Obesity in the Context of Aging: Quality of Life Considerations.

PharmacoEconomics DOI 10.1007/s40273-014-0237-8

REVIEW ARTICLE

Obesity in the Context of Aging: Quality of Life Considerations Francesco Corica • Giampaolo Bianchi • Andrea Corsonello • Natalia Mazzella • Fabrizia Lattanzio • Giulio Marchesini

Ó Springer International Publishing Switzerland 2014

Abstract The progressive increase in the prevalence of obesity and aging in the population is resulting in increased healthcare and disability spending. The burden of obesity is particularly relevant in old age, due to accumulating comorbidities and changes in body composition. Sarcopenic obesity, a mix of over- and under-nutrition, causes frailty, disability, and problems in social and psychological areas, impacting overall health-related quality of life (HR-QOL). The relationship between obesity, aging, and HR-QOL is, however, much more complex than generally acknowledged and is difficult to disentangle. The impact of obesity on HR-QOL is particularly strong in young people, who are free of co-morbidities. It progressively attenuates, compared with the general population, with advancing age, when co-morbid conditions are diffusely present and reduce the perceived health status, independent of obesity. However, even this apparent ‘obesity paradox’ should not minimize the importance of obesity on HR-QOL, as other obesity-associated limitations and disabilities do impact

F. Corica Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy

HR-QOL in older age. A patient-centered approach aimed at reducing the disability and social isolation of advancing age is mandatory to improve HR-QOL in any class of obesity.

Key Points Aging per se is characterized by a progressive loss of lean body mass; when coupled with obesity, sarcopenic obesity is an additional cause of frailty Obesity is characterized by poor health-related quality of life, because of physical and psychological co-morbidities, which is also related to the social stigma of obesity. When compared with the general population, the loss in quality of life (QOL) progressively attenuates with advancing age, due to accumulating co-morbidities in the community In subjects older than 75 years, no apparent additional effects of obesity on QOL are observed, but data are influenced by the selection bias of the low-to-moderate severity of obesity in these individuals

G. Bianchi Department of Internal Medicine and Geriatrics, ‘‘Alma Mater Studiorum’’ University, Bologna, Italy A. Corsonello F. Lattanzio Scientific Direction, Italian National Research Center on Aging (INRCA), Ancona, Italy

1 Introduction

N. Mazzella G. Marchesini (&) Unit of Metabolic Diseases and Clinical Dietetics, Department of Clinical Medicine, ‘‘Alma Mater Studiorum’’ University, Policlinico S. Orsola, Via Massarenti, 9, I-40138 Bologna, Italy e-mail: [email protected]

Life expectancy is increasing worldwide. The proportion of adults in the age range 60–80 years will double to over 20 % and another 4 % will be over 80 years old by 2050; in developed countries, these figures will be 32 and 9 %,

F. Corica et al.

respectively [1]. In parallel, the prevalence of overweight and obesity is increasing in both developing and developed countries in all age groups [2], with peaks between 65 and 70 years and declines thereafter because of premature mortality. In the USA nearly 70 % of individuals aged 65 years and older are in the overweight/obesity range and 29 % are obese [3], whereas in Italy approximately 20 % of men and 32 % of women between 65 and 74 years have a body mass index (BMI) higher than 28 kg/m2 (considered obese by Italian health standards) [4]. These numbers are expected to increase with time [5]. Obesity imposes a heavy clinical and social burden: worldwide, at least 2.8 million people die each year as a result of being overweight or obese, and an estimated 35.8 million (2.3 %) of global disability-adjusted life-years (DALYs) are due to obesity [6]. Since, due to advancements in medical research and technology, the leading cause of death has shifted from acute illnesses to chronic and degenerative diseases, aging is also associated with long-term functional impairment [7] and a high burden of illness [8, 9]. Both obesity and aging impose various functional limitations on the human body [10, 11], resulting in a severe burden on quality of life (QOL) [11–15]. An emerging global public health crisis is signaled by this progressive growth in the number of people living longer with disability, and, consequently, an increase in medical expenditures for this growing patient population [16–18]. The aim of this review is to collate and critically assess the available information regarding the impact of obesity on aging and its effect on QOL. Understanding how QOL is affected by obesity in the elderly might provide a clearer picture of how various individuals respond to the disease and its treatment. This, in turn, would provide clinicians with the necessary tools in managing this disease, as well as potentially influencing new clinical pathway development. Finally, it will stimulate changes in service provisions, healthcare cost management, and public health policy. 1.1 Search Strategy A PubMed search was carried out in February 2014 to gather relevant articles, combining the following keywords: obesity, aging, sarcopenic obesity, quality of life, quality of life measures. The bibliographies of relevant articles were also searched to identify additional studies. Search results were limited to studies published in English from 1990 to 2014. Studies concerning epidemiology or physiopathology of obesity and sarcopenic obesity, agerelated diseases, frailty, physical performance, cognitive status, health outcomes (disability, institutionalization, and death), health status, and psychological status, were included. Two independent reviewers (AC and GM)

extracted study characteristics including sample size, sample characteristics, and study design, and then selected the most valid, relevant, and useful studies for the aim of this review. Three hundred potential relevant studies were initially identified. After a screening on the basis of title and abstract, 166 articles were excluded. The remaining 144 articles were screened on the basis of full text, 56 of which did not fulfill the aim of the review and were also excluded, leaving 88 articles to be considered in the review. Studies carried out by the authors and studies extracted from reference lists were also included in the analysis.

2 Obesity: Special Considerations for the Elderly Obesity, defined as having a BMI above 30 kg/m2, is a chronic disease associated with a host of co-morbidities [16–20], a risk factor for decreased life expectancy [21], and presents a significant socioeconomic burden [22–25]. The risk factors of obesity and their management have been extensively reviewed elsewhere, both for the general population [20] and for the child and adolescent subgroup [26], but obesity in the elderly, though a growing public policy issue, has received less attention. Part of the reason for this lessened scrutiny is the fact that aging poses special challenges to the medical community [27], and obesity in the elderly has additional clinical implications in this population [28]; the other contributing factor is that there is some controversy regarding the potential harm of obesity in older age [15]. 2.1 Sarcopenic Obesity When coupled with obesity, the progressive loss of lean body mass observed with aging generates a new type of malnutrition, called sarcopenic obesity; its prevalence is estimated to range from 8.8 % in women aged 65–75 years to 17.5 % in men older than 75 years [29]. The term ‘sarcopenic obesity’ characterizes the coexistence of excess fat with scarce lean body mass (including both muscle and bone) [30–32] as it occurs during the aging process. This type of mixed malnutrition of the elderly not only increases the risk of cardiometabolic complications but also increases the likelihood of developing cognitive dysfunction and dementia, as well as physical disability [33], thus contributing to the increased risk of frailty, disability, and worsening QOL observed in older populations [3, 29, 34]. Finally, sarcopenic obesity is more closely associated with knee osteoarthritis than non-sarcopenic obesity, independent of body weight [35]. Aging is characterized by relevant changes in body composition—muscle mass loss and increased fat

Obesity, Aging, and Quality of Life

deposition—mediated by a variety of behavioral, endocrine, and pathologic factors. Increased adiposity develops at different ages, between the third and seventh decade, and may increase, decrease, or remain unchanged thereafter [36]. After the age of 20–30 years, lean muscle mass progressively decreases, mainly due to reduced physical activity [37–40]. Given the age-related changes in body composition, obesity and low muscle mass (or strength) may coexist in the same person simply by chance, but there is evidence of involvement of pathophysiologic pathways common to both muscle and fat metabolism in the process of aging [30], including an age-related decrease in resting metabolic rate, a thermic effect of food, and physical activity [41]. Muscle loss can be instigated through multiple mechanisms: decrease in anabolic hormones found in sarcopenia and obesity [30, 32, 42, 43], insulin resistance [44–46], and upregulation of protein degradation in the chronic inflammatory aspect of obesity via the ubiquitin– proteasome pathway [15, 30]. Additionally, in obesity muscle loss can lead to increased fat deposition [47] and fatty infiltration of muscle—myosteatosis—which may drive the inflammatory environment in muscle [30] and is associated with insulin resistance in older people [48, 49]. In chronic inflammatory states, obese patients mobilize muscle more than fat [50]; this represents a major contributor to fat gain, which in turn reinforces muscle loss [51], creating the vicious cycle of fat gain and muscle loss (also observed during frequent dieting) that contributes to sarcopenic obesity [52]. Finally, weakness or poor physical performance [53] adds to reduced muscle mass with aging; during the age span from 20 to 80 years, there is approximately a 30 % reduction in muscle mass and a decline in cross-sectional area of about 20 % [54], due to a decline in both muscle fiber size and number [55]. In the last few years, the terms sarco-osteopenia or sarco-osteoporosis have been used to describe the conditions where both muscle and bone mass are reduced [56]. Since these alterations are frequently found in obese patients, the term osteo-sarcopenic obesity was also coined [57]. Although previous studies have found an increase in absolute bone mineral density (BMD) in obese adults [58], the demonstration of a significant association between BMI and the risk of fractures even in the presence of normal absolute BMD does not support a protective role of obesity [59, 60]. Indeed, BMD per BMI unit is low in obese subjects [61]. Several metabolic factors may help explain the negative effects of obesity on bone: low levels of 25OH vitamin D, increased levels of parathyroid hormone, and inflammation may contribute [62–64]; finally, the mechanical stimulation that best promotes the maintenance of bone mineral is not the static (which increases in overweight/obesity) but the dynamic one [65], which is

dependent on physical activity (reduced in obesity). As a consequence, an increased risk of fracture becomes a major component of the complex scenario of obesity in older patients, with significant implications in terms of public health [66]. Whatever the origin in individual cases, sarcopenic (and osteo-sarcopenic) obesity is a major problem in the elderly, responsible for a variety of associated diseases, as well as being a marker and/or a cause of frailty in the elderly population (Fig. 1). 2.2 Obesity and Frailty Frailty is currently recognized as a condition that increases the probability of developing adverse health outcomes in the elderly, including disability, institutionalization, and death [67]. The age-related decline in many physiological systems results in increased vulnerability when triggered by minor stressor events and by changes in objective and subjective health status [68]. Frailty has been operationally defined as a condition meeting at least three out of the five selected phenotypic criteria (Table 1) [69], or as an index based on the number of deficits accumulated over time in 70 items in several domains, also including social and psychological areas (cognition, mood, motivation, communication skills, mobility, balance, activity of daily living, bowel and bladder functions, nutrition, co-morbidities, social resources) (Table 2) [70]. While there is considerable evidence that obesity and aging are associated with declines in organ functions that result in increased disability [71], the association between obesity and frailty is less obvious than for other clinical conditions, mainly because frailty is considered a wasting disorder and the common clinical perception of a frail person is of one who is small and thin [47, 72]. Nevertheless, there are several reasons for associating obesity with frailty: the endocrine disturbances observed in obesity [73], the low-grade inflammatory state, reduced immune function, cognitive decline [29], and decreased physical performance [74] may all be also associated with frailty. Older adults with sarcopenic obesity may experience considerable weakness from a decrease in quantity and quality of muscle mass and also from the need to move a heavier body [75], leading to increased joint dysfunction, chronic pain, dependency in activities of daily living, and impaired QOL [33, 76]. Recent longitudinal studies confirmed this view of obesity as an important driver of frailty, indicating that obesity predicts frailty among post-menopausal women [77], and in middle-aged men obesity predicts the occurrence of frailty 26 years later [78]. The development of frailty may start as early as midlife and obesity is one of the underlying risk factors [79].

