Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Correlates of perceived health related quality of life in obese, overweight and normal weight older adults: an observational study

  • Cinzia Giuli1Email author,
  • Roberta Papa2,
  • Roberta Bevilacqua3,
  • Elisa Felici3,
  • Cristina Gagliardi4,
  • Fiorella Marcellini3,
  • Marco Boscaro5,
  • Marco De Robertis5,
  • Eugenio Mocchegiani6,
  • Emanuela Faloia5 and
  • Giacomo Tirabassi5
BMC Public Health201414:35

DOI: 10.1186/1471-2458-14-35

Received: 27 June 2013

Accepted: 9 January 2014

Published: 15 January 2014



Obesity is a complex multifactorial disease, which also has an impact on quality of life. The aim of this paper is to identify the correlates of perceived health related quality of life in obese, overweight and normal weight Italians older adults.


205 subjects at the age ≥ 60 yrs. were recruited into the Division of Endocrinology of the Polytechnic University of Marche Region, Ancona (Italy). A protocol of questionnaires was constructed for data collection, and included domains such as physical activity, quality of life, socio-psychological aspects. The association of the latter variables with SF-36 Health Survey physical component (PCS-36) were evaluated in the whole sample. Multiple linear regression models were used to assess the effect of independent variables on PCS-36 and the physical subscales of SF-36.


PCS-36 showed a lower score in the obese and overweight subjects than the normal weight group (post-hoc test, p < 0.001 and p < 0.05 respectively). Age, gender (male), Body Mass Index, years of education, Physical Activity Scale for the Elderly (PASE) total score, Hospital Anxiety and Depression Scale anxiety, Hospital Anxiety and Depression Scale depression, number of medications prescribed and number of diseases were included in the model. Negative and significant PCS-associated variables included depression (p = 0.009), BMI (p = 0.001), age in years (p = 0.007), whereas positive and significant PCS-associated independent variables were years of education (p = 0.022), physical activity (p = 0.026). BMI was negatively associated with all the physical subscales of SF-36 (p < 0.05).


Research funding should be invested in the study of the benefits accruing from reducing obesity in the elderly.


Obesity Older adults Health related quality of life


Obesity is considered an important health problem in many developed and developing countries. In 2008, overweight and obesity were estimated to afflict nearly 1.5 billion adults worldwide [1]. This phenomenon has been increasing rapidly in the last few decades in USA and many European countries, including Italy [2]. Indeed, some authors found that more than 50% of Italian adult and older men and about 1 of 3 women are overweight or obese [3].

Elevated morbidity and mortality in overweight and obese subjects is caused by an array of associated diseases which place a major public health burden on society [4], and include hypertension, coronary heart disease [5], type 2 diabetes [6], and cancer [7]. Further, these conditions are becoming increasingly prevalent in the elderly [8]. Metabolic and genetic factors among many others underpin this association, as well as obesity-related complications [9].

Indeed, some authors describe obesity as a complex multifactorial disease, which also has an impact on physical function and quality of life [10, 11].

A study concluded that persons with obesity had significantly lower HRQL than those who were normal weight and such lower scores were seen even for persons without chronic diseases known to be linked to obesity [12].

Moreover, some authors appointed that obese older adults reported impaired quality of life in comparison with normal-weight people. In particular, they evidenced worse results on physical functioning and physical well-being [13]. These results reinforce the importance of normal body weight in older age.

Psychological problems are a common feature of obesity [14]. A recent study identified the strong association between depression, obesity and disability among the middle-aged, particularly in women [15]. Other psychological disorders, such as anxiety are associated with changes in body weight [16].

Moreover, a mix of socioeconomic, demographic and lifestyle factors as well as individual attitudes contribute to the risk for disease and obesity in older adults [17].

Many studies report the association between obesity and worsening health related quality of life, in both sexes [18, 19].

Perceived health is a highly significant indicator in self-rated health status [20]. A study points out that in the domain of public health practice and research, perceived and self-rated health provide valuable insight into subjective health status thanks to the simplicity involved in gathering such data [21]. As such, this indicator is widely adopted and is acknowledged as a valid benchmark and independent predictor of mortality together with a variety of diseases and conditions, such as obesity [22].

Given this background, the aim of this paper is to analyze the correlates of health related quality of life (HRQL) in obese, overweight and normal weight Italians older adults.


Sample and recruitment

The subjects included in the study were selected from the patients attending the Division of Endocrinology, Department of Clinical and Molecular Sciences of Polytechnic University of Marche. The sampling method consisted in the selection of a consecutive series of subjects on the basis of specific inclusion/exclusion criteria, in a period of three years (January 2010–December 2012).

The study was designed to detect a mean difference between the 3 groups (normal weight, overweight and obese subjects) in the SF-36 Physical component summary score. Sample size was computed on the basis of preliminary results obtained from 30 subjects (10 each group) using G-Power version 3.1.3. Alpha level was placed at 0.05, while power was set at 90%. This produced a sample size of 144 patients (48 subjects each group).

