Quality of Life Research

, Volume 16, Issue 10, pp 1595–1603

The association between body mass index and health-related quality of life: data from CaMos, a stratified population study

Authors

    • Clinical Research CentreKingston General Hospital
    • Department of Community Health and EpidemiologyQueen’s University
  • Claudie Berger
    • CaMos Methods CentreMcGill University
  • Lawrence Joseph
    • Department of Epidemiology and BiostatisticsMcGill University
  • Susan I. Barr
    • Human NutritionUniversity of British Columbia
  • Yongjun Gao
    • CaMos Methods CentreMcGill University
  • Jerilynn C. Prior
    • Division of Endocrinology, Department of MedicineUniversity of British Columbia
  • Suzette Poliquin
    • CaMos National Coordinating CentreMcGill University
  • Tanveer Towheed
    • Department of Community Health and EpidemiologyQueen’s University
    • Division of Rheumatology, Department of MedicineQueen’s University
  • Tassos Anastassiades
    • Division of Rheumatology, Department of MedicineQueen’s University
  • CaMos Research Group
Article

DOI: 10.1007/s11136-007-9273-6

Cite this article as:
Hopman, W.M., Berger, C., Joseph, L. et al. Qual Life Res (2007) 16: 1595. doi:10.1007/s11136-007-9273-6

Abstract

Background

Deviation from normal weight is associated with health risks, but less is known about the association between weight and health-related quality of life (HRQOL). We investigated this in the context of a population-based study, using a standard five-category weight classification system based on body mass index (BMI).

Methods

The Canadian Multicentre Osteoporosis Study is a randomly selected sample of men and women over 25 years of age from nine centres across Canada. Data were obtained by interview, and height and weight were measured and used to calculate BMI. HRQOL was measured using the SF-36. Multivariable linear regression was used to identify the association between BMI category and SF-36 scores after controlling for potential confounders.

Results

Complete data were available for 6302 women and 2792 men. Mean BMI for every age and gender group exceeded healthy weight guidelines. For women, being underweight, overweight or obese was associated with poorer HRQOL in most SF-36 outcomes while for men, this was associated with poorer HRQOL in some domains and with higher HRQOL in others.

Conclusions

A significant proportion of the population may be putting their health at risk due to excess weight, which may have a substantial negative effect on HRQOL, particularly in women. This underscores the need for continued public health efforts aimed at combating overweight and obesity.

Keywords

BMIBody mass indexEpidemiologySF-36

Abbreviations

HRQOL

Health-related quality of life

SF-36

Medical Outcomes Trust 36-item Health Survey

BMI

Body mass index

CaMos

Canadian Multicentre Osteoporosis Study

PCS

Physical component summary of the SF-36

MCS

Mental component summary of the SF-36

DXA

Dual-energy X-ray absorptiometry

BMD

Bone mineral density

CI

Confidence interval

Introduction

Data from the 2004 Canadian Community Health Survey suggest that 65.0% of men and 53.4% of women aged 18 years and over are overweight or obese, while an additional 1.4% of men and 2.5% of women are underweight [1]. Overweight and obese individuals are more likely than their normal-weight peers to suffer from stress, activity restriction and chronic conditions including heart disease, certain cancers, hypertension, diabetes mellitus, asthma, arthritis and joint pain [25], in addition to having an increased risk of mortality [6, 7]. For women, being overweight at age 18 also increased the risk for subsequent anovulatory infertility [8]. Being underweight is associated with health risks such as poor nutrition, osteoporosis, infertility and impaired immunocompetence [1], as well as certain types of cancer [9], risk of fracture [10] and increased risk of mortality [6, 7].

