Application and Presentation of the Edmonton Obesity Staging System in a Lifestyle Medicine Clinic: A Cross-Sectional Study

Abstract

Background

The rising global prevalence of obesity necessitates innovative management strategies. The Edmonton Obesity Staging System (EOSS) offers a more clinically useful approach to assess the attributed obesity risks.

Aims

to evaluate the application of EOSS in a primary care-based Lifestyle Medicine Clinic, describe patient characteristics, and analyze factors associated with higher EOSS stages.

Methods

A retrospective study was conducted on 960 patients aged fourteen and above who visited the Lifestyle Medicine Clinic at a primary care in 2023. Patients with type 2 diabetes, pregnancy, or age over 65 years were excluded. Data were extracted from electronic medical records. Descriptive statistics and analytical statistics were performed, focusing on demographics, vital signs, lifestyle assessments, obesity-related comorbidities, and management interventions, including EOSS staging.

Results

The study population was predominantly female (70.6%), with a mean age of 39.17 years. At the presentation to the Lifestyle Medicine Clinic, the prevalence of physical activity was 13.8% and adherence to a low-calorie diet was 15.7%. The most common comorbidities were prediabetes (70.6%) and dyslipidemia (61.8%). EOSS classification placed 23.6% of patients in stage 0, 58.8% in stage 1, and 17.6% in stage 2, with none in stages 3 or 4. Males and adults aged 40 years and above had significantly higher EOSS stages, p < 0.001 for each. Anthropometric measurements, blood pressure, and HbA1c were significantly higher in patients with higher EOSS stage. The engagement rates in nutrition and health education clinics were 90.6% and 59.2% respectively, with 36.8% required pharmacological intervention for weight loss.

Conclusion

Incorporating EOSS into primary care shows a feasible risk stratification in managing obesity. Further researches are needed to determine the long-term applicability of EOSS and its effect on obesity management in primary care settings.

Introduction

Lifestyles driven by poor nutrition, physical inactivity, smoking, and poor mental health are the root causes of numerous chronic diseases, such as obesity, cancer, cardiovascular disease, and type 2 diabetes. Obesity is considered a complex chronic disease affected by multiple contributory factors beyond lifestyle, including genetics, epigenetics, environmental influences, and culture [1, 2]. It is a major contributor to global morbidity and mortality and a significant risk factor for several chronic conditions, such as cardiovascular diseases, type 2 diabetes, hypertension, dyslipidemia, and some malignancies [3].

The global prevalence and mortality of obesity are steadily increasing, which is significantly impacting public health both directly and indirectly [4, 5]. This global burden is reflected in the regional perspective, including Saudi Arabia. Approximately 43% of the Saudi population are classified as obese, which is higher than the global prevalence of around 30% [6]. This elevated prevalence of obesity reflected the higher prevalence of related comorbidities, such as heart disease, stroke, and diabetes. For instance, the prevalence of diabetes among the Saudi population is among the highest in the world, with a significant proportion of these cases linked to obesity [7]. This stark comparison underscores the urgent need for effective obesity management strategies in Saudi Arabia to mitigate these associated health risks.

Although body mass index (BMI) has been the standard way to define obesity, it has been also used to predict cardiovascular risk and guide treatment decisions. Nevertheless, it has been recognized that BMI is an imperfect surrogate for obesity severity. For its part, BMI fails to distinguish between fat and lean mass or to consider metabolic, functional, or psychological health risks that often accompany obesity. Essentially, some people with high BMI can be metabolically healthy, whereas others with normal BMI can have significant metabolic abnormality [8]. This raises an essential inquiry about the significance of lifestyle factors as risk determinants and necessity a more advanced obesity classification systems.

Several frameworks have been introduced to address the shortcomings of BMI, including the Waist-to-Hip Ratio, King’s Obesity Staging Criteria, and the Edmonton Obesity Staging System (EOSS) [9, 10]. Among these, the standardized EOSS framework developed by Sharma and Kushner (2009) has been recognized as a comprehensive model for obesity classification. It incorporates the severity of comorbidities, functional limitations, and mental health conditions beyond body size [11–14].

The EOSS assigns patients into one of five stages (0–4) based on the extent of obesity-related health risk, informing clinical management [15]. EOSS has been shown to serve as a better predictor of mortality and morbidity than BMI alone. In addition, it may serve as a useful guide when determining the intensity of obesity interventions [14].

EOSS has been the subject of numerous publications and research studies. However, there is limited information on its utility in clinical practice, particularly in primary care and Lifestyle Medicine Clinics. Primary care serves as the frontline of health service provision and is thus a vital setting for the introduction of structured approaches to obesity management [16].

Lifestyle medicine acknowledges the importance of whole-person care. Complementing risk stratification models like EOSS will provide individualized, patient-centered interventions [12]. This is particularly important in Saudi Arabia where there is a growing epidemic of obesity. Understanding how EOSS can be applied in primary care-based obesity management strategies is crucial.

To our knowledge, few studies have investigated the integration of EOSS into clinical workflows within primary care-based lifestyle medicine clinics in this region. This study aimed to describe the general characteristics of patients, to investigate the applicability of EOSS in a primary care-based Lifestyle Medicine Clinic, and to identify factors associated with higher EOSS stages.

