SEARCH

SEARCH BY CITATION

Keywords:

  • geriatric obesity;
  • BMI;
  • elderly;
  • mental health;
  • physical functioning

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Objective: To examine relationships of BMI with health-related quality of life in adults 65 years and older.

Research Methods and Procedures: In 1996, a health survey was mailed to all surviving participants ≥ 65 years old from the Chicago Heart Association Detection Project in Industry Study (1967 to 1973). The response rate was 60%, and the sample included 3981 male and 3099 female respondents. BMI (kilograms per meter squared) was classified into four groups: underweight (<18.5), normal weight (18.5 to 24.9), overweight (25.0 to 29.9), and obese (≥30.0). Main outcome measures were Health Status Questionnaire-12 scores (ranging from 0 to 100) assessing eight domains: health perception, physical functioning, role limitations-physical, bodily pain, energy/fatigue, social functioning, role limitations-mental, and mental health. The higher the score, the better the outcome.

Results: With adjustment for age, race, education, smoking, and alcohol intake, obesity was associated with lower health perception and poorer physical and social functioning (women only) but not impaired mental health. Overweight was associated with impaired physical well-being among women only. Both underweight men and women reported impairment in physical, social, and mental well-being. For example, multivariable-adjusted health perception domain scores for women were 50.8 (underweight), 62.7 (normal weight), 60.5 (overweight), and 52.1 (obese), respectively. Associations weakened but remained significant with further adjustment for comorbidities.

Discussion: Compared with normal-weight people, both underweight and obese older adults reported impaired quality of life, particularly worse physical functioning and physical well-being. These results reinforce the importance of normal body weight in older age.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Obesity is a major public health problem due to its increasing prevalence (1) and its associations with higher morbidity and mortality from multiple diseases (2, 3). Many cross-sectional studies have also documented that obesity is related to impaired physical functioning—one aspect of health-related quality of life (HRQoL)1 (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16). However, previous findings on obesity and social well-being and mental health, two other aspects of HRQoL, have been inconsistent (5, 7, 8, 11). Although some studies have shown that obese persons have impaired social and mental well-being (7, 11), others reported no such associations (5, 8). It is also not clear whether observed associations of obesity with HRQoL are independent of co-existing chronic diseases (7, 8, 16), important determinants of HRQoL (17). Furthermore, most studies on overweight and obesity and quality of life have relied on data from young, middle-aged, or mixed age group populations. Research on persons 65 years of age and older (65+) is limited and has examined only physical functioning (13, 14), except one recent study of a Spanish population 60 years of age and older that included all aspects of HRQoL (15).

At present, ∼7% of the world population is 65+, and it is projected to rise to 12% by 2030, and in the U.S., it is projected to rise from 12% (35 million) to 20% (71 million) (18). In this era of population aging, the quality of life of older adults is of particular importance at both the societal and individual levels. We investigated, among men and women ages 65+ in the U.S., the cross-sectional associations of BMI with all three aspects of HRQoL (physical, social, and mental well-being) and studied whether any associations can be explained by presence of chronic conditions.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Study Population

The Chicago Heart Association Detection Project in Industry Study cohort was established in 1967 to 1973 and consisted of 39, 522 men and women employed at 84 Chicago-area companies and organizations. Major cardiovascular risk factors including body height and weight were measured by two trained research teams. Details of the baseline examination have been reported (19). In 1996, a health questionnaire was mailed to all participants 65+ who were not known to be deceased (n = 13, 242). With undeliverable questionnaires (n = 833) excluded, the response rate was 59.8%. The present cross-sectional study is based on 7080 (3981 men and 3099 women) of the 7417 respondents to the 1996 survey. Respondents with missing data for current weight (n = 40), HRQoL (n = 273), education, cigarette smoking, or alcohol intake (n = 24) were excluded.

Classification of BMI

To reduce item nonresponse, the questionnaire asked respondents to report their approximate weight (weight range) rather than their exact weight. The 14 weight categories/ranges were converted into numerical values by assigning category midpoints to all participants in that category: <100 lbs (90), 100 to 109 (105), 110 to 119 (115), …, 190 to 199 (195), 200 to 219 (210), 220 to 249 (235), and ≥250 (270) lbs. BMI was computed as weight in 1996 in kilograms divided by square of height in meters measured at baseline (1967 to 1973). According to national guidelines (2), all participants were classified into four BMI groups: <18.5 (underweight), 18.5 to 24.9 (normal weight), 25.0 to 29.9 (overweight), and ≥30 kg/m2 (obese). Accuracy of self-reported categorical weight was evaluated based on data from a sample of 93 study participants 65 years of age or older in 2002 to 2003. BMI computed from the converted self-reported categorical weight variable as described above was highly correlated with BMI computed from measured weight and height (Pearson r = 0.95).