Much of the research examining the association between body mass index (BMI) and health-related quality of life (HRQOL) has focused on weight in specific age groups, patient groups such as those with eating disorders or morbid obesity, or has examined the impact of weight loss, weight change or activity on HRQOL [1114]. Less is known about the association between weight and health-related quality of life in a general population [15], or using the recently adopted weight classification system which defines underweight as a BMI of <18.5, normal as 18.5 to <25, overweight as 25.0 to <30, and includes three classes of obesity for those with a BMI of 30.0 to <35, 35.0 to <40, and ≥40 [1, 16].

A population-based study of 5,633 Swedish residents between the ages of 16 and 64 years noted a significant negative impact of obesity on HRQOL, with obese older women reporting the poorest HRQOL as compared to other groups [17]. A random sample of 12,905 Dutch residents between the ages of 20 and 59 years noted that a BMI ≥ 25 was associated with increased risk of impaired quality of life, which was even more pronounced in the group with a BMI > 30 [18]. An additional study that compared a random sample of 500 non-obese and 500 overweight subjects noted that the quality of life of patients with severe obesity was impaired, although physical aspects of HRQOL are much more affected than psychological and social aspects [19]. Results from a population-based sample of 14,221 Taiwanese residents using self-reported height and weight noted that HRQOL worsened as weight increased, and that as compared to men, women with excess weight showed a greater deficit in HRQOL [20].

Several studies focused on excess weight and HRQOL in younger age groups. Two studies demonstrated poorer general and physical health in obese adolescents [21] and young women [22], and noted that the best scores for physical functioning, general health and vitality were among the women with a BMI of 18.5 to <25. Underweight (BMI < 18.5) was not strongly associated with reduced HRQOL in a sample of young Australian women, but was associated with other health problems such as low iron and irregular menstruation [22].

Three studies focused on older adults. In a population of 7,080 community-dwelling US adults aged 65 years and older, being either underweight (BMI < 18.5) or overweight (BMI ≥ 25) was associated with impaired quality of life [23], while a study of 160 home-dwelling elderly people found that being overweight or obese were among the primary predictors of decreased quality of life [24]. In a random sample of 3,605 Spanish residents aged 60 years and older, suboptimal physical functioning was more common among obese men and women as compared to those of a normal weight [25].

Two studies assessed data collected by the Behavioral Risk Factor Surveillance System, developed by the US Centers for Disease Control and Prevention. It is a telephone-administered survey of randomly selected households designed to collect state-specific estimates of the prevalence of behaviours that relate to the leading causes of death in the US [26]. It includes four health-related quality of life questions (self-rated health, physically unhealthy days, mentally unhealthy days, activity limitation days) [26, 27]. One study found that the obese and severely obese were more likely to have greater than 14 unhealthy days affecting both physical and mental health [27], while the other found that ratings of poor or fair health increased for those with low BMI (<18.5) and in those with elevated BMI (≥25.0) [26].

With the exception of the Taiwanese study [20], no other study has looked at the entire adult population, focusing instead on specific age groups. However, the Taiwanese study used self-reported rather than measured height and weight [20]. The use of measured height and weight is important, as there is evidence that both men and women overestimate their height and underestimate their weight [28, 29]. In one study, this resulted in a 3.3–12.2% misclassification of BMI category, depending on the gender and BMI category assessed [29]. Despite the cost of collecting measured height and weight, it is important, as self-reported BMI is not appropriate for precise measures of obesity prevalence [30].

The Canadian Multicentre Osteoporosis Study (CaMos) was designed to study the incidence and prevalence of osteoporosis in a random sample of Canadians over 25 years of age. This provided the opportunity to assess the association between health related quality of life and BMI category based on measured height and weight within a large population-based cohort of Canadian adults.