F. Corica et al. Table 1 Phenotype of frailty Frailty is defined by the presence of 3 or more characteristics, intermediate/ pre-frailty by 1 or 2 criteria [69] BMI body mass index, CES-D Center for Epidemiologic Studies Depression

Characteristics

Cut-off for frailty

Weight loss (unintentional)

[10 lb (4.5 kg) in previous year

Grip strength

Lowest 20 % by sex/BMI

Exhaustion

Self-report (CES-D scale)

Walk time, 15 feet (4.6 meters)

Lowest 20 % by sex/height

Low physical activity

\383 kcal/week (males); \270 kcal/week (females)

Fig. 1 Links between obesity, aging, and frailty. Note that obesity, through adipokine production by visceral fat, may accelerate sarcopenia, also increased by frequent dieting

Table 2 Classification of fitness/frailty in older people, according to Rockwood et al. [70] 1. Very fit: robust, active, energetic, well-motivated and fit; these people commonly exercise regularly and are in the most fit group for their age 2. Well: without active disease, but less fit than people in category 1 3. Well, with treated co-morbid disease: disease symptoms are well-controlled compared with those in category 4 4. Apparently vulnerable: although not frankly dependent, these people commonly complain of being ‘‘slowed up’’ or have disease symptoms 5. Mildly frail: with limited dependence on others for instrumental activities of daily living 6. Moderately frail: help is needed with both instrumental and non-instrumental activities of daily living 7. Severely frail: completely dependent on others for the activities of daily living, or terminally ill

In summary, there is evidence that obesity in midlife should be considered an independent risk factor for the onset of frailty about two decades later. 2.3 Mortality in the Elderly Obese Population Despite the increased risk of frailty and morbidity that obesity confers upon a person, there is some debate in the field regarding the effect of obesity on mortality in the elderly. Paradoxically, in the past 10 years several studies have documented that the presence of obesity or overweight in older people with chronic diseases (cancer, cardiovascular disease, kidney disease) results in a better prognosis than in normal-weight subjects [80, 81]. The mechanism(s) underlying the ‘obesity paradox’ is far from

clear, and several hypotheses have been proposed, from the inadequacy of BMI for the assessment of body composition in the elderly, to the survival effect (the selection of ‘resistant’ survivors where the relation between BMI and mortality is lost) [82], to the ability of adipose tissue to accommodate lipophylic toxins (persistent organic pollutants) [83], associated with oxidative stress. This issue has been extensively reassessed in a recent analysis of crosssectional, nationally representative US cohorts [84]. When confounders and selection biases are taken into account in survival models, no ‘obesity paradox’ was detected and the relationship between obesity and mortality was even stronger with advancing age. Other issues that arise when considering the impact of obesity on mortality in the obese patient population are the


potentially negative effect of weight loss in the elderly, and the unknown extent to which obesity impacts older patients with a shorter duration of obesity [15]. While weight loss has been shown to be detrimental in the population of obese elderly patients [85, 86], this was the case only when weight loss was involuntary [87–89] or when weight fluctuated repeatedly [90]. Finally, the impact on mortality of recent weight gain in the elderly population has not yet been studied; over the short-term, only being underweight is predictive of mortality in the elderly [91, 92].

3 Obesity and Quality of Life In the past 30 years there has been an increasing consensus regarding the necessity of incorporating a patients’ selfassessment of health status into disease-management practices. The concept of health-related QOL (HR-QOL), as originally developed by the World Health Organization (WHO), has gained relevance as a new outcome measure in chronic disease [93], where life is not at risk for several years, and treatment options—including surgery—need to be assessed in terms of risk/benefit with a special emphasis on patients’ perspectives. Ware [94] depicted the various components of QOL as an onion, where the different layers are representative of the physical, mental, and social dimensions of individual health status. Biological health, affected by somatic diseases, is at the core of the onion, and is measured by the biochemical or imaging parameters used by clinical medicine. The external layer (QOL) also comprises several domains, including social relationships, work satisfaction, money, and daily living. The central layers reflect the relationships between biological health and QOL, and are referred to as HR-QOL. 3.1 Measuring Quality of Life The negative impact of obesity on the four main domains of HR-QOL (physical health, mental health, social functioning, and vocational functioning [95]) largely outweighs physical symptoms and its associated co-morbidities, such as diabetes mellitus, hypertension, and coronary heart disease [96]. Obesity per se is a cause of discrimination in several areas of social life [97]. Psychological disturbances, frequently observed in obese people [98], are likely to further impair the perceived health status. Hence, a quantitative measurement of HR-QOL (including the physical and mental dimensions) is critical in defining strategies to improve patients’ reported outcomes. Multiple instruments have been developed to measure HR-QOL in the general population, as well as in various disease areas, over the past 30 years. These instruments fall into two categories—generic versus disease-specific tools.

The domains of generic questionnaires involve areas that all people consider important for their life, and thus outcomes may be compared with those obtained in a normative (general) population. In addition, generic instruments have the advantage of being comparable across diseases, as well as providing information on functional improvements in response to a given treatment. The disadvantage of generic HR-QOL measures is that they may be relatively insensitive to disease-specific outcomes [95, 98]. By contrast, disease-specific HR-QOL measures include domains particularly relevant to the targeted population, and thus are generally more sensitive to smaller differences between groups and smaller changes over time than generic measures [95, 98]. However, they have more limited empirical validation [95, 98]. In order for a disease-specific instrument to be appropriately used in the target population it needs to have a well-defined application in the population of choice, the homogeneity of the target population and the clinical outcomes of the disease (and/or treatment) should be considered in the design of the instrument, the psychometric properties (reproducibility, validity, and responsiveness) of the instrument need to be validated on the target population, statistical analysis of data should include appropriate methodology to the outcome measured, and, finally, results should be easily interpretable [99–101]. For a comprehensive understanding of the HR-QOL of the target population, it is recommended that researchers use both a generic and a disease-specific instrument in their evaluations [95, 98, 102], in order to cover the whole spectrum of disease-associated burden. Measures of HR-QOL in obesity have been reviewed in several publications [95, 98, 103–106]; select generic and obesity-specific measures and their properties are summarized in Table 3. The most largely used generic questionnaires are the Medical Outcomes Survey Short-Form 36 (SF-36) [107], EuroQol (EQ-5D) [108], Nottingham Health Profile (NHP) [109], and Psychological General WellBeing Inventory (PGWBI) [110]. The SF-36 is a 36-item questionnaire covering two main components: physical (which may be further split into the domains of physical functioning, role limitation—physical, bodily pain, and general health) and mental (which includes role limitation—emotional, vitality, mental health, and social functioning) [111]. Similarly, part I of the NHP includes six domains (energy, sleep, pain, emotional reactions, social isolation, physical mobility), while part II provides specific information regarding the effects of health status on relevant everyday activities, namely on paid employment, jobs around the home, social life, home life, sex life, hobbies, and holidays [109]. The EQ-5D questionnaire contains five questions about mobility, self-care, routine activities, pain/ discomfort, and anxiety/depression combined with a subjective quantitative assessment of healthcare status [108]

x

x

Quality of Well-Being (QWB) scale [180]

CDC’s Behavioral Risk Factor Surveillance System (BRFSS)—Healthy Days HR-QOL-14 measures [182]

x

x

Impact of Weight on Quality of Life (IWQOL) [145]

IWQOL-Lite [183]

Available in English

Obesity-specific

x

EuroQol health states descriptive system (EQ-5D) and visual analogue scale (EQVAS) [176, 177]

x

x

x

x

x

x

x

Mental/ emotional health

x

Physical health

x

x

x

x

x

x

Social functioning

x

x

x

x

x

General health

Dimensions measured in QOL instruments

Health Status Questionnaire-12 version 2.0 (HSQ12) [175]

Medical Outcomes Study Short Form36 (SF-36)/SF-5D [107, 172] RAND 36-Item Health Survey [173]

Generic

Questionnaire typea

Self-esteem, sexual life, public distress, and work

Work, mobility, self-esteem, sexual life, activities of daily living, and comfort with food

Global life satisfaction; satisfaction with emotional and social support; pain, depression, anxiety, sleeplessness, and vitality

Self-care/mobility, self-care/usual activity, physical activity, symptoms

Mobility, self-care, usual activities, pain/ discomfort, and anxiety/depression

Role—physical, role—mental, bodily pain, energy/fatigue

Role physical, bodily pain, vitality

Role physical, bodily pain, vitality

Other dimensions

Table 3 Health-related quality of life measurement instruments used in obesity and weight-loss studies