HRQoL

The 12-item Health Status Questionnaire (HSQ-12) developed by Health Outcomes Institute was used to measure HRQoL (20). Similar to the Medical Outcomes Study Short-Form Health Survey (21), the HSQ-12 was designed to provide a subjective evaluation of physical, social, and mental well-being. Validity and reliability of the HSQ-12 in measuring HRQoL in older individuals have been demonstrated (22, 23). The HSQ-12 captures eight domains: health perception (self-rated health), physical functioning (lifting/carrying groceries, climbing several flights of stairs, and walking several blocks), role limitations due to physical health (difficulty in doing work or other regular activities), bodily pain, energy/fatigue, social functioning (interference with normal social activities with family, friends, etc. due to physical or emotional problems), role limitations due to emotional problems, and mental health (feeling calm/peaceful, feeling downhearted/blue, and being a happy person). For each domain, categorical responses were scaled into numeric scores ranging from 0 to 100 according to the HSQ-12 scoring protocol—the higher the score, the better the outcome (20). Although the HSQ-12 is not designed to provide a single summary measure, we also presented data on the sum of all eight domain scores to demonstrate global outcome differences by BMI groups. In supplemental analyses, we dichotomized all response categories for each HSQ-12 item to indicate adverse outcomes for that item (for example, “limited a lot” in walking several blocks vs. all other responses: “limited a little” or “not limited at all”). These binary outcomes provide intuitive interpretations of HSQ-12 items.

Chronic Diseases and Other Covariates

Information on chronic diseases was obtained from medication use and a checklist of 30 physician-diagnosed medical conditions. Major conditions common among older adults were further classified into six categories: cancer, cardiovascular diseases, diabetes, hypertension, arthritis/sciatica, and hip fracture. Other covariates included: age (years), race (black or not), education (<high school, high school graduate, some college, college or higher), cigarette smoking (never, former, or currently smoking 1 to 9, 10 to 19, or ≥20 cigarettes/day), and average alcohol intake (converted into milliliters per day based on weekly quantities of beer, wine, and liquor consumed and a standard conversion formula) (24).

Statistical Analyses

Due to potentially large differences in associations of BMI with HRQoL by gender, all analyses were conducted separately for women and men. Selected characteristics of study participants were compared across the four BMI groups, and χ2 tests (for categorical variables) or F tests (for continuous variables) were used to test for statistical significance of differences across the four strata. Age-adjusted prevalence (percentage) of chronic diseases was computedwith general linear models (GLMs) (SAS version 8.2; SAS Institute, Inc., Cary, NC). These values are least squares estimates adjusted for age. GLM was also used to compute prevalence (percentage) of adverse outcomes for all HSQ-12 items adjusting for age, race, education, smoking, and alcohol intake (Model I). In analyses of binary outcomes, logistic regression was used to test for statistical significance of the differences between the reference group (normal weight) and other groups (represented by the coefficient for the dummy variable of that group). Mean scores for the eight HSQ-12 domains were computed by GLM with adjustment for Model I covariates. Model II included further adjustments for the six categories of major chronic conditions. Additional analyses were adjusted for the number of comorbid conditions instead of disease categories. Because another approach to studying the impact of excess body weight is to examine weight categories while controlling for height (instead of using BMI), all multivariable models were repeated with five weight categories (<130, 130 to 149, 150 to 169, 170 to 199, and ≥200 lbs) and with height as an additional covariate.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

In 1996, average age was 74.3 ± 6.0 years for women and 72.8 ± 5.9 for men, and average BMI was 25.1 ± 4.6 kg/m2 for women and 26.4 ± 3.7 kg/m2 for men. In general, there was an inverse association between BMI and age, education, smoking, and alcohol intake among women (Table 1). Similar patterns were observed for men except for alcohol.