Methods

CaMos is an on-going, 10-year prospective cohort study of 9,423 non-institutionalized, randomly selected men and women aged 25 years and older at baseline in 1995–1997. This analysis is of data collected at baseline. Participants were drawn from a 50-kilometer radius of nine Canadian cities (St John’s, Halifax, Quebec City, Toronto, Hamilton, Kingston, Saskatoon, Calgary and Vancouver). A detailed description of the objectives, methodology and sampling framework for CaMos is available elsewhere [31, 32]. Briefly, households within each region were selected by random draws of listed telephone numbers, and one randomly selected household member over 25 years of age was asked to participate. Of 22,173 eligible households, 27.5% declined to participate, 30.0% completed a short questionnaire that provided information about the age, gender and fracture history of the residents, and 9,423 (42.5%) went on to participate fully in the study. Ethics approval was obtained through the Review Boards of each participating centre.

All data were obtained from an interview-administered questionnaire designed for CaMos and shown to have good reproducibility [33]. The majority of participants were scheduled for dual-energy X-ray absorptiometry (DXA) assessment of bone mineral density (BMD) on the same day as the interview, at which time both height (without shoes) and weight (in indoor clothing) were measured. For those who elected not to have the BMD or for whom it could not be scheduled, the interviewer measured height and weight with a carpenter’s rule and a portable scale. BMI was calculated using the standard formula of weight in kilograms divided by the height in metres squared.

HRQOL was measured using the Medical Outcomes Trust 36-item health survey (SF-36), one of the most widely used, validated and psychometrically sound instruments for the assessment of HRQOL [32, 34, 35]. The SF-36 consists of 36 items encompassing eight HRQOL domains including physical function, role physical, bodily pain, general health, vitality, social functioning, role emotional and mental health. Domains are scored on standardized scales from 0–100, with higher values representing better HRQOL [34]. Two summary scores, a Physical Component Summary (PCS) and a Mental Component Summary (MCS), are standardized to a mean of 50, with a score above 50 representing better than average function and below 50 poorer than average function [35]. Both the PCS and the MCS are based on all eight domain scores, although the PCS is primarily based on the domains of physical function, role physical, bodily pain and general health, while the MCS is primarily based on the vitality, social functioning, role emotional and mental health domains [35].

All analyses were conducted separately for men and women. Six pregnant women were excluded from the analysis. The number with missing BMI and/or SF-36 data was determined, and those with and without missing data were compared. The number of men and women in each of the BMI categories was then calculated.

Multivariable linear regression modelling was used to assess the association between BMI category and the SF-36 domain and summary component scores after controlling for potential confounders, using the normal weight group (BMI 18.5 to <25) as the reference population. Since previous research has shown that the association between age and HRQOL is not linear [32], age was included as a categorical variable using 10-year increments, with the youngest group (25 to <35 years) as the reference category.

Potential confounding variables in addition to age were identified on the basis of an extensive literature review and previous work with the SF-36 in this population [36], and included an initial list of 60 variables. Univariate regression was used to examine the association between these variables and the domains, and 15 were retained. These included sociodemographic characteristics (region of Canada as the nine study sites listed earlier; education as seven levels including <grade 9; grade 9–12 or 13 without diploma; high school diploma; trade; some university; university certificate or diploma; university degree[s]); clinical characteristics (presence or absence of a variety of comorbid conditions including osteoarthritis, rheumatoid arthritis, thyroid [hyperthyroidism, hypothyroidism], heart disease, stroke or transient ischemic attack); number of comorbid conditions; previous surgery; history of fractures due to severe trauma, minimal trauma or other disease; smoking status, medications such as bisphosphonates or cortisone/prednisone; menopausal status and clinically reported depression. Two measures of activity level (time spent walking in a typical week in the past 6 months, and number of sedentary hours per day) were evaluated but did not make any substantial contributions to the domains, and were therefore excluded.

Differences in SF-36 domain scores between the reference (normal weight) age- and sex-specific populations and those who were in different BMI categories were considered clinically and socially relevant if they exceeded five points [34]. For the Physical and Mental Component Summaries, a two- to three-point difference is likely to be relevant [35].