5 domains, 31 items

8 domains, 74 items

14 Items

4 domains

5 domains, 5 items

8 domains, 12 items

8 domains, 36 items

8 domains, 36 items

Structure: total domains/ items

0–100 scale, where 100 represents the best HR-QOL

0–100 scale, where 100 represents the best HR-QOL

Scoring: 0–30 healthy days, with 30 = best HR-QOL

Reporting on a continuum of wellness from 0 to 1 for overall HR-QOL; dead = 0.00 and perfect health = 1.00

3 levels of severity per dimension (the digits for 5 domains can be combined in a 5-digit number describing the respondent’s health state); overall health marked visually, with 100 being best QOL

Scoring on a 3–6 Likert scale; end score transformed to 0–100 scale, with 100 indicating best QOL

0–100 scale, with 100 indicating best QOL

0–100 scale, with 100 indicating best QOL

Scoring

0.90–0.94 [103]

0.87 [103]

N/A

0.83 for mental subscale [181]

0.68 for lung cancer patients [178]: 0.85 in HIV patients [179]

0.88 [175]

0.78–0.93 [174]

0.76–0.95 [95]

Internal consistency (Chronbach’s alpha)b

F. Corica et al.

x

x

x

x

Obese Specific Quality of Life (OSQOL) [102]

Obesity Related Well-Being (ORWELL-97) [190]

Available in other languages

Bariatric Analysis and Reporting Outcomes System (BAROS) [189]

Obesity Adjustment Survey—Short Form (OAS-SF) [188]

Moorehead-Ardelt Quality of Life Questionnaire II (M-AQoLQII) [187]

x

x

x

x

x

x

x

x

x

x

Psychological status/social adjustment and physical symptoms/impairment

Vitality, desire to do things

Self-esteem, work, and sexuality

Assess the psychological distress of individuals who are morbidly obese

Self-esteem, work, sexuality, and eating behavior

Comparative health, overweight distress, depression, self-esteem and self-regard, physical appearance, current employment and physical and social activities, satisfaction with treatment, health state preference

x

x

Global evaluation of position in life related to weight, weight loss, and weight-loss treatment

Other dimensions

Presence and bothersomeness of symptoms

General health

x

x

Social functioning

Weight-Related Symptom Measure (WRSM) [184, 185] Health-related quality of life, health state preference (LewinTAG HSP) questionnaire [186]

x

Mental/ emotional health

x

Physical health


Obesity and Weight Loss Quality of Life (OWLQOL) questionnaire [184, 185]

Questionnaire typea

Table 3 continued

2 domains

4 domains, 11 items

5 domains, 7 items

20 items

5 domains, 6 items

9 domains, 55 items

20 items

17 Items


Rating on a 4-point Likert scale; summing scores to get total, higher score = worse HR-QOL

Rating on a 5-point Likert scale; transformed to 0–100 scale, where 100 represents the best HR-QOL

Rating on a 5-point scale; 1–9 points, with 9 best health

Rating on a 5-point Likert scale

Rating on a 10-point Likert scale, summing scores to get total, higher score = better HR-QOL

Yes/no responses and rating on a 7-point scale; 0–120 scale, with higher score = worse symptoms (HR-QOL) 0–100 Scale (or \100 for some domains), where 100 represents the best HR-QOL for current general health state

Rating on a 6-point Likert scale, transformed to 0 to 100 scale, where 100 represents the best HR-QOL

Scoring

0.83 [103]

0.77 [103]

N/A

0.72 [103]

0.84 [103]

0.85–0.94 [103]

0.87 [103]

0.93–0.96 [103]



x

x

x

General health

Life overall, independence, control over life, freedom, area (home and neighborhood), financial circumstances, and religion/ culture

Control, autonomy, pleasure, self-realization

Sensory abilities; autonomy; past, present and future activities; social participation; death and dying; and intimacy

Sex life, comfort with food and diet experience

Symptoms, activity/mobility, personal hygiene/clothing, sexual life

Psychosocial functioning in everyday life

Other dimensions

Rating on 4-point Likert scale, summed total, scale range 0–57, with 57 = best HR-QOL Rating on 5-point Likert scale; range 35–175 and 32–160, where 175 total or 160 total = best HRQOL

8 domains, 32–35 items

Rating on 5-point scales, total score range 24–120, with 120 = best HR-QOL

Rating on a 5-point scale, transformed to 0–100 scale, with 100 = best HR-QOL

Rating on a 7-point Likert scale, higher scores = better HR-QOL

Rating on a 4-point scale; responses are aggregated to a total scale score and transformed to a 0–100 scale; higher score = worse HRQOL

Scoring

19 items

7 domains, 24 items

5 dimension, 36 items

6 domains, 44 items

8 items


0.75–0.90 [194]

0.6–0.8 [195]; 0.55–0.87 [194]

0.72–0.91 [193]; 0.42–0.85 [194]

0.72–0.92 [192]

0.65–0.93 [115]

0.92 [103]


Internal consistency (as reported by the calculated Chronbach’s alpha) is a measure of the questionnaire’s reliability, and reports on the internal correlation among the questionnaire’s domains (each domain’s items should correlate with each other and not with items intended to measure other domains). An alpha value [0.7 is considered to have acceptable internal consistency for group comparisons [197]

b

More details on the psychometric properties of the selected questionnaires listed above, as well as their use in the obesity field, can be found in several reviews and comparative articles: generic measures [95, 98, 104, 196], obesity-specific measures [103, 105, 115, 183], and old age-specific measures [117, 118]

a

CDC Centers for Disease Control, HR-QOL health-related quality of life, N/A not applicable, QOL quality of life

x

Older People’s Quality of Life (OPQOL) questionnaire [118]

x

x

x

Control, Autonomy, Self-realization and Pleasure (CASP19) [195]

World Health Organization’s WHOQOL-old [193]

For the elderly (general QOL, not HR-QOL)

x

x

Quality of Life, Obesity and Dietetics (QOLOD) [192]

x

x

x

x

Social functioning

Laval Questionnaire [115]

Mental/ emotional health x

Physical health


Obesity-related Psychosocial problems scale (OP-Scale) [191]

Questionnaire typea

Table 3 continued

F. Corica et al.


and has been found sufficiently sensitive for use in obese patients [104]. The PGWBI is a 22-item instrument exploring six dimensions, namely anxiety, depressed mood, positive well-being, self-control, general health, and vitality [112]. It is frequently used to generate an overall score for general well-being, useful to define the HR-QOL profile of obese patients [113, 114]. With all four tools, the effects of obesity and aging on HR-QOL are not equally distributed across the different domains. In the obesity-specific instrument category, multiple options exist—for example, a few instruments are developed for a specific target population (such as the morbidly obese) or for a particular intervention (such as bariatric surgery)—with varying degrees of reliability (as indicated by psychometric testing) [103, 105, 115]. There is no clear consensus on the choice of obesity-specific instrument; since these instruments have been carefully tailored to specific populations and treatment options, the choice of the best instrument to measure HR-QOL depends on the target population, treatment, outcome, etc. Of the 12 instruments currently in use, six have been used in clinical trials [103]. Measuring HR-QOL in the elderly population is slightly more problematic. First, generic instruments are developed with a younger population in mind; thus, elderly patients have worse scores on physical norms, which may result in an underestimate of the HR-QOL as perceived by the elderly patient [116, 117]. Second, old age-specific questionnaires include non-HR-QOL domains (such as religion, economic status, etc.), and thus are really measuring generic QOL [117]. In the past decade, several QOL instruments have been developed for the elderly population, and their properties are also listed in Table 3. All three questionnaires listed in Table 3 have reasonable reliability [117], but the choice of a questionnaire may again depend on the target population and the specific disease/treatment evaluated [118]. There are no currently available instruments measuring HR-QOL in the obese and elderly population; typically, the results of a generic or obesityspecific instrument are evaluated for the sub-population of elderly patients. 3.2 Impact of Obesity on Health-Related Quality of Life (HR-QOL) Multiple studies have examined the impact of obesity on HR-QOL [119–122]. Because of socio-cultural differences, the impact of obesity on HR-QOL may differ from country to country [123, 124] and in relation to age and sex [125, 126]. For the most widely used tools, normative values are available and may be used for comparison with the obese population to assess the impact of the disease on HR-QOL, as well of the modifying effects of sex, age, and other

socioeconomic variables. As previously noted, unfortunately, normative values are not available for the population above 65 years, and the significance of obesity remains speculative in all tested domains, with advanced age as the modifier. In general, a negative association is reported between BMI and subjective health [119–122, 127], particularly at younger age in women, but not always in men [126, 128], with younger women being much more worried about their excess weight. The differences between normal-weight and obese people are largely fuelled by the underlying psychopathology, associated depression [129] and eating disorders [130], body image dissatisfaction [131], as well as somatic diseases, particularly in the younger population

Fig. 2 Obesity and quality of life: effect of aging on two domains of the SF-36 questionnaire, representative of physical HR-QOL (physical functioning, closed circles) and mental HR-QOL (social functioning, open circles). The upper panel shows crude values, where higher values indicate better HR-QOL). The lower panel shows Z-score values, indicating the difference between the values measured in the obese population and normative values, on a scale having the standard deviation of the general population as unit measure. All differences are statistically significant compared to norms (not crossing the zero line). Note the striking difference in physical HRQOL, when considered in general terms or as a function of the control population. Also, Z-scores of mental HR-QOL are particularly poor in young age (redrawn from data from the QUOVADIS study [114]). HR-QOL health-related quality of life, SF-36 Medical Outcomes Survey Short-Form 36

F. Corica et al. Fig. 3 The spectrum of diseases associated with obesity, which are responsible for disability and poor quality of life in the elderly. COPD chronic obstructive pulmonary disease, OSAS obstructive sleep apnea syndrome, TIA transient ischemic attack

