BACKGROUND: The prevalence of overweight and obesity is increasing and contributes to the burden of ill health in the community. The impact of obesity on health-related quality of life has been less well studied than how it affects physical morbidity and mortality.
METHODS: A survey of health-related quality of life using the 12-item Short Form (SF-12) of the Medical Outcomes Study Short Form-36 was mailed to patients attending a family medicine clinic. Multiple regression analyses were used to investigate the relationships between scores on the mental and physical components of the SF-12 and body mass index (BMI) while controlling for age, sex, and family income.
RESULTS: Responses were received from 565 subjects (53%). The relationships among BMI and quality of life in the mental and physical domains were nonlinear. Quality of life scores were optimal when BMI was in the range of 20 to 25 kg per m2.
CONCLUSIONS: The National Heart, Lung, and Blood Institute has published evidence-based clinical guidelines for the identification, evaluation, and treatment of overweight and obesity in adults. Subjects with BMI in the range 18.5 to 24.9 kg per m2 are classified as having normal weight. These observations suggest that achieving a weight in this range will maximize the patient’s subjective sense of well-being.
Obesity is a common condition that is increasing in prevalence. Mokdad and colleagues1 reported that the prevalence of obesity (defined as a body mass index [BMI] Ž30 kg/m2) increased in the United States from 12.0% in 1991 to 17.9% in 1998. Must and colleagues2 found that the prevalence of morbidities such as diabetes, gallbladder disease, and osteoarthritis increased with severity of overweight (BMI >27 and <30) and obesity. The impact of obesity on health-related quality of life has been less well studied than how it affects physical morbidity and mortality.
Le Pen and coworkers3 surveyed a random sample of 500 French subjects with BMI >27 and a control sample of 500 subjects matched for sex, age, and employment status drawn from the nonobese population. Using a short specific quality of life scale and the Medical Outcomes Study Short Form-36 (SF-36),4 it was found that: (1) moderately obese subjects (BMI >27 and <30) did not significantly differ from those in the control group except for physical capacity; and (2) in the group of obese subjects with a BMI Ž30, quality of life seemed to be impaired for 5 of 9 dimensions of the SF-36 compared with the control population, all related to physical consequences of obesity. The study population perceived itself in terms of poor general health.3 No significant difference was observed between the samples for the psychological and social dimensions of the SF-36. Barofsky and colleagues5 also found that pain had a significant impact on the quality of life of obese patients. Fontaine and coworkers6 reported that weight loss was associated with significantly improved scores relative to baseline on the physical functioning, role-physical, general health, vitality, and mental health domains of the SF-36. The largest improvements were with respect to the vitality, general health perception, and role-physical domains.
In most studies of health-related quality of life, obesity has been treated as a dichotomous variable, with the cut point between nonobese and obese persons commonly set at a BMI of 27 kg per m2. This study presents an analysis of the variation of self-reported quality of life in a survey of primary care patients in relation to BMI as a continuous measure.
Methods
A questionnaire was mailed to all patients of the Family Medicine Centre at Mt Sinai Hospital in Toronto who were 45 to 74 years of age and had made at least 3 visits to the clinic during 1996-1997. A modified Dillman method7 was used with an initial mailing followed by a reminder postcard and a second mailing of the questionnaire. An ethics committee at the University of Toronto approved the project. The 103-question survey included the 12-item Short Form of the SF-36 (SF-12) quality of life instrument (QOL),8 in addition to questions about height, weight, and family income. The SF-12 inquires about physical and mental health and permits the computation of 2 summary scales, the physical component scale (PCS-12) and the mental component scale (MCS-12). These scales have been standardized to a mean score of 50 and a standard deviation (SD) of 10 in the general population.
Multiple linear regression models were used to explore the relationships between PCS-12, MCS-12, and BMI. The variables of age, sex, and family income (in 6 categories) were controlled, but health factors such as hypertension and diabetes which are in the causal pathway between BMI and quality of life were deliberately omitted.9 Modern statistical methodology was used to model the shape of the relationships between BMI and quality-of-life measures. To visualize the relationship, a regression smoother (a nonparametric regression function with no prespecified shape) was fitted to the data. S-Plus software10 was used, but this capability is also available in other statistical packages, such as SPSS.11 The result suggested that the relationships between quality of life and BMI were nonlinear. To accommodate the curvilinear shape ([Figure]), BMI was modeled with restricted cubic splines.12 Restricted cubic splines are a method of describing dose-response curves that make no a priori assumptions about the shape of the curve. Cubic polynomials are fitted between prespecified points on the horizontal axis (knots), and restrictions are placed on the resulting curve to ensure a smooth appearance at those knot points.12 Hypothesis testing can be performed to determine whether the nonlinear components of the model significantly improve the fit to the data. BMI was modeled with a 4-knot restricted cubic spline using Harrell’s Design library13 in S-Plus.10