Methods

Study Design and Setting

A retrospective cross-sectional study for patients presented at the lifestyle medicine clinic of Wazarat Healthcare Center (WHC), which is the largest Primary Health Care (PHC) facility within the Family and Community Medicine (FCM) Administration at Prince Sultan Military Medical City (PSMMC) in Riyadh, Saudi Arabia. The center serves approximately 1500 patients daily and provides walk-in services, scheduled appointments, and various treatment options, preventive services, health education, and social support through multiple general and specialized clinics [17]. Among these clinics is The Lifestyle Medicine Clinic, established in 2021. This clinic focuses on motivated obese patients who need to lose weight as prescribed by their primary care physicians. In the Lifestyle Medicine Clinic, patients get a comprehensive biopsychosocial assessment, after which EOSS is calculated, and management decisions are determined. Management options include a thorough gradual lifestyle adjustments, weight-loss medication, and, for those who need it, a referral for bariatric surgery [17]. The policy for Lifestyle Medicine clinic states that patients who are in class 3 obesity or in stage 3 or 4 by EOSS should be referred to the obesity clinic at Prince Sultan Military Medical City. In addition, by policy this clinic is not serving patients diagnosed with type 2 diabetes, pregnant individuals, those who are below 14 years old, and those older than 65 years.

Study Participants

Inclusion Criteria

All patients aged 14 years or older who newly visited the clinic during 2023, from January 2023 to December 2023, with complete medical records were included in the study.

Exclusion Criteria

Patients diagnosed with type 2 diabetes, pregnant individuals, those who are below 14 years old, and those older than 65 years were excluded from the study. These individuals are served in the following clinics: type 2 diabetes patients are managed by the chronic illness clinic, pregnant ladies are managed by the antenatal clinics, the pediatrics clinics manage those who are below 14 years old, and those who are above 65 years old are managed by the geriatric clinics, since these individuals have different and special management guidelines.

2.2.3. After applying the inclusion and exclusion criteria, a total of 960 patients were enrolled in the study.

Data Collection

Data was collected from the Lifestyle Medicine Clinic’s electronic health records as entered by the physician during the first patient encounter. The data collection sheet consists of the following seven parts:

• Demographic data: age, sex.

• Anthropometric measurements: height, weight, BMI, and waist circumference.

• Clinical and laboratory data: blood pressure (systolic and diastolic) and glycated hemoglobin (HbA1c) levels.

• Lifestyle assessment: diet, physical activity, smoking.

• Obesity-related comorbidities: hypertension, fatty liver, prediabetes, diabetes, hypothyroidism, depression, and obstructive sleep apnea.

• Obesity management: liraglutide (Saxenda) prescription, referral to a dietitian, health education, psychiatrist, psychologist, endocrinologist, pulmonologist, and clinical pharmacists.

• The EOSS stage and the stage of change were recorded as well.

Working Definitions

The EOSS assesses health complications related to being overweight or obese. The data definitions for the EOSS comorbidities are included in Fig. 1 [15]. In this study, the staging criteria were developed based on the following:

Fig. 1.

Assessment of obesity according to the Edmonton Obesity Staging System (EOSS) and by body mass index (BMI) class [15]

Stage 0: Patients are classified into Stage 0 if they meet all of the following criteria:

• Normal HbA1c levels without a diagnosis of diabetes.

• Normal blood pressure without a diagnosis of hypertension.

• No diagnosis of dyslipidemia.

• Negative depression screening test.

• Negative obstructive sleep apnea screening based on the STOP-BANG questionnaire (S: Snoring, T: Tiredness, O: Observed apnea, P: high blood pressure, B: Body mass index, A: Age, N: Neck circumference, and G: Gender).

Stage 1: Patients are classified into Stage 1 if they exhibit any of the following:

• Prediabetes diagnosis or elevated HbA1c levels.

• Pre-hypertension diagnosis or elevated blood pressure levels.

• Dyslipidemia diagnosis.

• Positive depression screening test, but without requiring referral to a psychiatrist/psychologist.

• Negative obstructive sleep apnea screening based on the STOP-BANG questionnaire.

• Stage 2: Patients are classified into Stage 2 if they meet any of the following conditions:

• Diabetes diagnosis or HbA1c levels indicative of diabetes.

• Hypertension diagnosis or elevated blood pressure levels indicative of hypertension.

• Positive depression screening test requiring referral to a psychiatrist/psychologist.

• Positive obstructive sleep apnea screening by STOP-BANG questionnaire, requiring referral to a sleep study.

Stage 3: Patients classified in this stage have end-organ damage or significant functional impairment due to obesity-related conditions, such as heart disease, stroke, severe osteoarthritis, or advanced diabetic complications.

Stage 4: This stage includes patients with severe, life-threatening obesity-related conditions, such as end-stage organ failure, uncontrolled cardiovascular disease, or severe disability due to obesity.

Other obesity-related comorbidities, such as anxiety, were not scored due to the unavailability of data.