Inclusion criterion was to be aged 60 years and over. Exclusion criteria were: 1) pituitary, thyroid, adrenal and gonad disorders not controlled by the ongoing therapy; 2) evident electrolyte disorders; 3) poorly controlled diabetes mellitus (glycated haemoglobin > 8%).

On the same day of enrolment, subjects were clinically and anamnestically assessed and, then, were asked to fill out the administered questionnaires.

A total of 433 subjects aged 60 years and over were screened: among these, 115 subjects did not give their consent to participate, while another 97 met the exclusion criteria. Sixteen subjects were excluded because inaccurately filled out the questionnaires (more than 50% of missing answers). Finally, 205 subject were included in the study.

The Ethics Committee of Polytechnic University of Marche approved the project and an informed consent was obtained from each individual in compliance with Italian legislation and the Helsinki Declaration.


Anthropometric measures and clinical data

A clinical evaluation and an extensive case-history assessment was performed for each patient, in order to collect clinical data relevant to our study, i.e. body Mass Index (BMI), waist and hip circumference, and to evaluate comorbidities and medication prescribed. Body Mass Index (BMI), waist and hip circumference were measured according to standard protocol: BMI was calculated as weight in kilograms divided by the square of the height in metres (kg/m2), and the World Health Organization classification of BMI for adult population was applied: normal weight, 18.5 ≤ BMI < 25.0 kg/m2; overweight, 25.0 ≤ BMI < 30.0 kg/m2; obesity, BMI ≥ 30.0 kg/m2[23].

Smoking habit was also asked, classifying the subjects as current smoker, ex-smoker or no smoker.

An aggregate score of the number of medical conditions, including cardiovascular, endocrine, metabolic, neurological and gastrointestinal diseases, was calculated for each respondent.

Socio-demographic characteristics

A protocol of questionnaires was constructed for the collection of socio-demographic data. Marital status (single, married, separated/divorced, widowed), kind of household (living alone/with other persons), educational level, years of education, and employment status (working/retired) are included within the scope of this paper.

Educational level was indicated as following:
  1. 1.

    Primary education, including subjects with primary school certificate and junior high school certificate, literate subjects but no school certificate;

  2. 2.

    Secondary education, including subjects with medium and high educational level, such as senior high school certificate;

  3. 3.

    Tertiary education, including subjects with high educational level, such as university degree.


Physical activity

The validated Italian version of Physical Activity Scale for the Elderly [24, 25] was used to assess the amount of physical activity. The PASE consists of questions on self-reported occupational, household, and leisure activities over a one-week period. For each activity, the frequency (No. of times per week) and the duration (No. of hours) were asked. Total scores were calculated by multiplying the time spent on that activity for a specific weight and then adding up all the scores thereby obtaining a range of 0-400.

Quality of life

The Health-Related Quality of Life Scale (Short Form 36-item Health Survey) was used to assess quality of life [26]. The SF-36 measures diverse attributes of functional health status: physical functioning, role limitations due to poor physical health problems, bodily pain, general health, vitality (energy and fatigue), social functioning, role limitations arising from emotional problems and mental health (psychological distress and well-being). For each dimension, item scores are coded, summed, and transformed on to a scale from 0 (worst health) to 100 (best health) [27]. In addition, the SF-36 assesses overall physical and mental function using summary scales which include the Physical Component Summary Score (PCS-36) and Mental Component Summary Score (MCS-36). This instrument is reliable, valid and suitable for elderly people [28].

Anxiety and depression

The Hospital Anxiety and Depression Scale (HADS) [29] was used to assess levels of anxiety and depression. This instrument comprises two 7-item scales, one to evaluate anxiety and the other to assess depression. For each statement, the patient was asked to indicate which of four possible options best described his/her emotional state. The normative data classify scores less than or equal to 7 as normal, from 8 to 10 as borderline bases (situations that could potentially degenerate into anxiety or depression), and lastly scores above 11 indicating clinically relevant anxiety or depression as. The questionnaire is generally self-administered by the patient. The Italian version of this instrument is reliable, valid and suitable also for elderly people [30].

Social support

Social networks and informal social support were measured using the Lubben Social Network Scale (LSNS) which was specifically developed for use in older adults in both research and clinical settings (hospitals, nursing homes, clinics, day hospitals) [31]. The scale assesses the extent of social contact with family and friends. The total score is the sum of the items and ranges from 0 to 60, where high scores indicate good informal social support. This scale was previously used in another analysis to study the relationship between social support and quality of life in elderly [32].

Statistical analysis

Normality distribution of data was verified with the Kolmogorov-Smirnov test. Continuous data are expressed as mean ± SD, while categorical data as percentages. A descriptive analysis was performed, to evaluate the distribution of the variables in the three BMI classes (normal weight, overweight, obesity). Comparisons of mean values among three groups were made by one-way ANOVA followed by Bonferroni post-hoc test, while frequencies were compared using chi-square test. Pearson’s analysis was used to assess correlations among PCS-36 and MCS-36 and continuous variables in the total sample. ANOVA or t-test was used to compare PCS-36 mean values among categorical variables.

