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Obesity and its relation to quality of life are multifaceted. The purpose of this paper was to contribute evidence to support a framework for understanding the impact of obesity on quality of life in 42 morbidly obese subjects considering a wide number of potential determinants. A model of weight-related quality of life (WRQL) was developed based on the Wilson-Cleary model, considering subjects' weight characteristics, arterial oxygen pressure (PaO_{2}), walking capacity (6-minute walk test, 6MWT), health-related quality of life (HRQL; Physical and Mental Component Summaries of the SF-36 PCS/MCS), and WRQL. The model of WRQL was tested with linear regressions and a path analysis, which showed that as PaO_{2} at rest increased 6MWT increased. 6MWT was positively associated with the PCS, which in turn was positively related to WRQL along with the MCS. The model showed good fit and explained 38% of the variance in WRQL.

The obesity epidemic currently facing the developed world [

Obesity can and does produce disability [_{2}), increased alveolar-to-arterial oxygen pressure difference (AaDO_{2}) at rest, reduced respiratory system compliance with increased elastic loading of the inspiratory muscles, increased work and oxygen cost of breathing, and increased respiratory resistance compared to normal-weight individuals.

Nonetheless, obesity can affect almost all aspects of function and extend to impacting on quality of life (QOL) and health related quality of life (HRQL). The term “weight-related quality of life” (WRQL) is emerging to express the effect of excess weight on an individual’s ability to live a fulfilling life [

The factors affecting the quality of life of obese people remain to be identified. Body mass index (BMI) explains only about one-third of the variance of WRQL [

Measures of body size have been implicated with respiratory functions including PaO_{2} [_{2} decreases by 5 mmHg and AaDO_{2} increases by 5 mmHg as waist-to-hip ratio increases by 0.1 units [_{2} by 1 and decreases AaDO_{2} by 1 mmHg [_{2max}) supports a direct link between weight and mobility [_{2max} and 6MWT) [_{2max} [

To do justice to the complexity of the relationships underlying WRQL requires a strong theoretical framework for estimating direct and indirect effects of these multiple constructs. In this study, the Wilson-Cleary was the theoretical model used to inform the statistical approach. The Wilson-Cleary model provides a conceptual model that encompasses HRQL and QOL [

The aim of this study was to contribute evidence to support a framework for understanding the impact of obesity on quality of life, considering a wide number of potential determinants. The framework of WRQL was tested with linear regressions and a path analysis.

Morbidly obese persons scheduled for laparoscopic gastric bypass were recruited at the McGill University Health Center (MUHC), Montreal, Canada. They did not have (1) BMI ≥ 75 kg/m^{2}, (2) a medical contraindication to exercise testing (acute myocardial infarction, cardiac arrhythmia, or use of pacemaker); (3) respiratory, renal, or hepatic failure; (4) metastatic disease; (5) cognitive impairment. All participants signed an informed consent form. The measures were collected at the time of their assessment. The study was approved by the MUHC institutional review board.

Arterial blood gases were obtained from radial artery cannulation and sampled after 5 minutes of rest, with the participants sitting upright on a chair. The average of duplicate samples was recorded. Arterial blood-gases were corrected for changes in arterial blood temperature and measured directly via an ABL725 Blood Gas Analyzer (Radiometer, Copenhagen, Denmark). Details of the procedure are described elsewhere [

A test to determine peak oxygen uptake (VO_{2peak}) was performed on an electrically braked cycle ergometer (Velotron Dynafit Pro, Racermate Inc., Seattle, WA). The VO_{2peak} test commenced at 5 W and increased by 1 W every 2 to 6 seconds (10 to 30 watts every minute) until volitional exhaustion. The difference in the incremental increase in power output was to make sure all individuals fatigued within 8 to 12 minutes. VO_{2} was measured with a metabolic cart (model VMax 229LV, Sensorsmedics, Yorba Linda, CA) using the breath-by-breath option. The mean of the highest three consecutive VO_{2} values (averaged over 20-second intervals) was defined as the VO_{2peak}.