Table 1. . Characteristics of 3099 women and 3981 men ages 65+ by BMI categories, 1996
 BMI
 WomenMen
Variable (units)<18.518.5 to <2525 to <30≥30.0<18.518.5 to <2525 to <30≥30.0
  • *

    p < 0.05,

  • **

    p < 0.01

  • ***

    p < 0.001 for overall group differences from χ2 tests (for categorical variables) or F tests (for continuous variables).

  • BMI, defined as weight in kilograms divided by square of height in meters: underweight (BMI < 18.5), normal weight (18.5 to <25), overweight (25 to <30), and obese (≥30).

  • Numbers shown are mean (SD) unless otherwise noted.

N (%)146 (4.7)1499 (48.4)1010 (32.6)444 (14.3)39 (1.0)1366 (34.3)1923 (48.3)653 (16.4)
BMI (kg/m2)17.2 (0.9)22.1 (1.8)27.1 (1.3)33.2*** (3.0)17.4 (1.5)22.8 (1.4)27.0 (1.4)32.4*** (2.2)
Age (years)77.1 (6.1)75.1 (6.2)73.6 (5.8)72.7*** (5.2)76.4 (7.4)74.1 (6.3)72.4 (5.7)70.9*** (4.8)
Race (% black)03.54.75.6**10.32.22.73.7**
Education (years)13.0 (2.6)13.1 (2.7)12.8 (2.6)12.2*** (2.8)15.1 (3.4)14.7 (3.0)14.4 (3.2)13.9*** (2.8)
Smoking (%)        
 Never54.157.858.462.451.335.029.429.3
 Former27.434.136.334.033.356.864.365.5
 Current18.58.15.33.6***15.48.26.25.2***
Alcohol (mL/d)8.4 (18.1)6.0 (12.6)4.2 (10.2)4.2*** (13.0)4.8 (9.8)14.9 (23.7)15.7 (25.5)13.7* (24.4)

For both men and women, age-adjusted prevalence of chronic diseases differed significantly across BMI groups (Table 2). Compared with normal-weight persons, obese men and women had higher prevalence of most chronic diseases. Underweight individuals, especially underweight men (n = 39), also had more, although not significantly more, comorbidities.

Table 2. . Age-adjusted prevalence (%) of chronic diseases for 3099 women and 3981 men ages 65+ by BMI categories, 1996
 BMI
 WomenMen
Diseases<18.5 n = 14618.5 to <25 n = 149925 to <30 n = 1010≥30.0 n = 444<18.5 n = 3918.5 to <25 n = 136625 to <30 n = 1923≥30.0 n = 653
  • *

    P < 0.05,

  • **

    p < 0.01

  • ***

    p < 0.001 for differences with the normal-weight group (BMI 18.5 to 24.9 kg/m2) from logistic regression (binary outcomes) or linear regression (continuous outcomes).

  • Cancer includes lung, stomach, intestinal, rectal, breast, ovarian, uterine, prostate, leukemia, or other cancer (except skin cancer).

  • CVD, cardiovascular diseases including heart attack, angina, congestive heart failure, stroke, arteriosclerosis, or other heart disease.

  • §

    Having a medical diagnosis or currently on medication.

  • The total number of diseases in the check list is 30, including the above-mentioned diseases, diabetes, hypertension, arthritis, sciatica/chronic back pain, hip fracture, skin cancer, pneumonia, emphysema, liver disease, kidney disease, Alzheimer's disease, cataract, deafness, and other major disease.

Cancer14.812.611.915.720.517.415.317.3
CVD24.921.123.828.7***38.230.631.432.0
Diabetes§3.34.511.1***17.4***15.08.612.3**21.2***
Hypertension§32.2*41.951.2***64.7***36.337.747.6***57.1***
Arthritis/sciatica47.148.458.3***70.1***22.332.840.3***49.2***
Hip fracture9.0*4.54.12.89.32.11.62.2
Any disease (out of 30 categories)86.685.892.3***94.8***84.385.786.893.3***
Mean number of diseases, if any2.92.72.9*3.5***3.02.82.93.1***

Figure 1 shows the Model I-adjusted prevalence of adverse responses for eight items in HSQ-12 for men and women separately. For seven of eight items, obese and underweight women had higher prevalence of adverse conditions compared with normal-weight women (Figure 1A). Results for underweight and obese men were similar for physical health items but not for social or mental health, whereas outcomes for overweight men compared favorably with those for normal-weight men (Figure 1B).