[132]. In a recent systematic review and meta-analysis of eight studies in 43,086 participants, Ul-Haq et al. [121] tested the effects of obesity in physical and mental HRQOL assessed by the generic SF-36 questionnaire. Only one study included patients over 65 years, confirming that a significant association existed between reduced physical QOL and increased BMI, with an age-response relationship. The negative effects of increased BMI on mental health were only observed in class III obesity, and poorer mental health was probably specific to women at a younger age [133]. Treatment aimed at reducing obesity also showed an impact on subjects’ HR-QOL: most weight-loss trials indicated an improvement in the physical components of HR-QOL—when assessed with the generic HR-QOL SF36 measure—for patients who lost weight [134]. When obesity-specific measures were used in assessing HR-QOL in weight-loss trials, a greater percentage of the studies showed improved HR-QOL, indicating that instrument sensitivity is an important factor in assessing weight loss treatment effects on HR-QOL [135]. Other studies on the HR-QOL of obese women who are either sedentary or physically active indicate that obese patients do exhibit mental/emotional health impairments and poorer overall HR-QOL than leaner subjects, but that physical activity, while correlating with an improved social network, is not sufficient to promote a significant change in the mental health domains of HR-QOL for these obese women [136, 137]. These results were obtained with a combination of mental health domain measures and the generic Quality of Well-Being (QWB) HR-QOL instrument in the study by Foreyt et al. [136], and with a combination of domainspecific generic measures in the study by Hulens et al. [137]. However, bariatric surgery trials in both adult and

Fig. 4 Obesity and quality of life: the effect of age on the EQ-5D questionnaire in subjects aged 65 years or older according to BMI class and age group. The upper panel shows that the EQ-5D VAS score decreased progressively in relation to BMI class in the population aged 65–74 years (P = 0.046), and not in older subjects (P = 0.310). When the EQ-5D score was recalculated with the timetrade off methodology [108] (lower panel), no systematic differences were observed in either age group (age 65–74 years, P = 0.310; age 75? years, P = 0.387) (redrawn from data from the Pianoro study [167]). BMI body mass index, EQ-5D EuroQol, VAS visual analog scale


children and adolescent populations (assessed with both generic and obesity-specific instruments) indicated that surgery-assisted weight loss resulted in HR-QOL improvements in all domains measured [98, 138, 139]. A possible explanation for the discrepancies noted in obesityreducing treatment effects on HR-QOL might be provided by the possibility that more severe forms of obesity strongly impact all domains of HR-QOL [140, 141], and for the mild-to-moderate obesity level, only some domains (physical health, role limitations) may be impacted, or may be improved with treatment [98]. Certainly, body image issues and unrealistic patient expectations of weight-loss measures seem to fuel dissatisfaction with even clinically relevant weight loss [142] and this can impact the mental/ emotional aspect of HR-QOL even more than actual BMI [143].

progressively poorer HR-QOL. However, when transformed into weighted Z-scores, the data were compatible with a progressively better HR-QOL with advancing age, with the exception of bodily pain (Fig. 2). Similar data were observed for the different domains of PGWB (not reported in detail). The scores of ORWELL-97, covering the burden of disease and the perceived relative impact on everyday life, remained high but relatively stable until the age of 55 years, and declined thereafter. Thus, while obesity had a negative effect on HR-QOL for patients over 55 years in the obesity-specific HR-QOL measure, generic instruments were not able to detect this difference. A similar lack of sensitivity and need for use of an appropriate disease and population-specific instrument was noted in a weight-loss clinical trial in the USA that compared various HR-QOL measures [145].

3.2.1 Direct Experience: The QOVADIS Study

3.3 Aging, Obesity, and HR-QOL

An epidemiological Italian survey, QUOVADIS (QUality of life in Obesity: eVAluation and DIsease Surveillance), studied the relationship of obesity to HR-QOL [144], measuring the HR-QOL, psychological distress, and eating behavior of Italian patients (20–65 years old) seeking obesity treatment at 23 clinical centers. HR-QOL was tested using the generic SF-36 questionnaire, a generic measure of psychological well-being [Psychological General Well-Being Index (PGWBI)], and an obesity-specific questionnaire [Obesity-Related Well-Being (ORWELL97)] [135]. The study showed that HR-QOL scores were significantly lower in women than in men, and a greater impairment of QOL was observed in relation to increasing BMI class, concurrent psychopathology, associated somatic diseases, binge eating, and weight cycling. Psychopathology (presence of previously diagnosed mental disorders and/or elevated scores on psychopathology assessment) was associated with lower HR-QOL scores on both psychosocial and somatic domains; somatic diseases and higher BMI, after adjustment for confounders, were associated with impairment of physical domains, while binge eating and weight cycling appeared to affect psychosocial domains only [114]. The authors concluded that psychological/psychiatric interventions are mandatory for a comprehensive treatment of obesity, to improve treatment outcomes and to reduce the burden of obesity. Although the study did not cover the elderly population, and the validity of tests is not proven in the elderly [117], the analysis by age gives an impressive idea of the age-dependent changes in HR-QOL. Almost all SF-36 domains were affected in obesity. When grouped by patient age (by decade), the scores of nearly all domains (with the notable exception of social functioning and role limitation—emotional) showed a gradual decrease with advancing age, suggesting a

While not directly observed in the QUOVADIS trial, age is indeed a strong modifier of HR-QOL. Physical health is generally negatively impacted by obesity in the elderly population and there is strong evidence of a significant link between obesity and life-threatening conditions: diabetes, heart disease, stroke, chronic obstructive pulmonary disease, chronic kidney disease, Alzheimer’s and Parkinson disease, and disability (Fig. 3) [146] are all significantly associated with and exacerbated by obesity. Obesity also significantly affects age-related disability, manifesting in increased loss of mobility, risk of falls and of developing osteoarthritis, and increased likelihood of becoming unable to perform daily self-care activities. In a recent review of mobility assessments for the older obese population, crosssectional studies (from 82 to 4,000 participants) showed poorer lower extremity mobility with increasing obesity severity in both men and women, while most longitudinal studies (covering a follow-up from 1 to 22 years) reported a significant association between adiposity and reduced mobility. Walking, stair climbing, and chair rise ability were compromised, especially in grade II–III obesity [147]. Additionally, obesity seems to play a role in falls due to poor physical fitness, greater levels of pain, and postural balance problems [148], which are exacerbated by sarcopenia [149]. Non-disabled older adults with sarcopenic obesity are more than twice as likely to report subsequent instrumental activity of daily living disability as non-obese sarcopenic individuals [34]. Finally, the high prevalence of osteoarthritis accounts for much of the disability of the lower extremities in the elderly: the common sites of osteoarthritis in obese patients are the knee, hip, and carpometacarpal joint of the hand [150]. In a survey of more than 7,000 patients in Finland, BMI was closely related to the prevalence of osteoarthritis, and the odds ratio for

F. Corica et al.

osteoarthritis was 2.8 in patients with a BMI of 35 kg/m2 compared with a BMI of 25 kg/m2 [151]. In a study of middle-aged women, it was estimated that for every 1 kg increase in body weight, the risk of osteoarthritis of the knee and carpometacarpal joint of the hand increased by 9–13 % [152]. Obesity in the elderly is associated with a significant impact on the physical health and role limitation domains of HR-QOL (assessed with generic HR-QOL instruments) [14, 75, 153–155]. In particular, the Health Status Questionnaire-12 (HSQ-12), i.e., the HR-QOL instrument used by Yan et al. [14], is considered a test with good specificity for measuring HR-QOL in older populations, particularly for those with chronic illnesses [156]. In addition, frailty and sarcopenia, the two conditions commonly linked to obesity in the elderly, are associated with lower scores on all physical and cognitive HR-QOL scales (assessed mainly with generic HR-QOL instruments, and with some old agespecific instruments) [13]. A study evaluating the effect of increased physical activity in the obese elderly population with diabetes (which is particularly affected by mobility limitations) also confirms the impact of obesity in the elderly on the physical domains of HR-QOL by noting an improvement in overall HR-QOL (measured with the generic SF-36 instrument) [157] with increased mobility. By contrast, the psychological burden of obesity is possibly attenuated by age [14, 158, 159]. Cultural preoccupation with thinness, particularly in women, and discrimination in life events, increase the burden of obese people. The stigma is fuelled by the concept that excess body weight is expression of lack of will-power and personal control [160], a belief frequently shared by healthcare professionals. Social isolation is both a cause and an effect of psychopathology; it has been associated with the risk of major depression [161] and suicidal thoughts and attempts in women (but not in men, where excess weight carried a lower risk of depression and suicidal behavior) [162]. In young persons, but not in the elderly, these problems may be exacerbated by problems with sexual functioning, associated with both impaired mood and lower QOL [163]. The triad of excess weight, body image dissatisfaction, and depression, when coupled with poor social relationships and sexual functioning, may easily explain the burden on the mental and social components of HR-QOL in young persons, and its attenuation with advancing age. In obese elderly people, although HR-QOL remains poorer than the population norm [137], the loss of HR-QOL becomes progressively smaller [159] because of the high prevalence of co-morbidities in the general population, which smoothens the differences. Differences between sexes on various HR-QOL sub-domains also narrow in older age [164], possibly due to the lower importance of body image at that life stage. However, when weight-loss efforts were

measured with the obesity-specific Impact of Weight on Quality of Life (IWQOL)-Lite instrument in an obese elderly population, the patients experienced an improvement in the overall HR-QOL, and also manifested improvements in cognitive function and mental health [165], indicating that perhaps disease-specific instruments are needed to evaluate the smaller (but still significant) changes in various HR-QOL aspects particularly relevant to the study subjects. As in the general obese patient population, elderly bariatric surgery patients noted an improvement in overall QOL 6 months and 1 year after surgery (assessed with a generic instrument that is not restricted to HR-QOL) [166]. 3.3.1 Direct Experience: The Pianoro Study A recent clinical trial (the Pianoro study) investigated the impact of physical activity in the elderly on cardiovascular events and QOL [167, 168]. A total of 1,098 people completed the pre-study procedures, which also included a physical activity assessment for the prior week (Physical Activity Scale for the Elderly [PASE], including sections for household and leisure-time activities [169]), cognitive tests, and the generic EuroQol HR-QOL questionnaire [EQ-5D-3L, which includes the visual analog scale (VAS)]. The final results of the Pianoro study indicate that the prevalence of overweight and obese people varied significantly with age (with higher values in younger groups), while the prevalence of an enlarged waist circumference did not change in relation to aging. Mobility impairment was significantly associated with an increased BMI, expressed as a BMI class or the presence of obesity, as was the presence of anxiety/depression. The EQ-5D VAS score decreased progressively in relation to BMI class in the population aged 65–74 years (P = 0.046), but not in older subjects (P = 0.310) (Fig. 4). When the EQ-5D score was recalculated with the time-trade off methodology [108], where utility values are obtained by asking respondents to ‘trade off’ a portion of their remaining lifespan for an improved health state, no systematic differences were observed in either age group. The amount of life span, higher waist circumference, and lower physical activity (expressed as low PASE global score) were nonetheless independently associated with poorer EQ-5D score. In conclusion, aging is associated with a progressive decline in HR-QOL in obesity, making overall scores largely lower than the values observed in the general population. However, these differences are progressively cancelled with advancing age. In subjects above 75 years, the impact of obesity on QOL lessens compared with that on younger aged patients. Of note, obesity grade in the old population is less severe than in the younger age groups, possibly due to reduced survival in morbidly obese subjects


[15]. In the Pianoro study, the average BMI of obese subjects (n = 196) was only 32.6 kg/m2, and only 12 and 3 % of cases belonged to obesity classes II and III, respectively.