Lastly, the variables significantly associated with Physical component of SF-36 were included in different multiple linear regression models using PCS-36 and the physical subscales (Physical functioning, Role-physical, Bodily pain, General health) as dependent variables. All independent variables were included simultaneously in the regression model (Enter method). Independence of residuals and multicollinearity were verified by Durbin Watson and Variance Inflation Factor (VIF) statistic, respectively.

A value of p < 0.05 was accepted as statistically significant. Analyses were carried out using SPSS 16.0 statistical software for Windows (SPSS; Chicago, IL, 2002).


Sample characteristics

Participants were predominantly female (73.6%) aged on average 68.7 ± 6.4 years (mean ± SD). Table 1 shows the main psychosocial and lifestyle characteristics of the subjects.
Table 1

Characteristics of the participants by BMI classes


Normal weight




n = 86

n = 53

n = 66

Age (years)

69.02 ± 6.61

69.46 ± 5.69

67.76 ± 6.59















Waist circumference (cm)

82.99 ± 9.32

92.69 ± 8.36a

111.22 ± 12.47ab


Hip circumference (cm)

97.16 ± 6.49

106.17 ± 6.48a

118.85 ± 12.15ab


Number of medications prescribed

2.09 ± 1.85

2.36 ± 1.6

2.63 ± 1.97


Number of diseases

1.76 ± 1.39

1.77 ± 1

2.22 ± 1.58


Cardiovascolar diseases (yes)





Endocrine and metabolic diseases (yes)





Neurological and psychiatric diseases (yes)





Gastrointestinal diseases (yes)





Smoking habits



 Not smoker















Marital status























Living alone (yes)





Level of education



 No education




















PASE (total score)

111.91 ± 50.39

114.94 ± 46.1

102.3 ± 55.37


Self-evaluation of health status



 Excellent/very good















SF-36 Physical functioning

73.66 ± 27.76

68.1 ± 21.73

56.41 ± 26.75a


SF-36 Role-physical

64.51 ± 39.9

49.0 ± 41.33

48.05 ± 42.56


SF-36 Bodily pain

63.17 ± 24.18

50.64 ± 25.37c

49.12 ± 23.39d


SF-36 General health

54.83 ± 19.15

48.96 ± 20.93

45.25 ± 18.64c


SF-36 Vitality

55.71 ± 17.53

50.40 ± 23.88

47.38 ± 18.07c


SF-36 Social functioning

70.43 ± 20.37

66.50 ± 28.06

62.69 ± 22.48


SF-36 Role-emotional

71.54 ± 37.81

65.33 ± 42.57

54.69 ± 42.98c


SF-36 Mental health

63.0 ± 16.23

62.40 ± 22.74

57.90 ± 19.30


SF-36 Physical component summary PCS-36

44.57 ± 10.75

39.75 ± 8.96c

38.19 ± 9.07a


SF-36 Mental component summary- MCS-36

45.86 ± 9.03

45.33 ± 12.78

43.28 ± 10.13


LUBBEN SCALE (total score)

31.83 ± 9.08

33.16 ± 9.51

31.09 ± 10.62


HADS anxiety (total score)

6.34 ± 3.52

6.37 ± 4.05

7.22 ± 3.32


HADS depression (total score)

6.68 ± 3.7

7.45 ± 4.39

7.73 ± 3.85


`Note: Continuous variables are expressed as mean ± standard deviation; categorical data as percentage. Chi-square test or one-way ANOVA as appropriate; Bonferroni post-hoc test following ANOVA:

ap < 0.001 vs Normal weight.

bp < 0.001 vs Overweight.

cp < 0.05 vs Normal weight.

dp < 0.01 vs Normal weight.

42% of the subjects were in the normal weight range (BMI 18.5-24.9 Kg/m2), while 26% were overweight (BMI 25-29.9 Kg/m2) and 32% were obese (BMI > 29.9 Kg/m2). Among the groups, the percentage of smokers was higher in obese subjects (9.1%), though not significantly so. The majority of the subjects were retirees (82.5%), and as regards education, 61.6% had received primary schooling and 27.3% secondary schooling, with no significant differences among the groups. Even though there was no significant difference in the levels, the average anxiety and depression scores for the obese subjects were higher than in the other two groups.

Self-rated health status differed considerably among the three groups, as shown in Table 1.

Physical Component of SF-36 showed a lower score in the obese and overweight subjects than the normal weight group (post-hoc test, p < 0.001 and p < 0.05 respectively), while there were no significant differences for the Mental component (MCS-36). Additionally, the obese subjects also displayed lower scores for some of the SF-36 sub-scales, such as Physical functioning, Bodily pain, General Health, Vitality and Role-emotional.