6 MWT is the distance an individual can walk in 6-minutes; it reflects the capacity of an individual to perform daily activities. Typically, the distance walked by people with severe obesity revolves around 440–475 meters [

The SF-36 is a 36-item survey that includes eight domains measuring physical functioning, role limitations due to physical health problems, bodily pain, general health perceptions, vitality, social functioning, role limitations due to emotional problems, and mental health. A physical and mental component summary (PCS and MCS) can be derived from these items. The summary scales are standardized with a mean of 50 and a standard deviation of ten [

The IWQOL-Lite is a self-reported questionnaire designed to assess the impact of obesity on quality of life. It is comprised of 31 items grouped into five dimensions: physical functioning, self-esteem, sexual life, public distress, and work. The measure provides scores for each separate dimension and a total score. The measure has excellent psychometric properties with good internal consistency (ranging from 0.90 to 0.96) [

A model of WRQL was developed, based on the Wilson-Cleary model and existing evidence. The relationships between variables representing the different components within the Wilson-Cleary model were estimated with linear regressions. The statistical significance and the consistency with the literature in the direction of the coefficients were examined.

We further tested the model of WRQL with a path analysis. Path analysis allows the simultaneous estimation of all relationships between the variables in a single model rather than a series of models. It estimates the effect of each variable on the subsequent one, controlling for prior variables. A variable can be a dependent variable in one relationship and an independent variable in another. The several related relationships between biological, physiological, functional factors, and WRQL were modeled. We included variables that have been shown to have a significant impact on quality of life among people with obesity in previous studies. We used the software MPlus version 4 to confirm the model. The extent to which the data was consistent with the model was then tested with the maximum likelihood estimate method (ML). The full information maximum likelihood (FIML) estimation was used to impute data that were considered missing randomly. The latter is the recommended method as it yields consistent and efficient estimates [

Table

Characteristics of the study population by gender.