4 Conclusion As the population is growing older, the prevalence of obesity in the elderly is rising. Aging and obesity represent important influencers of healthcare expenditures: obesity and sarcopenia, alone or combined with each other, may determine disability or functional limitations in the activities of the daily life [170], requiring continuous support by the healthcare system. An increasing obese elderly population will thus result in financial problems and is alarming for the sustainability of all national health systems, particularly the universalistic, Beveridge-type systems, and in the light of the present economic crisis. Identification of elderly subjects with sarcopenic obesity is clinically relevant, and new tests are available to define sarcopenic obesity at a glance [171]. Poor physical functioning and the reduced HR-QOL attributable to being overweight/obese are important in terms of public health. Preventive strategies should be a priority in developing a plan to manage or reduce the expected escalating costs of future interventional measures. Acknowledgments The authors thank Ana Bozas, PhD, an employee of Analysis Group, Inc., for assistance with the preparation of this manuscript. FC, GB, AC, NM, FL, and GM declare that no competing interests exist in relation to the material presented in this article. Author contributions FC, GB, AC, and GM conceived the study; FC and GM reviewed the literature and selected the most valid, relevant, and useful studies; GB provided the data from the Pianoro study; FC, AC, and GM provided the data from the QUOVADIS study; FC, GB, AC, and GM wrote the first draft of the manuscript; NM and FL critically reviewed the draft; all named authors approved the final version of the manuscript. Guarantor’s name article.

GM takes responsibility for the contents of the

References 1. United Nations. World population prospects, the 2010 revision: highlights. New York: United Nations; 2011. 2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81. 3. Jarosz PA, Bellar A. Sarcopenic obesity: an emerging cause of frailty in older adults. Geriatr Nurs. 2009;30(1):64–70.

4. Italian Cardiovascular Epidemiological Observatory. Ital Heart J. 2004;5(Suppl 3):49S–92S. 5. Wang YC, Colditz GA, Kuntz KM. Forecasting the obesity epidemic in the aging US population. Obesity. 2007;15(11):2855–65. 6. Global Health Observatory (GHO) Obesity. Situation and trends. World Health Organization, Geneva, 2013. Available at http://www.who.int/gho/ncd/risk_factors/obesity_text/en/. 7. Avorn J. Unhealthy ageing: functional and socioeconomic impact. Eur Heart J. 2001;3(Suppl N):N3–5. 8. Centers for Disease C, Prevention. The state of aging and health in America 2013. Atlanta: Centers for Disease Control and Prevention, US Dept of Health and Human Services; 2013. 9. Penn DM, Fischer JG, Sun Lee J, Hausman DB, Johnson MA. High BMI and waist circumference are associated with a high prevalence of comorbidities in older Americans Act programs in Georgia senior centers. J Nutr Health Aging. 2009;13(9):827–32. 10. Gallagher Camden S, Gates J. Obesity: changing the face of geriatric care. Ostomy Wound Manage. 2006;52(10):36–8, 40–4. 11. Villareal DT, Apovian CM, Kushner RF, Klein S, American Society for N, Naaso TOS. Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society. Obes Res. 2005;13(11):1849–63. 12. Han TS, Wu FC, Lean ME. Obesity and weight management in the elderly: a focus on men. Best Pract Res Clin Endocrinol Metab. 2013;27(4):509–25. 13. Rizzoli R, Reginster JY, Arnal JF, Bautmans I, Beaudart C, Bischoff-Ferrari H, et al. Quality of life in sarcopenia and frailty. Calcif Tissue Int. 2013;93(2):101–20. 14. Yan LL, Daviglus ML, Liu K, Pirzada A, Garside DB, Schiffer L, et al. BMI and health-related quality of life in adults 65 years and older. Obes Res. 2004;12(1):69–76. 15. Zamboni M, Mazzali G, Zoico E, Harris TB, Meigs JB, Di Francesco V, et al. Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes (Lond). 2005;29(9):1011–29. 16. Decaria JE, Sharp C, Petrella RJ. Scoping review report: obesity in older adults. Int J Obes (London). 2012;36(9):1141–50. 17. Edwards RD. Population aging, the dependency burden, and challenges facing preventive medicine. Prev Med. 2012;55(6):533–4. 18. Yang Z, Hall AG. The financial burden of overweight and obesity among elderly Americans: the dynamics of weight, longevity, and health care cost. Health Serv Res. 2008;43(3):849–68. 19. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282(16):1523–9. 20. Pi-Sunyer X. The medical risks of obesity. Postgrad Med. 2009;121(6):21–33. 21. Peeters A, Barendregt JJ, Willekens F, Mackenbach JP, Al Mamun A, Bonneux L, et al. Obesity in adulthood and its consequences for life expectancy: a life-table analysis. Ann Intern Med. 2003;138(1):24–32. 22. Dee A, Kearns K, O’Neill C, Sharp L, Staines A, O’Dwyer V, et al. The direct and indirect costs of both overweight and obesity: a systematic review. BMC Res Notes. 2014;7(1):242. 23. Ezzati M, Vander Hoorn S, Lawes CM, Leach R, James WP, Lopez AD, et al. Rethinking the ‘‘diseases of affluence’’ paradigm: global patterns of nutritional risks in relation to economic development. PLoS Med. 2005;2(5):e133. 24. Muller-Riemenschneider F, Reinhold T, Berghofer A, Willich SN. Health-economic burden of obesity in Europe. Eur J Epidemiol. 2008;23(8):499–509.

F. Corica et al. 25. Withrow D, Alter DA. The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obes Rev. 2011;12(2):131–41. 26. Pulgaron ER. Childhood obesity: a review of increased risk for physical and psychological comorbidities. Clin Ther. 2013;35(1):A18–32. 27. Evans JG. Ageing and disease. Ciba Found Symp. 1988;134:38–57. 28. Mathus-Vliegen EM. Obesity and the elderly. J Clin Gastroenterol. 2012;46(7):533–44. 29. Dominguez LJ, Barbagallo M. The cardiometabolic syndrome and sarcopenic obesity in older persons. J Cardiometab Syndr. 2007;2(3):183–9. 30. Zamboni M, Mazzali G, Fantin F, Rossi A, Di Francesco V. Sarcopenic obesity: a new category of obesity in the elderly. Nutr Metab Cardiovasc Dis. 2008;18(5):388–95. 31. Roubenoff R. Sarcopenic obesity: the confluence of two epidemics. Obes Res. 2004;12(6):887–8. 32. Miller SL, Wolfe RR. The danger of weight loss in the elderly. J Nutr Health Aging. 2008;12(7):487–91. 33. Han TS, Tajar A, Lean ME. Obesity and weight management in the elderly. Br Med Bull. 2011;97:169–96. 34. Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, Morley JE. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res. 2004;12(12):1995–2004. 35. Lee S, Kim TN, Kim SH. Sarcopenic obesity is more closely associated with knee osteoarthritis than is nonsarcopenic obesity: a cross-sectional study. Arthritis Rheum. 2012;64(12):3947–54. 36. Barzilai N, Huffman DM, Muzumdar RH, Bartke A. The critical role of metabolic pathways in aging. Diabetes. 2012;61(6):1315–22. 37. Baumgartner RN, Stauber PM, McHugh D, Koehler KM, Garry PJ. Cross-sectional age differences in body composition in persons 60? years of age. J Gerontol A Biol Sci Med Sci. 1995;50(6):M307–16. 38. Gallagher D, Visser M, De Meersman RE, Sepulveda D, Baumgartner RN, Pierson RN, et al. Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. J Appl Physiol (1985). 1997;83(1):229–39. 39. Flynn MA, Nolph GB, Baker AS, Martin WM, Krause G. Total body potassium in aging humans: a longitudinal study. Am J Clin Nutr. 1989;50(4):713–7. 40. Muller DC, Elahi D, Tobin JD, Andres R. The effect of age on insulin resistance and secretion: a review. Semin Nephrol. 1996;16(4):289–98. 41. Elia M, Ritz P, Stubbs RJ. Total energy expenditure in the elderly. Eur J Clin Nutr. 2000;54(Suppl 3):S92–103. 42. Kennedy RL, Chokkalingham K, Srinivasan R. Obesity in the elderly: who should we be treating, and why, and how? Curr Opin Clin Nutr Metab Care. 2004;7(1):3–9. 43. Kennedy RL, Malabu U, Kazi M, Shahsidhar V. Management of obesity in the elderly: too much and too late? J Nutr Health Aging. 2008;12(9):608–21. 44. Nomura T, Ikeda Y, Nakao S, Ito K, Ishida K, Suehiro T, et al. Muscle strength is a marker of insulin resistance in patients with type 2 diabetes: a pilot study. Endocr J. 2007;54(5):791–6. 45. Abbatecola AM, Ferrucci L, Ceda G, Russo CR, Lauretani F, Bandinelli S, et al. Insulin resistance and muscle strength in older persons. J Gerontol A Biol Sci Med Sci. 2005;60(10):1278–82. 46. Guillet C, Boirie Y. Insulin resistance: a contributing factor to age-related muscle mass loss? Diabetes Metab. 2005;31(Spec No 2):5S20–6. 47. Roubenoff R. Sarcopenic obesity: does muscle loss cause fat gain? Lessons from rheumatoid arthritis and osteoarthritis. Ann N Y Acad Sci. 2000;904:553–7.