Correlation and multiple linear regression analysis

A correlation analysis was then performed to evaluate the variables associated with PCS-36 and MCS-36 (Table 2). MCS-36 was found to be not associated with BMI. For this reason, only PCS-36 was considered for further analysis. Categorical variables such as marital status, smoking habits, kind of household and presence of diseases did not show significant differences in PCS-36 score (data not shown).
Table 2

Pearson’s correlations between PCS-36 and MCS-36 and continuous variables













Waist circumference (cm)


< 0.001



Hip circumference (cm)


< 0.001



Age in years





Years of education


< 0.001



PASE total score





LUBBEN SCALE (total score)





HADS anxiety


< 0.001


< 0.001

HADS depression


< 0.001


< 0.001

N. of medications





N. of diseases





The following variables, significantly correlated with PCS-36, together with gender (male), were included in a multiple linear regression model as independent variables: age, BMI, years of education, PASE total score (physical activity), HADS anxiety, HADS depression, number of medications prescribed and number of diseases. The model was significant (F test = 8.840, p < 0.001) and produced a R-square of 0.291 (Table 3). Independent variables negatively and significantly associated with PCS-36 in the model were depression (p = 0.009), BMI (p = 0.001), age in years (p = 0.007). Conversely, the ones positively and significantly associated with PCS-36 were years of education (p = 0.022) and physical activity (p = 0.026).
Table 3

Multiple Linear Regression Model on PCS-36 (Physical component summary)





95% confidence interval






52.337; 92.182


Age in years




−0.534; −0.084


Gender (male)




−1.829; −4.624






−0.581; −0.155


Years of education




0.059; 0.731


PASE total score




0.004; 0.058


HADS anxiety




−0.824; 0.148


HADS depression




−1.040; −0.149


N. of medications




−1.906; 0.451


N. of diseases




−1.107; 2.004


Note: dependent variable: PCS-36; R2 = 0.291; F test = 8.840, p < 0.001.

The same model was calculated using each time one of the four physical component subscales (Physical functioning, Role-physical, Bodily pain, General health) as dependent variable. BMI was negatively associated with all the subscales (p < 0.05). Both anxiety and depression were negatively associated with Bodily pain and General Health subscales (p < 0.001), while physical activity was significant only for Physical functioning (p = 0.042). The number of medications was found negatively associated with Role-physical subscale (p = 0.01).


The aim of this paper was to analyse the correlates of health related quality of life (HRQL) in obese, overweight and normal weight Italians older adults. Moreover, the relationship among obesity and psychological, socio-demographic aspects was identified. Differences between the obese group and the other two groups emerged.

As our results show, the waist and hip circumferences of obese subjects were significantly higher than those in the other two groups. Unsurprisingly, the prevalence of cardiovascular diseases was significantly higher in obese subjects, in accordance with other studies [33].

Self-evaluation of perceived health status differed among the groups. Indeed, the obese group “fair/poor” rating was significantly higher than that of their overweight and normal weight counterparts. This result is consistent with other studies on elderly people in different countries [34]. Moreover, a comparison of normative data on a representative Italian population [28] demonstrates that the evaluation of the SF-36 scores obtained from our obese sample is lower, and probably consistent with the presence of pathologies linked to obesity which could impair quality of life.

Consistent with another Italian study [35], the negative impact on quality of life was observed in domains reflecting physical status, with no significant impairment in mental health. In particular, physical functioning, bodily pain, general health, vitality, role-emotional are the components of SF-36 which differed significantly among the groups.

One interesting result regards the correlation analysis done to evaluate the variables associated with PCS-36 in the total sample. We identified independent variables associated with physical health of the quality of life component (PCS-36).

Results showed that age, BMI, educational level, physical activity, depression, were significant correlates for quality of life. In this context, the role of socio-economic differences in perceived health status have been well documented [36]. Some authors identified significant differences in HRQOL by socio-demographic characteristics and behavioural risk factors, with both lower scores reported by females and less educated subjects [37]. Previous studies indicated that individuals with lower education have a poorer self perceived health status, due to several factors [38]. A possible reason may be cultural differences in values and reference levels, rather than true differences in health status [39]. Another reasons could be due to the presence of chronic diseases: some findings indicated that subjects which suffer from more than one chronic condition reported significantly lower HRQOL and the decrements were larger in PCS than in MCS [37, 40].

Moreover, it exists a relationship between quality of life and age. In a recent study, a lower PCS value was reported by older patients [37]. Additionally, obesity is related to increased risk of many chronic diseases that are highly prevalent among older adults [13].

Obesity in elderly people is a decisive factor adversely affecting the health related quality of life and psychological mood status [19]. Consistent with our findings, other authors have found that subjects with a high BMI had an increased adjusted risk of developing depression compared with subjects with a normal BMI [35, 41].

Our study has some limitations too. In particular, the small sample size of participants recruited exclusively in an Endocrinology Division was its main limitation with respect to representativeness and generalization of the results. Nevertheless, the endocrine disorders met in this Division, such as diabetes and thyroid diseases, are common disorders in older adults and elderly with a negative impact of quality of life [42]. Therefore, we think that there is a need to identify important characteristics related to quality of life in the older adults in order to prevent negative health outcomes, such as obesity. This is particularly true in Italy as well as in developed countries, where the prevalence of overweight and obesity is high and is increasing in elderly [43, 44]. Even though a very recent paper reports that false myths and unfounded scientific beliefs exist regarding obesity in both the literature and the popular press [45], we suggest that research funding should be channelled towards studying the benefits of reducing obesity in the elderly, as also evidenced by other authors [46]. Our results indicated that some psychological aspects represent correlates of perceived health related quality of life in older adults and elderly subjects. So, some prevention programmes should be implemented for improving health in aging. Some authors found that weight loss had some benefits on postural balance and on reduction of falls of older individuals, with a positive influence on health related quality of life in older and middle age obese subjects [4749].