image

Figure 1. Adjusted prevalence (percentage) of adverse outcomes for eight HSQ-12 items by BMI categories in 1996 for 3099 women (A) and for 3981 men (B), ≥65 years of age. *p < 0.05, †p < 0.01, and ‡p < 0.001 compared with the reference group (normal weight; BMI 18.5 to 24.9 kg/m2). Adjusted for age, race, education, smoking, and alcohol intake. HP is Health Perception (“fair” or “poor”); PF is Physical Functioning (represented by one item: “limited a lot” in walking several blocks); RP is Role limitations due to Physical health (“quite a bit” or “could not do”); BP is Bodily Pain (“severe” or “very severe”); EF is Energy/Fatigue (having a lot of energy “a little” or “none of the time”); SF is Social Functioning (“interfered quite a bit” or “extremely”); RM is Role limitations due to Mental health (“quite a bit” or “extremely”); MH is Mental Health (represented by one item: “feeling downhearted and blue most or all of the time”).

Download figure to PowerPoint

Patterns for HSQ-12 items were also evident in the eight domain scores with the same set of covariates (Table 3, Model I). In general, additional adjustments for major diseases only partially attenuated associations of BMI with HRQoL measures (Table 3, Model II). Similar results were observed when the number of comorbid conditions, ranging from 0 to 14, was used to adjust for chronic illnesses (data not shown). Virtually identical patterns were also obtained from analyses using weight categories with adjustment for height. For example, Model I-adjusted HSQ-12 summary scores were 567, 574, 557, 524, and 420 of a total of 800 for women with weight <130, 130 to 149, 150 to 169, 170 to 199, and ≥200 lbs, respectively.

Table 3. . Adjusted mean scores for HSQ-12 domains and mean summary score for 3099 women and 3981 men ages 65+ by BMI categories, 1996
  BMI
  WomenMen
HSQ-12 DomainModel<18.5 (n = 146)18.5 to <25 (n = 1499)25 to <30 (n = 1010)≥30.0 (n = 444)<18.5 (n = 39)18.5 to <25 (n = 1366)25 to <30 (n = 1923)≥30.0 (n = 653)
  • *

    p < 0.05,

  • **

    p < 0.01

  • ***

    p < 0.001 for differences with the normal-weight group (BMI 18.5 to <25 kg/m2).

  • For each domain, categorical responses are scaled into numeric scores (ranges 0 to 100) according to HSQ-12 scoring protocol. The higher the score, the better the quality of life.

  • Model I was adjusted for age, race (indicator for black), education, smoking, and alcohol intake. Model II was adjusted for all variables in Model I and the presence of cancer, cardiovascular diseases, diabetes, hypertension, arthritis/sciatica, and hip fracture.

  • §

    Summary score is computed as the sum of the eight domain scores, ranging from 0 to 800.

Health perceptionI50.8***62.760.552.1***51.2**64.766.9*59.1***
 II50.3***60.961.356.4**52.4*63.766.9**61.1
Physical functioningI63.3**71.264.8***50.8***66.9**79.780.071.9***
 II63.3**69.765.5***54.6***67.8**78.980.073.5***
Role limitations—physicalI60.8**70.566.3**55.2***62.6*75.177.268.6***
 II60.8**68.867.159.4***62.7*74.177.3**70.5*
Bodily painI73.573.369.5***61.2***81.379.877.6**73.0***
 II72.171.570.565.5**80.078.777.775.0***
Energy/fatigueI53.4*58.955.8**45.8***51.1*61.161.254.6***
 II53.2*57.756.348.7***51.2*60.361.255.9***
Social functioningI79.2**85.786.080.9***81.4*88.690.6**88.4
 II80.3*85.186.282.1*81.988.290.6**89.2
Role limitations—mentalI74.9*80.780.076.8*76.0*85.486.983.6
 II75.680.180.278.076.785.086.9*84.4
Mental healthI69.1**73.974.872.873.177.378.177.0
 II69.2**73.575.0*73.973.377.078.177.5
Summary score§I525.0***576.8557.8**495.7***543.6**611.6618.5576.1***
 II524.7**567.2562.0513.4***546.0**606.0618.7**587.0**