Results

As shown in Table 1, the sample included 6,302 (96.4%) women and 2,792 (96.8%) men after excluding those with missing BMI or HRQOL data. This table also contains the values for age category and weight category, although the data for the weight categories are more meaningful when age is taken into consideration in subsequent tables. Height and/or weight were missing for 202 women and 81 men, primarily due to scheduling difficulties for the DXA at two sites. The SF-36 scores were similar for men with and without BMI data. However, women with missing BMI differed from those with BMI data in that they had substantially lower mean scores for multiple domains including physical function (−18.4 points), role physical (−7.4), bodily pain (−5.7), general health (−6.0) and vitality (−7.9) domains. An examination of the age of the 202 women revealed that 20.8% were in the age group of 55–64 years, 26.7% were 65–74 years of age while an additional 33.2% were 75 years or older, so these results may reflect the lower physically-oriented function experienced by women in the older age groups as compared to younger age groups [32]. The distribution of BMI categories for those with and without HRQOL data did not differ substantially for men or women. Most BMI categories were adequately represented, with the exception of the underweight and obese class III for men, which had relatively low frequencies of 22 (0.8%) and 20 (0.7%) of the sample of men respectively.
Table 1

Description of sample

 

Women

Men

Total CaMos sample

6,539

2,884

Number missing BMI data

202 (3.1%)

81 (2.8%)

Number missing HRQOL data

41 (0.6%)

11 (0.4%)

Subset in analysis

6,302 (96.4%)a

2,792 (96.8%)

Age category

    25 to <35

190 (3.0%)

187 (6.7%)

    35 to <45

278 (4.4%)

205 (7.3%)

    45 to <55

1,091 (17.3%)

577 (20.7%)

    55 to <65

1,589 (25.2%)

627 (22.5%)

    65 to <75

2,064 (32.8%)

782 (28.0%)

    75+

1,090 (17.3%)

414 (14.8%)

Weight category

    Underweight

123 (2.0%)

22 (0.8%)

    Normal weight

2,367 (37.6%)

843 (30.2%)

    Overweight

2,324 (36.9%)

1,379 (49.4%)

    Obese class 1

1,035 (16.4%)

444 (15.9%)

    Obese class 2

337 (5.3%)

84 (3.0%)

    Obese class 3

116 (1.8%)

20 (0.7%)

aSix were missing both BMI and HRQOL data

Percentages for the age and weight categories are based on the subset of those in the analysis

CaMos was designed to collect epidemiological data related to the incidence and prevalence of osteoporosis, therefore although the sampling framework was random, it was designed to include more women than men, and a higher proportion of older than younger Canadian residents. This is evident in the frequencies for the age categories in Table 1. Figure 1 portrays the mean BMI and 95% confidence intervals (CIs) for each of the age groups, for both men and women. The mean BMI values for all age and gender groups exceeded the healthy weight guidelines of a BMI < 25 [2].
https://static-content.springer.com/image/art%3A10.1007%2Fs11136-007-9273-6/MediaObjects/11136_2007_9273_Fig1_HTML.gif
Fig. 1

Mean baseline body mass index with standard deviations, by gender and age

Adjusted parameter estimates for the eight domains and the two summary component scores of the SF-36 are shown for women in Table 2 and for men in Table 3. Each column is based on a separate regression model, and normal weight was used as the reference category. The entire regression models are not presented as the variables in the model differ for each domain and summary component score, and the estimates for the weight categories are the primary focus of this research. The number of variables controlled for in each model varied from a low of three for the role emotional model for both men and women and the social function model for men, to a high of 12 for the physical function and bodily pain domains for women.
Table 2

Adjusted parameter estimates and 95% confidence intervals for weight categories for women

Weight category

Physical function

Role physical

Bodily pain

General health

Vitality

Social function

Role emotional

Mental health

PCS

MCS

Normal

78.6

77.3

73.8

76.1

64.9

86.8

85.4

78.3

48.1

53.1

BMI 18.5 to <25

(77.7, 79.5)

(75.8, 78.7)