Within some prevention programmes, specific personalized physical activity has to be mainly foreseen and included taking into account that physical activity is an excellent tool to prevent cardiovascular diseases, diabetes type II and also cancer in ageing as well as in obesity [5053]. It is known that age and BMI also negatively affected engagement in physical activity [54]. In our previous study, we found that obese subjects tended to engage in physical activity significantly less than the non–obese [10]. Therefore, the physical activity and, more in general, correct life style conditions (for instance, the nutrition) have beneficial effects in reducing the inflammatory state [55, 56] and in restoring the altered neuroendocrine pathway in ageing and obesity [57] with subsequent significant positive effects on the anxiety and depression [58].


In conclusion, our paper showing the close negative interrelationships among some psychological factors, BMI, physical activity in obesity, offers a valid tool in order to prevent adverse effects and cardiovascular complications in old obese subjects without further pharmacological interventions due to the possible presence of various co-morbidities, such as sarcopenia, metabolic syndrome, osteoarthritis, pulmonary complications and obstructive sleep apnea syndrome (OSAS) [59]. Previously, we found that older subjects who perform regular exercise (classified as ≥ 1 h/week) had a better psychological conditions, useful for the prevention of many chronic and age-associated disorders [54]. In view of the consequences of obesity in older persons, the ESWGOP committee members are seeking answer about what is the role of physical activity in prevention and treatment of sarcopenia in older people and what exercises are most effective for older people [60].



The authors wish to thank Paul Bowerbank for his help in reviewing the English of the manuscript.

Authors’ Affiliations

Unit of Geriatrics, INRCA (Italian National Institute on Aging)
Centre of Socio-economic Gerontological Research, Scientific-Technological Area, INRCA (Italian National Institute on Aging)
Scientific Direction, INRCA (Italian National Institute on Aging)
Unit of Rehabilitation Medicine, Scientific-Technological Area, INRCA (Italian National Institute on Aging)
Division of Endocrinology, Department of Clinical and Molecular Sciences, Umberto I Hospital, Polytechnic University of Marche
Laboratory of Nutrigenomic and Immunosenescence, Scientific-Technological Area, INRCA (Italian National Institute on Ageing)