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

The epidemic of obesity across all ages (1) coupled with rapid aging of the population (18) means that the proportion and number of obese older persons will be unprecedented. This study of 7080 men and women ages 65+ showed that obesity was strongly associated with lower HRQoL for both men and women in the health perception domain and physical domains including physical functioning, role limitations-physical, bodily pain, and energy/fatigue. Impaired social functioning was observed among obese women only. No statistically significant differences in mental health domains (role limitations-mental and mental health) were observed between obese persons and other individuals. Among women only, overweight was related, although to a smaller degree than obesity, to impaired physical well-being. All domains except bodily pain were adversely influenced by being underweight for both older men and women. All of these associations were independent of age, race, education, smoking, and alcohol intake. They were attenuated but not eliminated with further adjustment for comorbidities.

Previous research on BMI and HRQoL among older adults is very limited (13, 14, 15), and most studies have included people from mixed age groups (for review, see (9, 10, 12). A small clinical study of 88 older women (mean age 71 ± 5 years) reported that higher BMI was significantly associated with poorer upper and lower body function in performance tests of 18 physical tasks (13). In a study of Spanish persons 60 years of age and older (n = 3605), prevalence of suboptimal physical functioning was higher among both obese men and obese women (BMI ≥ 30 kg/m2) (15). Our finding of a strong association between BMI and physical well-being is consistent with these reports and several other studies on young and middle-aged adults in different countries (4, 5, 6, 7, 8, 11).

The lack of association between obesity and mental health was consistent with some (5, 8) but not all (7, 11) previous studies of primarily middle-aged adults. The Spanish study described above found no relation between obesity and mental health among women but a positive relation among men (15). However, longitudinal findings on BMI measured in midlife and HRQoL in older age from the same population as in the present study found a dose-response increase not only in impairment of physical domains but also, although less strongly, in impairment of social functioning and mental health domains (25). One possible explanation for the different cross-sectional and longitudinal findings is that some individuals may have gained weight in the last few years and were classified in the obese group; thus, duration of exposure may have been too short for the adverse effects of obesity to become evident, especially in the social and mental domains. In our sample, 47% of obese women and 43% of obese men reported gaining more than 10 lbs in the last 5 years, compared with 8% and 18% of normal-weight women and men, respectively.

The relationship between BMI and HRQoL is not linear; both underweight and obese older adults had considerably more chronic diseases and poorer HRQoL than those with normal weight. We also found a significant association between overweight and poor physical functioning among women but not among men. This is consistent with cross-sectional studies in young and middle-aged adults (8, 11) but in contrast to longitudinal findings from the same cohort where overweight in middle age was associated with lower physical well-being in older age for both women and men, although the association was weaker for men (25). It is plausible that the healthy BMI range for men may be different from that for women. More in-depth studies on potential gender differentials and on weight changes over time are needed.

Most chronic diseases are important determinants of HRQoL (17). In addition, obesity and underweight are related to increased risks (2, 3) of many chronic diseases that are highly prevalent among older adults (26). Therefore, it is important to examine whether comorbidities can explain the association between BMI and HRQoL. In this study, additional adjustment for comorbidities only partially attenuated the association of BMI with HRQoL, which is consistent with most previous studies (7, 8, 16). For example, a study of 155, 989 U.S. adults 18 years and older using self-reported height and weight found that adjustment for joint pain and obesity-related comorbidities diminished but did not eliminate the association between obesity and quality of life as determined by general health status and number of unhealthy days in the past month (16). On the other hand, some underweight persons may have lost weight due to underlying illness (e.g., 51.4% of underweight individuals reported losing 10 lbs or more during the past 5 years, compared with 28.4%, 23.7%, and 24.2% of normal-weight, overweight, and obese participants, respectively); thus, the relationships among underweight, HRQoL, and chronic disease are more complex.

One important limitation of this study is that BMI was computed from height measured in middle age and self-reported categorical weight instead of from current objective measurements. However, average height shrinkage has been reported to be only ∼2 cm from middle age (mean age 48 years) to old age (mean age 73 years) (27, 28), translating to a 0.6-unit difference in BMI for a person with mean height (1.7 m) and weight (75 kg). Thus, it is unlikely that our four-group BMI classifications were much affected by this phenomenon. Moreover, despite use of categorical weight, the high correlation between measured and self-reported BMI in our validation study lends support to the credibility of our findings. Another validation study from the Nurses’ Health Study also showed that self-reported weight and measured weight were highly correlated (Spearman r = 0.96) (29). Nevertheless, it has been documented that obese people tend to underreport and underweight persons overreport their weight (30), which is likely to yield underestimates of associations of BMI with HRQoL. In addition, BMI is only a proxy measure for obesity status and body composition. Direct measures of body composition (e.g., fat mass and fat-free mass) and fat distribution (e.g., waist circumference) may provide more precise estimates of the association between obesity and HRQoL than BMI per se (14).