(72.8, 74.7)

(75.3, 76.8)

(64.1, 65.7)

(86.0, 87.6)

(84.1, 86.6)

(77.7, 78.9)

(47.7, 48.5)

(52.7, 53.4)

Underweight

−6.6

−2.8

−0.2

−5.6

−3.8

−3.3

−3.6

−1.9

−1.6

−0.7

BMI < 18.5

(−10.2, −3.0)

(−9.2, 3.6)

(−4.3, 3.8)

(−8.6, −2.6)

(−7.0, −0.6)

(−6.8, 0.3)

(−9.0, 1.8)

(−4.3, 0.5)

(−3.2, 0.0)

(−2.1, 0.6)

Overweight

−1.1

−0.3

−0.4

−0.3

−0.9

−0.3

−0.9

−0.2

−0.3

−0.2

BMI 25 to <30

(−2.3, 0.02)

(−2.4, 1.7)

(−1.7, 0.9)

(−1.2, 0.7)

(−1.9, 0.1)

(−1.4, 0.8)

(−2.6, 0.8)

(−0.9, 0.6)

(−0.8, 0.2)

(−0.7, 0.2)

Obese class I

−6.0

−2.1

−1.7

−1.1

−2.7

−1.7

−2.3

0.04

−1.6

−0.1

BMI 30 to <35

(−7.5, −4.5)

(−4.8, 0.5)

(−3.3, 0.0)

(−2.3, 0.1)

(−3.9, −1.4)

(−3.1, −0.3)

(−4.5, −0.2)

(−0.9, 1.0)

(−2.2, −0.9)

(−0.7, 0.5)

Obese class II

−11.7

−6.7

−5.7

−2.1

−4

−2.9

−3.4

0.6

−3.7

0.4

BMI 35 to <40

(−14.0, −9.4)

(−10.7, −2.6)

(−8.4, −3.1)

(−4.0, −0.2)

(−6.0, −2.0)

(−5.1, −0.7)

(−6.8, 0.0)

(−0.9, 2.1)

(−4.8, −2.7)

(−0.5, 1.3)

Obese class III

−17.8

−14.8

−8.1

−8.4

−5.5

−5.7

−8.0

1.1

−6.6

0.3

BMI ≥ 40

(−21.6, −14.0)

(−21.5, −8.2)

(−12.4, −3.8)

(−11.5, −5.3)

(−8.8, −2.3)

(−9.4, −2.1)

(−13.6, −2.4)

(−1.4, 3.6)

(−8.3, −4.9)

(−1.1, 1.7)

Normal weight (BMI 18.5 to <25) is the reference category

Each column represents a separate regression model

PCS = Physical component summary; MCS = Mental component summary; the PCS is primarily based on the physical function, role physical, bodily pain and general health domains, while the MCS is primarily based on the vitality, social functioning, role emotional and mental health domains. Domain scores can range from 0–100, whereas PCS and MCS are standardized to a mean of 50

Bolded scores are considered importantly different (>5 points for the domains, >2–3 points for the summaries [28, 29])

Table 3

Adjusted parameter estimates and 95% confidence intervals for weight categories for men

Weight category

Physical function

Role physical

Bodily pain

General health

Vitality

Social function

Role emotional

Mental Health

PCS

MCS

Normal

82.1

80.7

77.4

74.4

68

87.8

85

80.2

49.2

53.4

BMI 18.5 to <25

(80.6, 83.6)

(78.4, 83.0)

(75.9, 78.9)

(73.2, 75.7)

(66.8, 69.2)

(86.5, 89.2)

(82.9, 87.0)

(79.2, 81.1)

(48.6, 49.9)

(52.8, 53.9)

Underweight

−5.7

−0.7

−2.8

−3.4

−4.6

−11.8

−1.6

−2.7

−2

−4.8

BMI < 18.5

(−13.3, 1.9)

(−13.9, 12.5)