Men ( | Women ( | |||

Mean | SD | Mean | SD | |

Age (years) | 43 | 8.9 | 38 | 10.2 |

Weight (kg) | 159 | 35.5 | 136* | 19.0 |

Height (m) | 1.78 | 0.07 | 1.64* | 0.07 |

BMI (kg/m^{2}) | 50 | 9.8 | 51 | 6.7 |

Hip (cm) | 140 | 19.2 | 147 | 12.7 |

Waist (cm) | 148 | 18.0 | 135* | 14.5 |

Waist-to-hip ratio | 1.06 | 0.07 | 0.92* | 0.08 |

VO_{2peak} (mL/kg/min) | 16.2 | (4.9) | 14.4 | (2.9) |

VO_{2peak} (L/min) | 2.58 | 0.78 | 1.96* | 0.39 |

VO_{2} at rest (L/min) | 0.42 | 0.10 | 0.33* | 0.06 |

PaO_{2} at rest (mmHg) | 83 | 11.3 | 91* | 9.8 |

6MWT (M) | 429 | 110.5 | 414 | 83.3 |

Physical functioning | 32 | 27.8 | 43 | 24.2 |

Role physical | 49 | 41.0 | 47 | 41.9 |

Bodily pain | 52 | 28.4 | 45 | 23.9 |

GHP | 43 | 13.8 | 41 | 19.5 |

Vitality | 39 | 13.1 | 36 | 16.1 |

Social functioning | 58 | 26.1 | 57 | 29.0 |

Role emotional | 59 | 44.9 | 64 | 40.4 |

MH | 63 | 17.5 | 60 | 19.4 |

PCS | 35 | 11.3 | 33 | 9.8 |

MCS | 44 | 9.8 | 44 | 9.9 |

IWQOL overall | 55 | 24.7 | 40* | 21.3 |

Public distress | 56 | 37.3 | 37* | 20.5 |

Physical function | 45 | 25.0 | 36 | 16.1 |

Self-esteem | 49 | 29.1 | 24* | 25.6 |

Sexual life | 46 | 21.0 | 35 | 28.7 |

Work | 45 | 22.9 | 36 | 22.6 |

Abbreviations: BMI, body mass index; 6MWT, 6-minute walk test; MCS, mental component summary; PCS, physical component summary; MHI, Mental Health Index; GHP, general health perception; IWQOL, impact of weight on quality of life—lite. The parentheses in italic are norms for the SF-36 [_{2} [_{2peak} (mL/kg/min) the classification of “fair” is the 40th percentile value for age and gender from the American College of Sports Medicine [

The WRQL model consisted of the linear effect of waist circumference on PaO_{2} at rest, PaO_{2} at rest on 6MWT, 6MWT on PCS, PCS on IWQOL-Lite, and MCS on IWQOL-Lite. Table _{2} at rest on 6MWT (_{2} at rest, PaO_{2} at rest was a predictor of walking capacity, which in turn predicted PCS. Both PCS and MCS were predictors of IWQOL-Lite.

Univariate linear regression models.

Outcomes | |||||

PaO_{2} at rest | 6MWT | PCS | IWQOL | ||

(mmHg) | (meters) | ||||

Predictors | Waist circumference (cm) | ||||

PaO_{2} at rest (mmHg) | |||||

6MWT (meters) | |||||

PCS | |||||

MCS | |||||

*Significant at <0.05.

Four models were tested for each outcome. The first model estimated the effect of waist circumference on PaO_{2} at rest, the second model estimated the effect of waist circumference and PaO_{2} at rest on 6MWT, the third estimated the effect of waist circumference, PaO_{2} at rest, and 6MWT on PCS, and finally the fourth estimated the effect of all these factors together on IWQOL. Tables _{2} at rest, 6MWT, PCS, and MCS was 28%. These results show the limitations of regression as there is only one outcome variable and the predictors must act independently in order to have an effect. Numerous regressions need to be estimated to assess the relationship between several variables. In the multivariable model, each variable is adjusted for the effects of the others and hence their effects on other variables are not estimable.

Multiple linear regression models.

Outcomes | |||||

PaO_{2} at rest | 6MWT | PCS | IWQOL | ||

(mmHg) | (meters) | ||||

Predictors | Waist circumference (cm) | ||||

PaO_{2} at rest (mmHg) | |||||

6MWT (meters) | |||||

PCS | |||||

MCS |

The effect of the predictors is estimated in four regression models for four different outcomes. *Significant at <0.05.

A path Analysis was conducted (Table _{2} at rest decreased (_{2} at rest increased, the distance walked in 6-minutes increased (

Subpath estimates from the path analysis.

Estimate of effect | |

Waist → PaO_{2} | −0.234* (0.093) |

PaO_{2} → 6MWT | 2.585* (1.265) |

6MWT → PCS | 0.051* (0.015) |

PCS → IWQOL | 1.148* (0.287) |

MCS → IWQOL | 0.872* (0.313) |

The values are unstandardized beta coefficients and standard error in brackets.

Univariate and multiple linear regression models to predict WRQL.

Beta coefficient | Adjusted | ||
---|---|---|---|

Waist circumference (cm) | −0.180 | 0.411 | −0.008 |

PaO_{2} at rest (mmHg) | −0.057 | 0.869 | −0.025 |

6MWT (meters) | 0.049 | 0.224 | 0.013 |

PCS | 1.067 | 0.002* | 0.220 |

MCS | 0.818 | 0.029* | 0.101 |

0.275 | |||

Waist circumference (cm) | −0.041 | 0.858 | |

PaO_{2} at rest (mmHg) | −0.006 | 0.987 | |

6MWT (meters) | −0.015 | 0.712 | |

PCS | 1.122 | 0.005 | |

MCS | 0.918 | 0.015 |

Abbreviations: PaO_{2}, arterial blood gases at rest; 6MWT, 6-minute walk test; PCS, physical component summary; MCS, mental component summary.