48. Beaufrere B, Morio B. Fat and protein redistribution with aging: metabolic considerations. Eur J Clin Nutr. 2000;54(Suppl 3):S48–53. 49. Cree MG, Newcomer BR, Katsanos CS, Sheffield-Moore M, Chinkes D, Aarsland A, et al. Intramuscular and liver triglycerides are increased in the elderly. J Clin Endocrinol Metab. 2004;89(8):3864–71. 50. Jensen GL. Inflammation: roles in aging and sarcopenia. JPEN J Parenter Enteral Nutr. 2008;32(6):656–9. 51. Cesari M, Pahor M. Target population for clinical trials on sarcopenia. J Nutr Health Aging. 2008;12(7):470–8. 52. Waters DL, Baumgartner RN. Sarcopenia and obesity. Clin Geriatr Med. 2011;27(3):401–21. 53. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–23. 54. Frontera WR, Hughes VA, Fielding RA, Fiatarone MA, Evans WJ, Roubenoff R. Aging of skeletal muscle: a 12-yr longitudinal study. J Appl Physiol (1985). 2000;88(4):1321–6. 55. Lexell J, Henriksson-Larsen K, Winblad B, Sjostrom M. Distribution of different fiber types in human skeletal muscles: effects of aging studied in whole muscle cross sections. Muscle Nerve. 1983;6(8):588–95. 56. Binkley N, Buehring B. Beyond FRAX: it’s time to consider ‘‘sarco-osteopenia’’. J Clin Densitom. 2009;12(4):413–6. 57. Ormsbee MJ, Prado CM, Ilich JZ, Purcell S, Siervo M, Folsom A, et al. Osteosarcopenic obesity: the role of bone, muscle, and fat on health. J Cachexia Sarcopenia Muscle. 2014;5(3):183–92. 58. Zhao LJ, Jiang H, Papasian CJ, Maulik D, Drees B, Hamilton J, et al. Correlation of obesity and osteoporosis: effect of fat mass on the determination of osteoporosis. J Bone Miner Res. 2008;23(1):17–29. 59. Nielson CM, Marshall LM, Adams AL, LeBlanc ES, Cawthon PM, Ensrud K, et al. BMI and fracture risk in older men: the osteoporotic fractures in men study (MrOS). J Bone Miner Res. 2011;26(3):496–502. 60. Premaor MO, Pilbrow L, Tonkin C, Parker RA, Compston J. Obesity and fractures in postmenopausal women. J Bone Miner Res. 2010;25(2):292–7. 61. De Laet C, Kanis JA, Oden A, Johanson H, Johnell O, Delmas P, et al. Body mass index as a predictor of fracture risk: a metaanalysis. Osteoporo Int. 2005;16(11):1330–8. 62. Wortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF. Decreased bioavailability of vitamin D in obesity. Am J Clin Nutr. 2000;72(3):690–3. 63. Pitroda AP, Harris SS, Dawson-Hughes B. The association of adiposity with parathyroid hormone in healthy older adults. Endocrine. 2009;36(2):218–23. 64. Guri AJ, Bassaganya-Riera J. Systemic effects of white adipose tissue dysregulation and obesity-related inflammation. Obesity. 2011;19(4):689–700. 65. DeMaria EJ, Sugerman HJ, Meador JG, Doty JM, Kellum JM, Wolfe L, et al. High failure rate after laparoscopic adjustable silicone gastric banding for treatment of morbid obesity. Ann Surg. 2001;233(6):809–18. 66. Caffarelli C, Alessi C, Nuti R, Gonnelli S. Divergent effects of obesity on fragility fractures. Clin Interv Aging. 2014;9:1629–36. 67. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752–62. 68. Walston J, Hadley EC, Ferrucci L, Guralnik JM, Newman AB, Studenski SA. Research agenda for frailty in older adults: toward a better understanding of physiology and etiology: summary from the American Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults. J Am Geriatr Soc. 2006;54(6):991–1001.

Obesity, Aging, and Quality of Life 69. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56. 70. Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007;62(7):738–43. 71. Tas U, Verhagen AP, Bierma-Zeinstra SM, Odding E, Koes BW. Prognostic factors of disability in older people: a systematic review. Br J Gen Pract. 2007;57(537):319–23. 72. Roubenoff R. The pathophysiology of wasting in the elderly. J Nutr. 1999;129(1S Suppl):256S–9S. 73. Sakuma K, Yamaguchi A. Sarcopenic obesity and endocrinal adaptation with age. Int J Endocrinol. 2013;2013:204164. 74. Florez H, Troen BR. Fat and inflammaging: a dual path to unfitness in elderly people? J Am Geriatr Soc. 2008;56(3):558–60. 75. Villareal DT, Banks M, Siener C, Sinacore DR, Klein S. Physical frailty and body composition in obese elderly men and women. Obes Res. 2004;12(6):913–20. 76. McCarthy LH, Bigal ME, Katz M, Derby C, Lipton RB. Chronic pain and obesity in elderly people: results from the Einstein aging study. J Am Geriatr Soc. 2009;57(1):115–9. 77. Woods NF, LaCroix AZ, Gray SL, Aragaki A, Cochrane BB, Brunner RL, et al. Frailty: emergence and consequences in women aged 65 and older in the Women’s Health Initiative Observational Study. J Am Geriatr Soc. 2005;53(8):1321–30. 78. Strandberg TE, Sirola J, Pitkala KH, Tilvis RS, Strandberg AY, Stenholm S. Association of midlife obesity and cardiovascular risk with old age frailty: a 26-year follow-up of initially healthy men. Int J Obes (Lond). 2012;36(9):1153–7. 79. Stenholm S, Strandberg TE, Pitkala K, Sainio P, Heliovaara M, Koskinen S. Midlife obesity and risk of frailty in old age during a 22-year follow-up in men and women: the Mini-Finland Follow-up Survey. J Gerontol A Biol Sci Med Sci. 2014;69(1):73–8. 80. Stessman J, Jacobs JM, Ein-Mor E, Bursztyn M. Normal body mass index rather than obesity predicts greater mortality in elderly people: the Jerusalem longitudinal study. J Am Geriatr Soc. 2009;57(12):2232–8. 81. Habbu A, Lakkis NM, Dokainish H. The obesity paradox: fact or fiction? Am J Cardiol. 2006;98(7):944–8. 82. Oreopoulos A, Kalantar-Zadeh K, Sharma AM, Fonarow GC. The obesity paradox in the elderly: potential mechanisms and clinical implications. Clin Geriatr Med. 2009;25(4):643–59, viii. 83. Hong NS, Kim KS, Lee IK, Lind PM, Lind L, Jacobs DR, et al. The association between obesity and mortality in the elderly differs by serum concentrations of persistent organic pollutants: a possible explanation for the obesity paradox. Int J Obes (Lond). 2012;36(9):1170–5. 84. Masters RK, Powers DA, Link BG. Obesity and US mortality risk over the adult life course. Am J Epidemiol. 2013;177(5):431–42. 85. Amador LF, Al Snih S, Markides KS, Goodwin JS. Weight change and mortality among older Mexican Americans. Aging Clin Exp Res. 2006;18(3):196–204. 86. Newman AB, Yanez D, Harris T, Duxbury A, Enright PL, Fried LP, et al. Weight change in old age and its association with mortality. J Am Geriatr Soc. 2001;49(10):1309–18. 87. Gregg EW, Gerzoff RB, Thompson TJ, Williamson DF. Intentional weight loss and death in overweight and obese US adults 35 years of age and older. Ann Intern Med. 2003;138(5):383–9. 88. Gregg EW, Gerzoff RB, Thompson TJ, Williamson DF. Trying to lose weight, losing weight, and 9-year mortality in overweight US adults with diabetes. Diabetes care. 2004;27(3):657–62.

89. Harrington M, Gibson S, Cottrell RC. A review and meta-analysis of the effect of weight loss on all-cause mortality risk. Nutr Res Rev. 2009;22(1):93–108. 90. Peters ET, Seidell JC, Menotti A, Arayanis C, Dontas A, Fidanza F, et al. Changes in body weight in relation to mortality in 6,441 European middle-aged men: the Seven Countries Study. Int J Obes (London). 1995;19(12):862–8. 91. Ford DW, Hartman TJ, Still C, Wood C, Mitchell DC, Erickson P, et al. Body mass index, poor diet quality, and health-related quality of life are associated with mortality in rural older adults. J Nutr Gerontol Ger. 2014;33(1):23–34. 92. Landi F, Onder G, Gambassi G, Pedone C, Carbonin P, Bernabei R. Body mass index and mortality among hospitalized patients. Arch Intern Med. 2000;160(17):2641–4. 93. Testa MA, Simonson DC. Assesment of quality-of-life outcomes. N Engl J Med. 1996;334(13):835–40. 94. Ware JE Jr. Conceptualization and measurement of healthrelated quality of life: comments on an evolving field. Arch Phys Med Rehabil. 2003;84(4 Suppl 2):S43–51. 95. Wadden TA, Phelan S. Assessment of quality of life in obese individuals. Obes Res. 2002;10 Suppl 1:50S–7S. 96. National Institutes of Health, National Heart Lung and Blood Institute, North American Association for the Study of Obesity. The practical guide: identification, evaluation, and treatment of overweight and obesity in adults. Bethesda: National Institutes of Health; 2000. 97. Stunkard AJ, Wadden TA. Psychological aspects of severe obesity. Am J Clin Nutr. 1992;55(2 Suppl):524S–32S. 98. Fontaine KR, Barofsky I. Obesity and health-related quality of life. Obes Rev. 2001;2(3):173–82. 99. Guyatt GH, Bombardier C, Tugwell PX. Measuring diseasespecific quality of life in clinical trials. CMAJ. 1986;134(8):889–95. 100. Guyatt GH, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Intern Med. 1993;118(8):622–9. 101. Guyatt GH, Veldhuyzen Van Zanten SJ, Feeny DH, Patrick DL. Measuring quality of life in clinical trials: a taxonomy and review. CMAJ. 1989;140(12):1441–8. 102. Le Pen C, Levy E, Loos F, Banzet MN, Basdevant A. ‘‘Specific’’ scale compared with ‘‘generic’’ scale: a double measurement of the quality of life in a French community sample of obese subjects. J Epidemiol Community Health. 1998;52(7):445–50. 103. Duval K, Marceau P, Perusse L, Lacasse Y. An overview of obesity-specific quality of life questionnaires. Obes Rev. 2006;7(4):347–60. 104. Sach TH, Barton GR, Doherty M, Muir KR, Jenkinson C, Avery AJ. The relationship between body mass index and healthrelated quality of life: comparing the EQ-5D, EuroQol VAS and SF-6D. Int J Obes (London). 2007;31(1):189–96. 105. Stucki A, Borchers M, Stucki G, Cieza A, Amann E, Ruof J. Content comparison of health status measures for obesity based on the international classification of functioning, disability and health. Int J Obes (London). 2006;30(12):1791–9. 106. Tayyem R, Ali A, Atkinson J, Martin CR. Analysis of healthrelated quality-of-life instruments measuring the impact of bariatric surgery: systematic review of the instruments used and their content validity. Patient. 2011;4(2):73–87. 107. Ware JE Jr. Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473–83. 108. Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001;33(5):337–43. 109. Hunt SM, McKenna SP, McEwen J, Williams J, Papp E. The Nottingham Health Profile: subjective health status and medical consultations. Soc Sci Med A. 1981;15(3 Pt 1):221–9.