  1. Popkin BM, Adair LS, Ng SW: Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012, 70: 3-21. 10.1111/j.1753-4887.2011.00456.x.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Gallus S, Odone A, Lugo A, Bosetti C, Colombo P, Zuccaro P, La Vecchia C: Overweight and obesity prevalence and determinants in Italy: an update to 2010. Eur J Nutr. 2013, 52: 677-685. 10.1007/s00394-012-0372-y.View ArticlePubMedGoogle Scholar
  3. Micciolo R, Di Francesco V, Fantin F, Canal L, Harris TB, Bosello O, Zamboni M: Prevalence of overweight and obesity in Italy (2001-2008): is there a rising obesity epidemic?. Ann Epidemiol. 2010, 20: 258-264. 10.1016/j.annepidem.2010.01.006.View ArticlePubMedGoogle Scholar
  4. Flegal KM, Kit BK, Orpana H, Graubard BI: Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013, 309: 71-82. 10.1001/jama.2012.113905.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Lavie CJ, De Schutter A, Patel DA, Romero-Corral A, Artham SM, Milani RV: Body composition and survival in stable coronary heart disease: impact of lean mass index and body fat in the “obesity paradox”. J Am Coll Cardiol. 2012, 60: 1374-1380. 10.1016/j.jacc.2012.05.037.View ArticlePubMedGoogle Scholar
  6. Neeland IJ, Turer AT, Ayers CR, Powell-Wiley TM, Vega GL, Farzaneh-Far R, Grundy SM, Khera A, McGuire DK, de Lemos JA: Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA. 2012, 308: 1150-1159. 10.1001/2012.jama.11132.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Vucenik I, Stains JP: Obesity and cancer risk: evidence, mechanisms, and recommendations. Ann N Y Acad Sci. 2012, 1271: 37-43. 10.1111/j.1749-6632.2012.06750.x.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Han TS, Tajar A, Lean ME: Obesity and weight management in the elderly. Br Med Bull. 2011, 97: 169-196. 10.1093/bmb/ldr002.View ArticlePubMedGoogle Scholar
  9. Costarelli L, Muti E, Malavolta M, Cipriano C, Giacconi R, Tesei S, Piacenza F, Pierpaoli S, Gasparini N, Faloia E, Tirabassi G, Boscaro M, Polito A, Mauro B, Maiani F, Raguzzini A, Marcellini F, Giuli C, Papa R, Emanuelli M, Lattanzio F, Mocchegiani E: Distinctive modulation of inflammatory and metabolic parameters in relation to zinc nutritional status in adult overweight/obese subjects. J Nutr Biochem. 2010, 21: 432-437. 10.1016/j.jnutbio.2009.02.001.View ArticlePubMedGoogle Scholar
  10. Marcellini F, Giuli C, Papa R, Tirabassi G, Faloia E, Boscaro M, Polito A, Ciarapica D, Zaccaria M, Mocchegiani E: Obesity and body mass index (BMI) in relation to lifestyle and psycho-social aspects. Arch Gerontol Geriatr. 2009, 49: 195-206.View ArticlePubMedGoogle Scholar
  11. Corica F, Corsonello A, Apolone G, Mannucci E, Lucchetti M, Bonfiglio C, Melchionda N, Marchesini G: Metabolic syndrome, psychological status and quality of life in obesity: the QUOVADIS study. Int J Obes (Lond). 2008, 32: 185-191. 10.1038/sj.ijo.0803687.View ArticleGoogle Scholar
  12. 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: 156-164.View ArticleGoogle Scholar
  13. Yan LL, Daviglus ML, Liu K, Pirzada A, Garside DB, Schiffer L, Dyer AR, Greenland P: BMI and health-related quality of life in adults 65 years and older. Obes Res. 2004, 12: 69-76. 10.1038/oby.2004.10.View ArticlePubMedGoogle Scholar
  14. Williamson DA, Martin CK, Stewart T: Psychological aspects of eating disorders. Best Pract Res Clin Gastroenterol. 2004, 18: 1073-1088.View ArticlePubMedGoogle Scholar
  15. Arterburn D, Westbrook EO, Ludman EJ, Operskalski B, Linde JA, Rohde P, Jeffery RW, Simon GE: Relationship between obesity, depression, and disability in middle-aged women. Obes Res Clin Pract. 2012, 6: e197-e206. 10.1016/j.orcp.2012.02.007.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Mather AA, Cox BJ, Enns MW, Sareen J: Associations of obesity with psychiatric disorders and suicidal behaviors in a nationally representative sample. J Psychosom Res. 2009, 66: 277-285. 10.1016/j.jpsychores.2008.09.008.View ArticlePubMedGoogle Scholar
  17. Marcellini F, Giuli C, Papa R, Gagliardi C, Malavolta M, Mocchegiani E: BMI, life-style and psychological conditions in a sample of elderly Italian men and women. J Nutr Health Aging. 2010, 14: 515-522. 10.1007/s12603-010-0098-6.View ArticlePubMedGoogle Scholar
  18. 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: 143-10.1186/1471-2458-12-143.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Banegas JR, López-García E, Graciani A, Guallar-Castillón P, Gutierrez-Fisac JL, Alonso J, Rodríguez-Artalejo F: Relationship between obesity, hypertension and diabetes, and health-related quality of life among the elderly. Eur J Cardiovasc Prev Rehabil. 2007, 14: 456-462. 10.1097/HJR.0b013e3280803f29.View ArticlePubMedGoogle Scholar
  20. Wang JJ, Smith W, Cumming RG, Mitchell P: Variables determining perceived global health ranks: findings from a population-based study. Ann Acad Med Singapore. 2006, 35: 190-197.PubMedGoogle Scholar
  21. Dhaussy G, Dramé M, Jolly D, Mahmoudi R, Barbe C, Kanagaratnam L, Nazeyrollas P, Blanchard F, Novella JL, SAFES Group: Is health-related quality of life an independent prognostic factor for 12-month mortality and nursing home placement among elderly patients hospitalized via the emergency department?. J Am Med Dir Assoc. 2012, 13 (5): 453-458. 10.1016/j.jamda.2011.10.002.View ArticlePubMedGoogle Scholar