Another limitation is that the study population was drawn from a follow-up study of participants originally from the Chicago area, with a response rate of ∼60%; thus, the results may not be generalizable. However, 37% of the respondents had moved out of the Chicago area, with residences in all 50 U.S. states in 1996. Further, our response rate of 60% compares favorably with similar mail surveys of older persons (31). The estimates are likely to be conservative due to worse outcomes and lower response rates among obese people (54.7%) compared with normal-weight individuals (60.5%). Finally, the number of underweight participants, especially men, was small. Nevertheless, patterns distinct from the other groups were observed for these men and women.

To our knowledge, this is the first study in older American men and women on relationships between all dimensions of HRQoL and BMI classified using contemporary guidelines for identification of obesity. Our main findings are that extremes of body weight (underweight and obesity) are associated with lower health perception and poorer physical functioning. The results reinforce the importance of normal body weight in older age (2).

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

We thank the officers and employees of the Chicago companies and organizations and those involved in the Chicago Heart Association Detection Project in Industry, whose invaluable cooperation and assistance made this study possible. An extensive list of colleagues who contributed to this important endeavor has been published in the journal Cardiology (19). We also thank the participants who responded to the health survey. This research was supported by grants from the American Heart Association and its Chicago and Illinois affiliates; the Illinois Regional Medical Program; National Heart, Lung, and Blood Institute (R01 HL21010 and R01 HL62684); the Chicago Health Research Foundation; private donors; and by an Established Investigator Award from the American Heart Association (to M.L.D.). The views expressed in this paper are those of the authors and are not to be construed as representing official viewpoints of any of the funding sources above.