F. Corica et al. 110. Dupuy HJ. The psychological general well-being (PGWB) inventory. In: Wenger NK, editor. Assessment of quality of life in clinical trials of cardiovascular therapies. New York: Le Jacq Publications; 1984. p. 170–83. 111. Ware JE Jr, Kosinski M, Gandek B, Aaronson NK, Apolone G, Bech P, et al. The factor structure of the SF-36 Health Survey in 10 countries: results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol. 1998;51(11):1159–65. 112. Chassany O, Dimena¨s E, Dubois D, Wu A. The Psychological General Well-Being Index (PGWBI) User Manual. Accessed 11 Oct 2014. http://178.23.156.107:8085/Instruments_files/ USERS/pgwbi.pdf 113. Corica F, Corsonello A, Apolone G, Mannucci E, Lucchetti M, Bonfiglio C, et al. Metabolic syndrome, psychological status and quality of life in obesity: the QUOVADIS Study. Int J Obes (Lond). 2008;32(1):185–91. 114. Mannucci E, Petroni ML, Villanova N, Rotella CM, Apolone G, Marchesini G. Clinical and psychological correlates of healthrelated quality of life in obese patients. Health Qual Life Outcomes. 2010;8:90. 115. Therrien F, Marceau P, Turgeon N, Biron S, Richard D, Lacasse Y. The Laval Questionnaire: a new instrument to measure quality of life in morbid obesity. Health Qual Life Outcomes. 2011;9:66. 116. Andresen EM, Rothenberg BM, Panzer R, Katz P, McDermott MP. Selecting a generic measure of health-related quality of life for use among older adults. A comparison of candidate instruments. Eval Health Prof. 1998;21(2):244–64. 117. Hickey A, Barker M, McGee H, O’Boyle C. Measuring healthrelated quality of life in older patient populations: a review of current approaches. Pharmacoeconomics. 2005;23(10):971–93. 118. Bowling A. The psychometric properties of the older people’s quality of life questionnaire, compared with the CASP-19 and the WHOQOL-OLD. Curr Gerontol Geriatr Res. 2009;2009:298950. 119. Buttitta M, Iliescu C, Rousseau A, Guerrien A. Quality of life in overweight and obese children and adolescents: a literature review. Qual Life Res. 2014;23(4):1117–39. 120. Larsson U, Karlsson J, Sullivan M. Impact of overweight and obesity on health-related quality of life—a Swedish population study. Int J Obes Relat Metab Disord. 2002;26(3):417–24. 121. 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. 122. Ul-Haq Z, Mackay DF, Fenwick E, Pell JP. Meta-analysis of the association between body mass index and health-related quality of life among children and adolescents, assessed using the pediatric quality of life inventory index. J Pediatr. 2013;162(2):280–6.e1. 123. Caixas A, Lecube A, Morales MJ, Calanas A, Moreiro J, Cordido F, et al. Weight-related quality of life in Spanish obese subjects suitable for bariatric surgery is lower than in their North American counterparts: a case-control study. Obes Surg. 2013;23(4):509–14. 124. McDonough C, Dunkley AJ, Aujla N, Morris D, Davies MJ, Khunti K. The association between body mass index and healthrelated quality of life: influence of ethnicity on this relationship. Diabetes Obes Metab. 2013;15(4):342–8. 125. Hopman WM, Berger C, Joseph L, Barr SI, Gao Y, Prior JC, et al. The association between body mass index and healthrelated quality of life: data from CaMos, a stratified population study. Qual Life Res. 2007;16(10):1595–603. 126. Song HR, Park HS, Yun KE, Cho SH, Choi EY, Lee SY, et al. Gender and age differences in the impact of overweight on

127.

128.

129.

130.

131.

132.

133.

134.

135.

136.

137.

138.

139.

140.

141.

142.

143.

obesity-related quality of life among Korean adults. Obes Res Clin Pract. 2010;4(1):e1–e82. Jia H, Lubetkin EI. The impact of obesity on health-related quality-of-life in the general adult US population. J Public Health (Oxf). 2005;27(2):156–64. Seidell JC, Bakx KC, Deurenberg P, Burema J, Hautvast JG, Huygen FJ. The relation between overweight and subjective health according to age, social class, slimming behavior and smoking habits in Dutch adults. Am J Public Health. 1986;76(12):1410–5. Marchesini G, Bellini M, Natale S, Belsito C, Isacco S, Nuccitelli C, et al. Psychiatric distress and health-related quality of life in obesity. Diabetes Nutr Metab. 2003;16(3):145–54. Marchesini G, Solaroli E, Baraldi L, Natale S, Migliorini S, Visani F, et al. Health-related quality of life in obesity: the role of eating behaviour. Diabetes Nutr Metab. 2000;13(3):156–64. Marano G, Cuzzolaro M, Vetrone G, Calugi S, Dalle Grave R, Marchesini G, et al. Further validation study of the Body Uneasiness Test (BUT) in a clinical sample of 1,922 adult obese subjects. Eat Weight Disord. 2007;12(2):70–82. Marchesini G, Natale S, Tiraferri F, Tartaglia A, Moscatiello S, MarchesiniReggiani L, et al. The burden of obesity on everyday life: a role for osteoarticular and respiratory diseases. Diabetes Nutr Metab. 2003;16(5–6):284–90. Ul-Haq Z, Mackay DF, Fenwick E, Pell JP. Association between body mass index and mental health among Scottish adult population: a cross-sectional study of 37,272 participants. Psychol Med. 2013;25:1–10. Warkentin LM, Das D, Majumdar SR, Johnson JA, Padwal RS. The effect of weight loss on health-related quality of life: systematic review and meta-analysis of randomized trials. Obes Rev. 2014;15(3):169–82. Maciejewski ML, Patrick DL, Williamson DF. A structured review of randomized controlled trials of weight loss showed little improvement in health-related quality of life. J Clin Epidemiol. 2005;58(6):568–78. Foreyt JP, Brunner RL, Goodrick GK, St Jeor ST, Miller GD. Psychological correlates of reported physical activity in normalweight and obese adults: the Reno diet-heart study. Int J Obes Rel Metab Dis. 1995;19(Suppl 4):S69–72. Hulens M, Vansant G, Claessens AL, Lysens R, Muls E, Rzewnicki R. Health-related quality of life in physically active and sedentary obese women. Am J Hum Biol. 2002;14(6):777–85. Black JA, White B, Viner RM, Simmons RK. Bariatric surgery for obese children and adolescents: a systematic review and meta-analysis. Obes Rev. 2013;14(8):634–44. McEwen LN, Coelho RB, Baumann LM, Bilik D, Nota-Kirby B, Herman WH. The cost, quality of life impact, and cost-utility of bariatric surgery in a managed care population. Obes Surg. 2010;20(7):919–28. Chang CY, Hung CK, Chang YY, Tai CM, Lin JT, Wang JD. Health-related quality of life in adult patients with morbid obesity coming for bariatric surgery. Obes Surg. 2010;20(8):1121–7. Poves Prim I, Macias GJ, Cabrera Fraga M, Situ L, Ballesta Lopez C. Quality of life in morbid obesity. Rev Esp Enferm Dig. 2005;97(3):187–95. Foster GD, Wadden TA, Phelan S, Sarwer DB, Sanderson RS. Obese patients’ perceptions of treatment outcomes and the factors that influence them. Arch Intern Med. 2001;161(17):2133–9. Herman KM, Hopman WM, Rosenberg MW. Self-rated health and life satisfaction among Canadian adults: associations of perceived weight status versus BMI. Qual Life Res. 2013;22(10):2693–705.