*Significant at <0.05.

The path to quality of life for morbidly obese men and women. The coefficients correspond to the regression coefficient.

Univariate regressions: (a) relationship between PaO_{2} at rest and 6-minute walk test. (b) Relationship between 6-minute walk test and SF-36 Physical Component Summary. (c) In dashed line, the relationship between SF-36 Physical Component Summary (PCS) and IWQOL, and in solid line, the relationship between SF-36 Mental Component Summary (MCS) and IWQOL.

The summary statistics suggested excellent fit of the data (_{2}, 22% of PCS, 9% of 6MWT, and 38% of IWQOL-Lite.

About 6% of Americans are morbidly obese [

HRQL measures focus mainly on functioning, and fail to consider that people with chronic disease, such as obesity, may have adapted by finding alternative ways to have a satisfactory life. Some authors argue that we are really measuring perceived health and not HRQL with our present measures. That is, to assess HRQL adequately, the measures would have to incorporate the values and meanings an individual places on a given function [

The statistical significance of the relationships between the components of the model of WRQL was demonstrated with linear regression. Path analysis was used to further test the model. Path analysis is the optimal approach to test a model as it evaluates an entire hypothesized multivariate model. It allows for the estimation of the direct effect of a variable on another, as well as the indirect effect of a variable on another through an intervening variable. Consequently, path analysis has the capacity to assess complex models. The model revealed by the analysis is consistent with the theory and evidence. It supports the Wilson-Cleary conceptual model of HRQL. As shown here, the measures of body size were all highly correlated, but the model with waist size had the best fit. Based on correlations and regression models, waist-to-hip ratio has been found to predict PaO_{2}, and an increase of waist-to-hip ratio is associated with lower resting PaO_{2} [

There are some limitations to our study. The analysis was conducted on a small sample size. The determination of the minimum sample size required in path analysis is complex. Unlike in a linear regression, where the sample size depends on the ratio of subjects to variables, in a path analysis it depends on the number of parameters to be estimated. There is little empirical basis for any particular recommendations. The model estimated is simple consisting of six variables and 5 paths (sample size of 42 for 13 free parameters). The variables are normally distributed, and there were no convergence problems or improper solutions, such as negative variance estimates or Heywood cases. There may be other valid alternative models, and some relationships may have been excluded from the model by lack of power, but the relationships included are undeniably significant. In addition, a path analysis (sometimes called causal modeling) tests theoretical propositions about cause and effect without manipulating variables. In this study the propositions are supported by this method of decomposing correlations but the result does not prove that the causal assumptions are correct. Therefore the results should be considered as preliminary and need to be cross-validated in other samples. The model explained 38% of the variance of IWQOL-Lite, which may appear small but can be expected considering the complexity of WRQL and the limited spectrum of variables available on this sample. The use of latent variable representing many observed indicators may help to explain more variance in future studies. Other variables such as weight history, readiness for change, and physical activity habits would be interesting to examine. Furthermore, the presence of binge-eating disorder was not evaluated, which could have affected the results. Finally, the analysis is based on the Wilson-Cleary model which proposes linear relationships between variables. This linear model may not be optimal to capture the network of factors around WRQL.

In conclusion, waist circumference, PaO_{2}, functional walking capacity, and mental health were predictors of WRQL. Health professionals should address these factors.

The paper has not been published elsewhere and is not under simultaneous consideration by another journal. Previous reports of the same or very similar work has not been published. N. Christou is a consultant for Ethicon Endo-Surgery Inc. and has stock ownership in Weight Loss Surgery.

The authors have no financial or other relationships that might lead to a conflict of interest, and the paper has been read and approved by all the authors.

Some funds for this paper were provided by the Quebec Health Research Foundation (Fonds de Recherche en Santé du Québec).