(−11.6, 6.0)

(−10.2, 3.3)

(−11.2, 2.1)

(−19.3, −4.3)

(−12.9, 9.6)

(−7.7, 2.3)

(−5.3, 1.4)

(−12.7, 3.1)

Overweight

0.2

2.7

0.9

1.4

0.6

1.6

3.8

1.1

0.2

0.5

BMI 25 to <30

(−1.4, 1.8)

(−0.0, 5.3)

(−0.9, 2.8)

(0.0, 2.7)

(−8.2, 1.9)

(0.1, 3.2)

(1.5, 6.0)

(0.0, 2.1)

(−0.5, 0.9)

(−1.1, 2.1)

Obese class I

−1.8

0.7

−0.7

−0.8

−1.6

0.6

3.2

0.5

−0.6

−2.2

BMI 30 to <35

(−4.0, 0.3)

(−2.9, 4.4)

(−3.1, 1.8)

(−2.7, 1.0)

(−3.4, 0.3)

(−1.4, 2.7)

(0.1, 6.2)

(−0.9, 1.9)

(−1.6, 0.3)

(−4.3, 0.0)

Obese class II

−7.3

−3.7

−1.2

−2.2

−2.3

0.3

−1.5

0.6

−2.1

−7.2

BMI 35 to <40

(−11.5, −3.1)

(−10.7, 3.3)

(−6.0, 3.6)

(−5.8, 1.4)

(−5.9, 1.3)

(−3.8, 4.3)

(−7.4, 4.5)

(−2.1, 3.4)

(−4.0, −0.3)

(−11.3, −3.0)

Obese class III

−17.4

−14.2

−10.3

−6.2

−0.5

−7.8

−4.3

2.1

7.1

−16.8

BMI ≥ 40

(−25.3, −9.4)

(−28.1, −0.4)

(−19.5, −1.1)

(−13.2, 0.9)

(−7.4, 6.5)

(−15.7, 0.0)

(−16.1, 7.5)

(−3.2, 7.3)

(−10.6, −3.5)

(−25.0, −8.6)

Normal weight (BMI 18.5 to <25) is the reference category

Each column represents a separate regression model

PCS = Physical component summary; MCS = Mental component summary; the PCS is primarily based on the physical function, role physical, bodily pain and general health domains, while the MCS is primarily based on the vitality, social functioning, role emotional and mental health domains. Domain scores can range from 0 to 100, whereas PCS and MCS are standardized to a mean of 50

Bolded scores are considered importantly different (>5 points for the domains, >2–3 points for the summaries [28, 29])

For women, being underweight was associated with clinically important reductions in 3/8 domains (physical function, general health, vitality), and two of these (physical function, general health) exceeded the 5-point difference commonly considered to be clinically and socially relevant [34]. The remaining 5 domains and the two summary component scores were also negatively affected by being underweight, although the results were inconclusive in that the 95% CI included zero. A similar pattern was evident for men, although a clinically relevant effect was only evident in the social function domain.

For women, being overweight or obese was associated with poorer HRQOL in almost all domains and component summary scores. The only exceptions were the three classes of obesity, which were associated with inconclusively but better mental health compared with the normal reference sample. The same held true for the Mental Component Summary for the two highest levels of obesity. Of the 40 cells representing excess weight (4 levels of overweight and 10 SF-36 outcomes), 20 (50%) had lower HRQOL as compared to the normal group, with 15 of these considered to be clinically relevant (>5 points for the domain scores [34] and >2–3 points likely to be clinically relevant for the component summary scores [35]). An additional 15 (37.5%) domain and summary component scores showed inconclusive but lower HRQOL than the reference sample.