  22. Luo Y, Xu J, Granberg E, Wentworth WM: A longitudinal study of social status, perceived discrimination, and physical and emotional health among older adults. Res Aging. 2012, 34: 275-301. 10.1177/0164027511426151.View ArticleGoogle Scholar
  23. Faloia E, Tirabassi G, Canibus P, Boscaro M: Protective effect of leg fat against cardiovascular risk factors in obese premenopausal women. Nutr Metab Cardiovasc Dis. 2009, 19: 39-44. 10.1016/j.numecd.2008.02.004.View ArticlePubMedGoogle Scholar
  24. Washburn RA, Smith KW, Jette AM, Janney CA: The physical activity scale for the elderly (PASE): development and evaluation. J Clin Epidemiol. 1993, 46: 153-162. 10.1016/0895-4356(93)90053-4.View ArticlePubMedGoogle Scholar
  25. Abete P, Ferrara N, Cacciatore F, Sagnelli E, Manzi M, Carnovale V, Calabrese C, de Santis D, Testa G, Longobardi G, Napoli C, Rengo F: High level of physical activity preserves the cardioprotective effect of preinfarction angina in elderly patients. J Am Coll Cardiol. 2001, 38: 1357-1365. 10.1016/S0735-1097(01)01560-1.View ArticlePubMedGoogle Scholar
  26. Ware JE, Sherbourne CD: The MOS 36-item short-form health survey (SF-36): I: conceptual framework and item selection. Med Care. 1992, 30: 473-483. 10.1097/00005650-199206000-00002.View ArticlePubMedGoogle Scholar
  27. Ware JE, Snow KK, Kosinski M, Gandek B: SF-36 health survey manual and interpretation guide. New England Medical Center. 1993, Boston, MA: The Health InstituteGoogle Scholar
  28. Apolone G, Cifani S, Liberati MC, Mosconi P: Questionario sullo stato di salute SF-36: traduzione e validazione in italiano (progetto IQOLA). Medic. 1997, 5: 86-94.Google Scholar
  29. Zigmond AS, Snaith RP: The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983, 67: 361-370. 10.1111/j.1600-0447.1983.tb09716.x.View ArticlePubMedGoogle Scholar
  30. Costantini M, Musso M, Viterbori P, Bonci F, Del Mastro L, Garrone O, Venturini M, Morasso G: Detecting psychological distress in cancer patients: validity of the Italian version of the hospital anxiety and depression scale. Support Care Cancer. 1999, 7: 121-127. 10.1007/s005200050241.View ArticlePubMedGoogle Scholar
  31. Lubben JE: Assessing social networks among elderly populations. Fam Community Health. 1988, 11: 42-52.View ArticleGoogle Scholar
  32. Giuli C, Spazzafumo L, Sirolla C, Abbatecola AM, Lattanzio F, Postacchini D: Social isolation risk factors in older hospitalized individuals. Arch Gerontol Geriatr. 2012, 55: 580-585. 10.1016/j.archger.2012.01.011.View ArticlePubMedGoogle Scholar
  33. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, Eckel RH, American Heart Association: Physical activity, and metabolism obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific statement on obesity and heart disease from the obesity committee of the council on nutrition, physical activity, and metabolism. Circulation. 2006, 113: 898-918. 10.1161/CIRCULATIONAHA.106.171016.View ArticlePubMedGoogle Scholar
  34. 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. Int J Obes Relat Metab Disord. 2003, 27: 701-709. 10.1038/sj.ijo.0802275.View ArticlePubMedGoogle Scholar
  35. Mannucci E, Petroni ML, Villanova N, Rotella CM, Apolone G, Marchesini G, QUOVADIS Study Group: Clinical and psychological correlates of health-related quality of life in obese patients. Health Qual Life Outcomes. 2010, 8: 90-10.1186/1477-7525-8-90.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, Kunst AE, European Union Working Group on Socioeconomic Inequalities in Health: Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008, 358: 2468-2481. 10.1056/NEJMsa0707519.View ArticlePubMedGoogle Scholar
  37. Manuti B, Rizza P, Pileggi C, Bianco A, Pavia M: Assessment of perceived health status among primary care patients in Southern Italy: findings from a cross-sectional survey. Health Qual Life Outcomes. 2013, 11: 93-10.1186/1477-7525-11-93.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Eikemo TA, Huisman M, Bambra C, Kunst A: Health inequalities according to educational level in different welfare regimes: a comparison of 23 European countries. Sociol Health Illn. 2008, 30: 565-582. 10.1111/j.1467-9566.2007.01073.x.View ArticlePubMedGoogle Scholar
  39. Jurges H: True health vs. response styles: exploring cross-country differences in self-reported health. Health Econ. 2007, 16: 163-178. 10.1002/hec.1134.View ArticlePubMedGoogle Scholar
  40. Wee CC, Davis RB, Hamel MB: Comparing the SF-12 and SF-36 health status questionnaires in patients with and without obesity. Health Qual Life Outcomes. 2008, 6: 11-17. 10.1186/1477-7525-6-11.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Godin O, Elbejjani M, Kaufman J: Body mass index, blood pressure, and risk of depression in the elderly: a marginal structural model. Am J Epidemiol. 2012, 176: 204-213. 10.1093/aje/kws003.View ArticlePubMedGoogle Scholar
