomy/subjective well-being, was not available in this study, which should be a serious limitation. Other potential confounding factors, e.g., social network (46), were not collected in our questionnaire. Third, although almost 60% of LLIs in Rugao participated in this study, the effect size is still limited by the relatively insufficient sample size, especially for long-lived men. The negative results of men with only 100 subjects do not preclude the possibility of false negative (data not shown). Hence, we suggest that the analyses of women represents a limitation of the study findings and implies the inability to directly apply these results to men. We really call for future studies could recruit more long-lived men for verifying our finding observed in this study. In conclusion, this study suggests that for long-lived women, low BMI, rather than elevated BMI, is an indicator of poor psychological well-being, and functional ability may mediate the association. The findings call for public health awareness about low body weight in long-lived women, especially in those with physical disabilities when focusing on quality of life. Further longitudinal studies are needed for validations of the preliminary findings.

7. 8. 9. 10. 11. 12.

13.

14. 15. 16. 17. 18.

19.

21. 22.

Acknowledgments: We acknowledge all people involved in this study, and especially, the long-lived individuals for participating in the study. This work was supported by a grant from National Science Fund for Distinguished Young Scholars (30625016), a grant from the Major Program of National Natural Science Foundation (30890034), a grant from Shanghai Municipal Health Bureau Fund for Distinguished Young Scholars (2006Y22), and a grant from National Science & Technology Support Program (2011BAI09B00). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

23. 24. 25. 26.

Conflict of interest: The authors declare no conflicts of interests.

27.

Ethical Standards: The study was approved by the Human Ethnics Committee of Fudan University School of Life Sciences.

28.

References

29.

1. 1. Crisp AH & McGuiness B. Jolly fat: relation between obesity and psychoneurosis in general population. British medical journal. 1976;1:7-9. 2. Dong Q, Liu JJ, Zheng RZ, et al. Obesity and depressive symptoms in the elderly: a survey in the rural area of Chizhou, Anhui province. International journal of geriatric psychiatry. 2013;28:227-232. 3. Li ZB, Ho SY, Chan WM, et al. Obesity and depressive symptoms in Chinese elderly. International journal of geriatric psychiatry. 2004;19:68-74. 4. Carpenter KM, Hasin DS, Allison DB & Faith MS. Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. American journal of public health. 2000;90:251-257. 5. Kim E, Song JH, Hwang JY, et al. Obesity and depressive symptoms in elderly Koreans: evidence for the «Jolly Fat» hypothesis from the Ansan Geriatric (AGE) Study. Archives of gerontology and geriatrics. 2010;51:231-234. 6. Roberts RE, Strawbridge WJ, Deleger S & Kaplan GA. Are the fat more jolly?

30. 31. 32. 33. 34.

6

Annals of behavioral medicine : a publication of the Society of Behavioral Medicine. 2002;24:169-180. Diener E. Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist. 2000;55:34-43. Diener E. New findings and future directions for subjective well-being research. The American psychologist. 2012;67:590-597. Baker LA, Cesa IL, Gatz M & Mellins C. Genetic and environmental influences on positive and negative affect: support for a two-factor theory. Psychology and aging. 1992;7:158-163. Bradburn NM. The structure of psychological well-being. Chicago: Aldine; 1969. Schuur W & Kruijtbosch M. Measuring subjective well-being: Unfolding the Bradburn Affect Balance Scale. Social Indicators Research. 1995;36:49-74. Wiest M, Schuz B, Webster N & Wurm S. Subjective well-being and mortality revisited: differential effects of cognitive and emotional facets of well-being on mortality. Health psychology : official journal of the Division of Health Psychology, American Psychological Association. 2011;30:728-735. Xu J & Roberts RE. The power of positive emotions: it’s a matter of life or death-subjective well-being and longevity over 28 years in a general population. Health psychology : official journal of the Division of Health Psychology, American Psychological Association. 2010;29:9-19. Pasco JA, Williams LJ, Jacka FN, Brennan SL & Berk M. Obesity and the relationship with positive and negative affect. The Australian and New Zealand journal of psychiatry. 2013;47:477-482. Linna MS, Kaprio J, Raevuori A, Sihvola E, Keski-Rahkonen A & Rissanen A. Body mass index and subjective well-being in young adults: a twin population study. BMC public health. 2013;13:231. Böckerman P, Johansson E, Saarni S & Saarni S. The Negative Association of Obesity with Subjective Well-Being: Is it All About Health? Journal of Happiness Studies. 2013:1-11. Carr D, Friedman MA & Jaffe K. Understanding the relationship between obesity and positive and negative affect: the role of psychosocial mechanisms. Body image. 2007;4:165-177. Jorm AF, Korten AE, Christensen H, Jacomb PA, Rodgers B & Parslow RA. Association of obesity with anxiety, depression and emotional well-being: a community survey. Australian and New Zealand journal of public health. 2003;27:434-440. Newman AB & Murabito JM. The Epidemiology of Longevity and Exceptional Survival. Epidemiologic reviews. 2013;35:181-197.20. Scheetz LT, Martin P & Poon LW. Do centenarians have higher levels of depression? Findings from the Georgia Centenarian Study. Journal of the American Geriatrics Society. 2012;60:238-242. Liu Z, Li L, Huang J, et al. Association between subjective well-being and exceptional longevity in a longevity town in China: a population-based study. Age. 2014; 36:9632. Horber FF, Gruber B, Thomi F, Jensen EX & Jaeger P. Effect of sex and age on bone mass, body composition and fuel metabolism in humans. Nutrition. 1997;13:524-534. Kuriyama S, Koizumi Y, Matsuda-Ohmori K, et al. Obesity and depressive symptoms in elderly Japanese: the Tsurugaya Project. Journal of psychosomatic research. 2006;60:229-235. Siordia C & Saenz J. Neighborhood Perception and Obesity in Aged Mexican Americans. The Journal of frailty & aging. 2012;1:152-161. Bowling A & Grundy E. Activities of daily living: changes in functional ability in three samples of elderly and very elderly people. Age and ageing. 1997;26:107-114. Faith MS, Matz PE & Jorge MA. Obesity-depression associations in the population. Journal of psychosomatic research. 2002;53:935-942. Cai XY, Wang XF, Li SL, et al. Association of mitochondrial DNA haplogroups with exceptional longevity in a Chinese population. PloS one. 2009;4:e6423. Zhou B & Coorperative Meta-Analysis Group Of China Obesity Task Force. Predictive values of body mass index and waist circumference to risk factors of related diseases in Chinese adult population. Zhonghua liu xing bing xue za zhi. 2002;23:5-10. (in Chinese) Lawton MP & Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. The Gerontologist. 1969;9:179-186. Katz S, Ford AB, Moskowitz RW, Jackson BA & Jaffe MW. Studies of illness in the aged: The index of adl: a standardized measure of biological and psychosocial function. JAMA. 1963;185:914-919. Baron RM & Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173-1182. Kimm H, Sull JW, Gombojav B, Yi SW & Ohrr H. Life satisfaction and mortality in elderly people: the Kangwha Cohort Study. BMC public health. 2012;12:54. Torma J, Winblad U, Saletti A & Cederholm T. Strategies to implement community guidelines on nutrition and their long-term clinical effects in nursing home residents. The journal of nutrition, health & aging. 2015;19:70-76. Larsen RJ & Prizmic Z. Regulation of emotional well-being: Overcoming the hedonic treadmill. In: Larsen MERJ, ed. The science of subjective well-being. New York, NY, US: Guilford Press; 2008:258-289.