Footnotes
  • 1

    Nonstandard abbreviations: HRQoL, health-related quality of life; HSQ-12, 12-item Health Status Questionnaire; GLM, general linear model.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References
  • 1
    Flegal, K. M., Carroll, M. D., Ogden, C. L., Johnson, CL. (2002) Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 288: 17231727.
  • 2
    NIH Heart, Lung, and Blood Institute (1998) Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: The Evidence Report. NIH: Bethesda, MD.
  • 3
    World Health Organization (2000) Obesity: Preventing and Managing the Global Epidemic: Technical Report Series, No. 894. World Health Organization: Geneva, Switzerland.
  • 4
    Coakley, E. H., Kawachi, I., Manson, J. E., Speizer, F. E., Willet, W. C., Colditz, GA. (1998) Lower levels of physical functioning are associated with higher body weight among middle-aged and older women. Int J Obes Relat Metab Disord. 22: 958965.
  • 5
    Le Pen, C., Levy, E., Loos, F., Banzet, M. N., Basdevant, A. (1998) “Specific” scale compared with “generic” scale: a double measurement of the quality of life in a French community sample of obese subjects. J Epidemiol Comm Health. 52: 445450.
  • 6
    Lean, M. E., Han, T. S., Seidell, JC. (1999) Impairment of health and quality of life using new US federal guidelines for the identification of obesity. Arch Internal Med. 159: 837843.
  • 7
    Doll, H. A., Petersen, S. E., Stewart-Brown, SL. (2000) Obesity and physical and emotional well-being: associations between body mass index, chronic illness, and the physical and mental components of the SF-36 questionnaire. Obes Res. 8: 160170.
  • 8
    Katz, D. A., McHorney, C. A., Atkinson, RL. (2000) Impact of obesity on health-related quality of life in patients with chronic illness. J Gen Internal Med. 15: 789796.
  • 9
    Kushner, R. F., Foster, GD. (2000) Obesity and quality of life. Nutrition. 16: 947952.
  • 10
    Fontaine, K. R., Barofsky, I. (2001) Obesity and health-related quality of life. Obes Rev. 2: 173182.
  • 11
    Ford, E. S., Moriarty, D. G., Zack, M. M., Mokdad, A. H., Chapman, DP. (2001) Self-reported body mass index and health-related quality of life: findings from the Behavioral Risk Factor Surveillance System. Obes Res. 9: 2131.
  • 12
    Kolotkin, R. L., Meter, K., Williams, GR. (2001) Quality of life and obesity. Obes Rev. 2: 219229.
  • 13
    Apovian, C. M., Frey, C. M., Wood, G. C., Rogers, J. Z., Still, C. D., Jensen, GL. (2002) Body mass index and physical function in older women. Obes Res. 10: 740747.
  • 14
    Sternfeld, B., Ngo, L., Satariano, W. A., Tager, IB. (2002) Associations of body composition with physical performance and self-reported functional limitation in elderly men and women. Am J Epidemiol. 156: 110121.
  • 15
    Lopez-Garcia, E., Banegas, Banegas Jr., Gutierrez-Fisac, J. L., Perez-Regadera, A. G., Ganan, L. D., Rodriguez-Artalejo, F. (2003) Relation between body weight and health-related quality of life among the elderly in Spain. Int J Obes Relat Metab Disord. 27: 701709.
  • 16
    Heo, M., Allison, D. B., Faith, M. S., Zhu, S., Fontaine, KR. (2003) Obesity and quality of life: mediating effects of pain and comorbidities. Obes Res. 11: 209216.
  • 17
    Burstrom, K., Johannesson, M., Diderichsen, F. (2001) Health-related quality of life by disease and socio-economic group in the general population in Sweden. Health Policy. 55: 5169.
  • 18
    Goulding, M., Rogers, M., Smith, S. (2003) Public health and aging: trends in aging—United States and worldwide. MMWR Morb Mortal Wkly Rep. 52: 101106.
  • 19
    Stamler, J., Dyer, A., Shekelle, R., et al (1993) Relationship of baseline major risk factors to coronary and all-cause mortality, and to longevity: findings from long-term follow-up of Chicago cohorts. Cardiology. 82: 191222.
  • 20
    Health Outcomes Institute (1996) Twelve-Item Health Status Questionnaire (HSQ-12) Version 2.0 User Guide. Health Outcomes Institute: Bloomington, MN.
  • 21
    Ware, J. E., Jr, Kosinski, M., Keller, S. D. (1996) A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 34: 220233.
  • 22
    Bowling, A., Windsor, J. (1997) 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. 51: 564573.
  • 23
    Pettit, T., Livingston, G., Manela, M., Kitchen, G., Katona, C., Bowling, A. (2001) Validation and normative data of health status measures in older people: the Islington study. Intl J Geriatr Psych. 16: 10611070.
  • 24
    Dyer, A. R., Cutter, G. R., Liu, K. Q., et al (1990) Alcohol intake and blood pressure in young adults: the CARDIA Study. J Clin Epidemiol. 43: 113.
  • 25
    Daviglus, M. L., Liu, K., Yan, L. L., et al (2003) Body mass index in middle age and health-related quality of life in older age: The Chicago Heart Association Detection Project in Industry Study. Arch Internal Med. 163: 24482455.
  • 26
    Guralnik, JM. (1996) Assessing the impact of comorbidity in the older population. Ann Epidemiol. 6: 376380.
  • 27
    Borkan, G., Hults, D., Glynn, R. (1983) Role of longitudinal change and secular trend in age differences in male body dimensions. Human Biol. 55: 629641.
  • 28
    Cline, M., Meredith, K., Boyer, J., Burrows, B. (1989) Decline of height with age in adults in a general population sample - Estimating maximum height and distinguishing birth cohort effects from actual loss of stature with aging. Human Biol. 61: 415425.
  • 29
    Rimm, E. B., Stampfer, M. J., Colditz, G. A., Chute, C. G., Litin, L. B., Willett, WC. (1990) Validity of self-reported waist and hip circumferences in men and women. Epidemiology. 1: 466473.
  • 30
    Kuskowska-Wolk, A., Bergstrom, R., Bostrom, G. (1992) Relationship between questionnaire data and medical records of height, weight and body mass index. Int J Obes Relat Metab Disord. 16: 19.
  • 31
    Clarke, R., Breeze, E., Sherliker, P., et al (1998) Design, objectives, and lessons from a pilot 25 year follow up re-survey of survivors in the Whitehall study of London Civil Servants. J Epidemiol Community Health. 52: 364369.