Obesity, Aging, and Quality of Life 144. Melchionda N, Marchesini G, Apolone G, Cuzzolaro M, Mannucci E, Grossi E, et al. The QUOVADIS Study: features of obese Italian patients seeking treatment at specialist centers. Diabetes Nutr Metab. 2003;16(2):115–24. 145. Kolotkin RL, Norquist JM, Crosby RD, Suryawanshi S, Teixeira PJ, Heymsfield SB, et al. One-year health-related quality of life outcomes in weight loss trial participants: comparison of three measures. Health Qual Life Outcomes. 2009;7:53. 146. Wang Y, Beydoun MA. The obesity epidemic in the United States—gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28. 147. Vincent HK, Vincent KR, Lamb KM. Obesity and mobility disability in the older adult. Obes Rev. 2010;11(8):568–79. 148. Reynolds SL, McIlvane JM. The impact of obesity and arthritis on active life expectancy in older Americans. Obesity. 2009;17(2):363–9. 149. Sharkey JR, Ory MG, Branch LG. Severe elder obesity and 1-year diminished lower extremity physical performance in homebound older adults. J Am Geriatr Soc. 2006;54(9):1407–13. 150. Berenbaum F, Eymard F, Houard X. Osteoarthritis, inflammation and obesity. Curr Opin Rheumatol. 2013;25(1):114–8. 151. Heliovaara M, Makela M, Impivaara O, Knekt P, Aromaa A, Sievers K. Association of overweight, trauma and workload with coxarthrosis. A health survey of 7,217 persons. Acta Orthop Scand. 1993;64(5):513–8. 152. Cicuttini FM, Baker JR, Spector TD. The association of obesity with osteoarthritis of the hand and knee in women: a twin study. J Rheumatol. 1996;23(7):1221–6. 153. Giuli C, Papa R, Bevilacqua R, Felici E, Gagliardi C, Marcellini F, et al. Correlates of perceived health related quality of life in obese, overweight and normal weight older adults: an observational study. BMC Publ Health. 2014;14:35. 154. Goins RT, Spencer SM, Krummel DA. Effect of obesity on health-related quality of life among Appalachian elderly. South Med J. 2003;96(6):552–7. 155. Groessl EJ, Kaplan RM, Barrett-Connor E, Ganiats TG. Body mass index and quality of well-being in a community of older adults. Am J Prev Med. 2004;26(2):126–9. 156. Bowling A, Windsor J. Discriminative power of the health status questionnaire 12 in relation to age, sex, and longstanding illness: findings from a survey of households in Great Britain. J Epidemiol Community Health. 1997;51(5):564–73. 157. Seshadri KG, Ananthakrishnan V, Tamilselvan B, Amarabalan R, Kumar RN. Effect of mild physical activity in obese and elderly women with type 2 diabetes. Indian J Endocrinol Metab. 2012;16(Suppl 2):S453–4. 158. Bottone FG Jr, Hawkins K, Musich S, Cheng Y, Ozminkowski RJ, Migliori RJ, et al. The relationship between body mass index and quality of life in community-living older adults living in the United States. J Nutr Health Aging. 2013;17(6):495–501. 159. Huang IC, Frangakis C, Wu AW. The relationship of excess body weight and health-related quality of life: evidence from a population study in Taiwan. Int J Obes (London). 2006;30(8):1250–9. 160. Sikorski C, Luppa M, Kaiser M, Glaesmer H, Schomerus G, Konig HH, et al. The stigma of obesity in the general public and its implications for public health—a systematic review. BMC Publ Health. 2011;11:661. 161. Onyike CU, Crum RM, Lee HB, Lyketsos CG, Eaton WW. Is obesity associated with major depression? Results from the Third National Health and Nutrition Examination Survey. Am J Epidemiol. 2003;158(12):1139–47. 162. Carpenter KM, Hasin DS, Allison DB, Faith MS. Relationships between obesity and DSM-IV major depressive disorder, suicide

163.

164.

165.

166.

167.

168.

169.

170.

171.

172.

173. 174.

175.

176.

177.

178.

179.

180.

181.

ideation, and suicide attempts: results from a general population study. Am J Public Health. 2000;90(2):251–7. Laumann EO, Paik A, Rosen RC. Sexual dysfunction in the United States: prevalence and predictors. JAMA. 1999;281(6):537–44. Guallar-Castillon P, Sendino AR, Banegas JR, Lopez-Garcia E, Rodriguez-Artalejo F. Differences in quality of life between women and men in the older population of Spain. Soc Sci Med. 2005;60(6):1229–40. Napoli N, Shah K, Waters DL, Sinacore DR, Qualls C, Villareal DT. Effect of weight loss, exercise, or both on cognition and quality of life in obese older adults. Am J Clin Nutr. 2014;100(1):189–98. O’Keefe KL, Kemmeter PR, Kemmeter KD. Bariatric surgery outcomes in patients aged 65 years and older at an American Society for Metabolic and Bariatric Surgery Center of Excellence. Obes Surg. 2010;20(9):1199–205. Bianchi G, Rossi V, Muscari A, Magalotti D, Zoli M, Pianoro Study Group. Physical activity is negatively associated with the metabolic syndrome in the elderly. QJM. 2008;101(9):713–21. Muscari A, Giannoni C, Pierpaoli L, Berzigotti A, Maietta P, Foschi E, et al. Chronic endurance exercise training prevents aging-related cognitive decline in healthy older adults: a randomized controlled trial. Int J Geriatr Psychiatry. 2010;25(10):1055–64. Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46(2):153–62. Motl RW, McAuley E. Physical activity, disability, and quality of life in older adults. Phys Med Rehabil Clin N Am. 2010;21(2):299–308. Donini LM, Ricciardi LM, Neri B, Lenzi A, Marchesini G. Risk of malnutrition (over and under-nutrition): validation of the JaNuS screening tool. Clin Nutr. Epub 2013 Dec 14. Fryback DG, Dunham NC, Palta M, Hanmer J, Buechner J, Cherepanov D, et al. US norms for six generic health-related quality-of-life indexes from the National Health Measurement study. Med Care. 2007;45(12):1162–70. Hays RD, Sherbourne CD, Mazel RM. The RAND 36-Item Health Survey 1.0. Health Econ. 1993;2(3):217–27. VanderZee KI, Sanderman R, Heyink J. A comparison of two multidimensional measures of health status: the Nottingham Health Profile and the RAND 36-Item Health Survey 1.0. Qual Life Res. 1996;5(1):165–74. Barry TL, Kaiser KL, Atwood JR. Reliability, validity, and scoring of the Health Status Questionnaire-12 version 2.0. J Nurs Meas. 2007;15(1):24–35. Brazier J, Jones N, Kind P. Testing the validity of the Euroqol and comparing it with the SF-36 health survey questionnaire. Qual Life Res. 1993;2(3):169–80. Gudex C, Dolan P, Kind P, Williams A. Health state valuations from the general public using the visual analogue scale. Qual Life Res. 1996;5(6):521–31. Pickard AS, Neary MP, Cella D. Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer. Health Qual Life Outcomes. 2007;5:70. Tran BX, Ohinmaa A, Nguyen LT. Quality of life profile and psychometric properties of the EQ-5D-5L in HIV/AIDS patients. Health Qual Life Outcomes. 2012;10(1):132. Kaplan RM, Ganiats TG, Sieber WJ, Anderson JP. The Quality of Well-Being Scale: critical similarities and differences with SF-36. Int J Qual Health Care. 1998;10(6):509–20. Sarkin AJ, Groessl EJ, Carlson JA, Tally SR, Kaplan RM, Sieber WJ, et al. Development and validation of a mental health subscale from the Quality of Well-Being Self-Administered. Qual Life Res. 2013;22(7):1685–96.

F. Corica et al. 182. Centers for Disease C, Prevention. Measuring healthy days: population assessment of health-related quality of life. Atlanta: Centers for Disease Control and Prevention; 2000. Report no.: 7704885464. 183. Forhan M, Vrkljan B, MacDermid J. A systematic review of the quality of psychometric evidence supporting the use of an obesity-specific quality of life measure for use with persons who have class III obesity. Obesity Rev. 2010;11(3):222–8. 184. Niero M, Martin M, Finger T, Lucas R, Mear I, Wild D, et al. A new approach to multicultural item generation in the development of two obesity-specific measures: the obesity and weight loss quality of life (OWLQOL) questionnaire and the weightrelated symptom measure (WRSM). Clin Ther. 2002;24(4):690–700. 185. Patrick DL, Bushnell DM, Rothman M. Performance of two self-report measures for evaluating obesity and weight loss. Obes Res. 2004;12(1):48–57. 186. Mathias SD, Williamson CL, Colwell HH, Cisternas MG, Pasta DJ, Stolshek BS, et al. Assessing health-related quality-of-life and health state preference in persons with obesity: a validation study. Qual Life Res. 1997;6(4):311–22. 187. Moorehead MK, Ardelt-Gattinger E, Lechner H, Oria HE. The validation of the Moorehead-Ardelt quality of life questionnaire II. Obes Surg. 2003;13(5):684–92. 188. Butler GS, Vallis TM, Perey B, Veldhuyzen van Zanten SJ, MacDonald AS, Konok G. The obesity adjustment survey: development of a scale to assess psychological adjustment to morbid obesity. Int J Obes Rel Metab Dis. 1999;23(5):505–11. 189. Oria HE, Moorehead MK. Updated bariatric analysis and reporting outcome system (BAROS). Surg Obes Relat Dis. 2009;5(1):60–6.

190. Mannucci E, Ricca V, Barciulli E, Di Bernardo M, Travaglini R, Cabras PL, et al. Quality of life and overweight: the obesity related well-being (Orwell 97) questionnaire. Addict Behav. 1999;24(3):345–57. 191. Karlsson J, Taft C, Sjostrom L, Torgerson JS, Sullivan M. Psychosocial functioning in the obese before and after weight reduction: construct validity and responsiveness of the obesityrelated problems scale. Int J Obes Rel Metab Dis. 2003;27(5):617–30. 192. Ziegler O, Filipecki J, Girod I, Guillemin F. Development and validation of a French obesity-specific quality of life questionnaire: quality of life, obesity and dietetics (QOLOD) rating scale. Diabetes Metab. 2005;31(3 Pt 1):273–83. 193. Power M, Quinn K, Schmidt S, Group W-O. Development of the WHOQOL-old module. Qual Life Res. 2005;14(10):2197–214. 194. Bowling A, Stenner P. Which measure of quality of life performs best in older age? A comparison of the OPQOL, CASP-19 and WHOQOL-OLD. J Epidemiol Community Health. 2011;65(3):273–80. 195. Hyde M, Wiggins RD, Higgs P, Blane DB. A measure of quality of life in early old age: the theory, development and properties of a needs satisfaction model (CASP-19). Aging Ment Health. 2003;7(3):186–94. 196. Wee CC, Davis RB, Hamel MB. Comparing the SF-12 and SF36 health status questionnaires in patients with and without obesity. Health Qual Life Outcomes. 2008;6:11. 197. Revicki DA, Osoba D, Fairclough D, Barofsky I, Berzon R, Leidy NK, et al. Recommendations on health-related quality of life research to support labeling and promotional claims in the United States. Qual Life Res. 2000;9(8):887–900.

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