J Nutr Health Aging

EMERGENCY DEPARTMENT READMISSION AND GERIATRIC INPATIENTS 35. Chow SM, Ram N, Boker SM, Fujita F & Clore G. Emotion as a thermostat: representing emotion regulation using a damped oscillator model. Emotion. 2005;5:208-225. 36. Schiaffino KM. Other measures of psychological well-being: The Affect Balance Scale (ABS), General Health Questionnaire (GHQ-12), Life Satisfaction Index-A (LSI-A), Rosenberg Self-Esteem Scale, Satisfaction with Life Scale (SWLS), and State-Trait Anxiety Index (STAI). Arthritis & Rheumatism. 2003;49:S165-S174. 37. Palmore EB, Nowlin JB & Wang HS. Predictors of function among the old-old: a 10-year follow-up. Journal of gerontology. 1985;40:244-250. 38. Hilleras PK, Jorm AF, Herlitz A & Winblad B. Activity patterns in very old people: a survey of cognitively intact subjects aged 90 years or older. Age and ageing. 1999;28:147-152. 39. Iannuzzi-Sucich M, Prestwood KM & Kenny AM. Prevalence of sarcopenia and predictors of skeletal muscle mass in healthy, older men and women. The journals of gerontology Series A, Biological sciences and medical sciences. 2002;57:M772-777. 40. Dong HJ, Marcusson J, Wressle E & Unosson M. Obese very old women have low relative hangrip strength, poor physical function, and difficulties in daily living. The journal of nutrition, health & aging. 2015;19:20-25.41. Evert J, Lawler E, Bogan H & Perls T. Morbidity profiles of centenarians: survivors, delayers, and escapers. The journals of gerontology Series A, Biological sciences and medical sciences.

2003;58:232-237. 42. Zheng H, Tumin D & Qian Z. Obesity and mortality risk: new findings from body mass index trajectories. American journal of epidemiology. 2013;178:1591-1599. 43. Yang M, Ding X, Luo L, Hao Q & Dong B. Disability associated with obesity, dynapenia and dynapenic-obesity in Chinese older adults. Journal of the American Medical Directors Association. 2014;15:150 e111-156. 44. Yu R, Wong M, Leung J, Lee J, Auyeung TW & Woo J. Incidence, reversibility, risk factors and the protective effect of high body mass index against sarcopenia in community-dwelling older Chinese adults. Geriatrics & gerontology international. 2014;14 Suppl 1:15-28. 45. Dierk JM, Conradt M, Rauh E, Schlumberger P, Hebebrand J & Rief W. What determines well-being in obesity? Associations with BMI, social skills, and social support. Journal of psychosomatic research. 2006;60:219-227. 46. Bowling A, Farquhar M, Grundy E & Formby J. Changes in life satisfaction over a two and a half year period among very elderly people living in London. Social science & medicine. 1993;36:641-655.

7