  42. Sinclair A, Morley JE, Rodriguez-Mañas L, Paolisso G, Bayer T, Zeyfang A, Bourdel-Marchasson I, Vischer U, Woo J, Chapman I, Dunning T, Meneilly G, Rodriguez-Saldana J, Gutierrez Robledo LM, Cukierman-Yaffe T, Gadsby R, Schernthaner G, Lorig K: Diabetes mellitus in older people: position statement on behalf of the International Association of Gerontology and Geriatrics (IAGG), the European Diabetes Working Party for Older People (EDWPOP), and the International Task Force of Experts in Diabetes. J Am Med Dir Assoc. 2012, 13: 497-502. 10.1016/j.jamda.2012.04.012.View ArticlePubMedGoogle Scholar
  43. Intorre F, Maiani G, Cuzzolaro M, Simpson EE, Catasta G, Ciarapica D, Mauro B, Toti E, Zaccaria M, Coudray C, Corelli S, Palomba L, Polito A: Descriptive data on lifestyle, anthropometric status and mental health in italian elderly people. J Nutr Health Aging. 2007, 11: 165-174.PubMedGoogle Scholar
  44. Forrester T: Epidemiologic transitions: migration and development of obesity and cardiometabolic disease in the developing world. Nestle Nutr Inst Workshop Ser. 2013, 71: 147-156.View ArticlePubMedGoogle Scholar
  45. Casazza K, Fontaine KR, Astrup A, Birch LL, Brown AW, Bohan Brown MM, Durant N, Dutton G, Foster EM, Heymsfield SB, McIver K, Mehta T, Menachemi N, Newby PK, Pate R, Rolls BJ, Sen B, Smith DL, Thomas DM, Allison DB: Myths, presumptions, and facts about obesity. N Engl J Med. 2013, 368: 446-454. 10.1056/NEJMsa1208051.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Salihu HM, Bonnema SM, Alio AP: Obesity: what is an elderly population growing into?. Maturitas. 2009, 63: 7-12. 10.1016/j.maturitas.2009.02.010.View ArticlePubMedGoogle Scholar
  47. Maffiuletti NA, Agosti F, Proietti M, Riva D, Resnik M, Lafortuna CL, Sartorio A: Postural instability of extremely obese individuals improves after a body weight reduction program entailing specific balance training. J Endocrinol Invest. 2005, 28: 2-7.View ArticlePubMedGoogle Scholar
  48. Teasdale N, Hue O, Marcotte J, Berrigan F, Simonau M, Doré J, Marceau P, Marceau S, Tremblay A: Reducing weight increases postural stability in obese and morbid obese men. Int J Obes (Lond). 2007, 31: 153-160. 10.1038/sj.ijo.0803360.View ArticleGoogle Scholar
  49. Fjeldstad C, Fjeldstad AS, Acree LS, Nickel KJ, Gardner AW: The influence of obesity on falls and quality of life. Dyn Med. 2008, 7: 4-10.1186/1476-5918-7-4.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Uauy R, Corvalan C, Dangour AD: Conference on “Multidisciplinary approaches to nutritional problems”: rank prize lecture: global nutrition challenges for optimal health and well-being. Proc Nutr Soc. 2009, 68: 34-42. 10.1017/S002966510800880X.View ArticlePubMedGoogle Scholar
  51. Tanaka K, Nakanishi T: Obesity as a risk factor for various diseases: necessity of lifestyle changes for healthy aging. Appl Human Sci. 1996, 15: 139-148. 10.2114/jpa.15.139.View ArticlePubMedGoogle Scholar
  52. Hawley JA: Exercise as a therapeutic intervention for the prevention and treatment of insulin resistance. Diabetes Metab Res Rev. 2004, 20: 383-393. 10.1002/dmrr.505.View ArticlePubMedGoogle Scholar
  53. Vitetta L, Sali A: Colorectal cancer and CHF–reviewing the evidence for complementary medicine. Aust Fam Physician. 2006, 35: 339-342.PubMedGoogle Scholar
  54. Giuli C, Papa R, Mocchegiani E, Marcellini F: Predictors of participation in physical activity for community-dwelling Italian elderly people. Arch Gerontol Geriatr. 2012, 54: 50-54. 10.1016/j.archger.2011.02.017.View ArticlePubMedGoogle Scholar
  55. Nicklas BJ, You T, Pahor M: Behavioural treatments for chronic systemic inflammation: effects of dietary weight loss and exercise training. CMAJ. 2005, 172: 1199-1209. 10.1503/cmaj.1040769.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Hurley BF, Hanson ED, Sheaff AK: Strength training as a countermeasure to aging muscle and chronic disease. Sports Med. 2011, 41: 289-306. 10.2165/11585920-000000000-00000.View ArticlePubMedGoogle Scholar
  57. Lopresti AL, Drummond PD: Obesity and psychiatric disorders: commonalities in dysregulated biological pathways and their implications for treatment. Prog Neuropsychopharmacol Biol Psychiatry. 2013, 45: 92-99.View ArticlePubMedGoogle Scholar
  58. Azevedo Da Silva M, Singh-Manoux A, Brunner EJ, Kaffashian S, Shipley MJ, Kivimäki M, Nabi H: Bidirectional association between physical activity and symptoms of anxiety and depression: the Whitehall II study. Eur J Epidemiol. 2012, 27: 537-546. 10.1007/s10654-012-9692-8.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, Di Francesco V, Fantin F, Bissoli L, Bosello O: Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes. 2005, 29: 1011-1129. 10.1038/sj.ijo.0803005.View ArticleGoogle Scholar
  60. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM, Topinková E, Vandewoude M, Zamboni M, European Working Group on Sarcopenia in Older People: Sarcopenia: European consensus on definition and diagnosis: report of the European working group on Sarcopenia in older people. Age Ageing. 2010, 39: 412-423. 10.1093/ageing/afq034.View ArticlePubMedPubMed CentralGoogle Scholar
  61. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:


© Giuli et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. 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 work is properly cited.