A somewhat different pattern of associations between BMI categories and HRQOL emerged for the men. Being overweight or obese was associated with poorer HRQOL in some domains and component summary scores, and with higher HRQOL in others. Of the 40 cells representing excess weight (4 levels of overweight and 10 SF-36 outcomes), only 8 (20%) had lower HRQOL as compared to the normal group, with seven of these attaining clinical relevance. An additional 15 (37.5%) had inconclusive but poorer HRQOL. Four (10%) had higher HRQOL, although none attained clinical relevance, while an additional 13 (32.5%) had higher but inconclusive HRQOL. This was particularly apparent in the overweight group, and less evident in the three classes of obesity.

Discussion

In general, these results suggest that any deviation from normal weight, particularly underweight and the three levels of obesity, is, on average, associated with poorer HRQOL. In men, being in the overweight category was associated with slightly better HRQOL as compared to a normal weight, while being overweight was associated with slightly lower HRQOL for women, but most values for both men and women were close to zero for the overweight category.

The negative impact of underweight in both men and women supports the findings of one previous study that found lower HRQOL in underweight US adults over 65 years of age [21], as well as another that found reduced MCS scores in underweight adults [37]. Our data suggest that the effect of being underweight is more pronounced in the physical domains for women, but is more pronounced in the mental domains, particularly social function, for men.

The finding that obesity is associated with lower HRQOL, particularly in the physically oriented domains, supports previous research [18, 20, 2325]. The effect becomes more pronounced as level of obesity increases [18, 20, 37]. One other study has noted that the effect is more pronounced in women [20] and several have noted that overweight and obesity affect the physical domains far more than the mental domains [20, 25, 38]. These findings are quite consistent regardless of whether the research focused on younger populations [17, 21], the elderly [25], or the general population [20, 38]. This may be associated with mobility problems and pain experienced with increased weight [39]. One study noted that virtually all health outcomes studied, including respiratory insufficiency, low back pain, non-insulin-dependent diabetes, cardiovascular risk factors and general physical function were significantly influenced by increased level of BMI [18].

One study of Spanish adults found that obesity in men was associated with better mental health [25]. Although the effect size was small and the findings were inconclusive, we also found that the highest levels of obesity were positively associated with mental health. This may, to some extent, reflect a process of adaptation, as other research has shown that mental health is particularly resilient in the face of increased age, disease and disability [40].

Limitations of the study need to be considered. First, although the CaMos participants were randomly selected, not all of those who were invited to do so participated. In addition, although the 50-kilometer radius around each city often did include some rural areas, our data do not allow us to fully investigate rural regions. Caution must also be used when interpreting any results based on BMI data, for while it is a commonly used indicator of relative weight, it is a composite measure that is unable to distinguish between fat and lean tissue [41]. However, the fact that our data are based on measured rather than self-reported height and weight is a particular strength of the study [2830].

The mean BMI values for all age and gender groups in this study exceeded healthy weight guidelines. This is consistent with data from the Canadian Community Health Survey, which indicated that the majority of Canadians are overweight or obese [1]. Longitudinal research has shown that those whose weight is already in excess of recommended guidelines are more likely to continue to gain weight than to lose it [42, 43], and that a higher BMI in middle age is associated with poorer HRQOL in older age [44]. These findings therefore suggest that a substantial number of people may not only be putting their health at risk due to excess weight, but that this also may have a significant negative effect on their HRQOL. This underscores the need for continued public health efforts aimed at combating the current epidemic of obesity.

Acknowledgements

CaMos was funded by the Canadian Institutes of Health Research (CIHR), Merck Frosst Canada Ltd., Eli Lilly Canada Inc., Novartis Pharmaceuticals Inc., The Alliance: Safoni-Aventis & Procter and Gamble Pharmaceuticals Canada Inc., The Dairy Farmers of Canada, The Arthritis Society. None of the funding agencies were directly involved in the design or conduct of the study, the collection, management, analysis or interpretation of the data, or the preparation, review or approval of the manuscript. The authors thank all the participants in the Canadian Multicentre Osteoporosis Study.

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© Springer Science+Business Media B.V. 2007