Statistical Analysis

The Statistical Software Package for Social Sciences (SPSS), version 20.0, was used to perform the statistical analysis. Descriptive statistics, including means and standard deviations (SD) and frequencies (percentages), were used to describe the patients’ demographics and clinical characteristics. All continuous variables were tested for normality using histograms and the Kolmogorov–Smirnov test. To assess the association between dependent and independent variables, the chi-square test was used for categorical independent variables and the ANOVA test was used for continuous independent variables. A two-sided p-value < 0.05 was considered statistically significant.

Human Ethics and Consent To Participate Declarations

The study was approved by the Institutional Review Board from the ethical committees of PSMMC (approval no. [HP-01-R079]). This research was conducted in compliance with Helsinki Declaration. Due to the retrospective nature of the study and the utilization of de-identified patient data, informed consent was waived. Collected data were used only for this study, kept secure, and confidentiality was maintained throughout the study.

Consent for Publication

Not applicable.

Clinical Trial Number

Not applicable.

Funding

No external funding or financial support was received for this study.

Results

Out of 960 patients, 678 (70.6%) were female. The average age of the patients was 39.17 ± 10.36 years. The baseline lifestyle assessments revealed that 55 (5.7%) patients were currently smokers, 151 (15.7%) adhered to low-caloric diets, and 132 (13.8%) were physically active for 150 min/week. The anthropometric measurements of the patients were: the mean BMI was 36 ± 4 kg/m², the mean waist circumference was 106 ± 11 cm (114 ± 10 in males, 104 ± 10 cm in females) (Table 1).

Table 1.

Demographic, lifestyle, anthropometric, and clinical characteristics of study patients (N = 960)

Characteristic	Mean ± SD	n	Percentage (%)
Age (years)	39.17 ± 10.36	-	-
Sex
Male	-	282	29.4%
Female	-	678	70.6%
Currently Smokers	-	55	5.7%
On Low-Caloric Diet	-	151	15.7%
Physical Activity (≥ 150 min/week)	-	132	13.8%
Height (cm)	161 ± 9	-	-
Weight (Kg)	93 ± 14	-	-
Body Mass Index (Kg/m²)	36 ± 4	-	-
Waist Circumference (cm)	106 ± 11	-	-
Male Waist Circumference (cm)	114 ± 10	-	-
Female Waist Circumference (cm)	104 ± 10	-	-
Systolic Blood Pressure (mm Hg)	124 ± 14	-	-
Diastolic Blood Pressure (mm Hg)	77 ± 9	-	-
HbA1c (%)	5.70 ± 0.43	-	-

SD: standard deviations, HbA1c: glycated hemoglobin

The most frequent comorbidities among the patients at baseline were prediabetes 678 (70.6%) and dyslipidemia 593 (61.8%), (Table 2).

Table 2.

Comorbidities among the patients (N = 960)

Comorbidities	N	Percentage (%)
Prediabetes	678	70.6%
Dyslipidemia	593	61.8%
Hypertension	184	19.2%
Hypothyroidism	116	12.1%
Depression	54	5.6%
Diabetes	12	1.3%
Moderate to high risk for obstructive sleep apnea by STOP-BANG screening tool	11	1.1%
Fatty liver disease	6	0.6%

STOP-BANG questionnaire (S: Snoring, T: Tiredness, O: Observed apnea, P: high blood pressure, B: Body mass index, A: Age, N: Neck circumference, and G: Gender)

Based on BMI classification, patients who visited the lifestyle clinic within the normal weight range were two (0.2%). Patients with overweight were 41 (4.3%), class 1 obesity were 375 (39.1%), class 2 obesity were 471 (49.1%), and class 3 obesity were 71 (7.4%) (Fig. 2).

Fig. 2.

Body mass index categories (kg/m2) of the patients (n = 960)

The distribution of the patients according to the Edmonton Obesity Staging System (EOSS) was as follows: stage zero, 227 (23.6%); stage one, 564 (58.8%); stage two, 169 (17.6%); and none of them were in stage three or four at presentation (Fig. 3).

Fig. 3.

Distribution of edmonton obesity staging system (EOSS) among the patients (N = 960)

Among the patients at presentation, two (0.2%) were in the contemplation stage, 763 (79.5%) in the preparation stage, 164 (17.1%) in the action stage, and 31 (3.2%) in the maintenance stage (Table 3).

Table 3.

Distribution of the stage of change among the study patients (N = 960)

Stage of change	N	Percentage (%)
Contemplation	2	0.2%
Preparation	763	79.5%
Action	164	17.1%
Maintenance	31	3.2%

At the first visit, 870 (90.6%) were referred to the nutrition clinic and 568 (59.2%) to the health education clinic. In addition, 353 (36.8%) patients were started on medication (Liraglutide-Saxenda) (Table 4).

Table 4.

Additional care needed for patients visiting the lifestyle medicine clinic (N = 960)

Care team	N	Percentage (%)
Dietitian	870	90.6%
Health education	568	59.2%
Psychiatrist	6	0.6%
Psychologist	6	0.6%
Endocrinologist	10	1.0%
Clinical pharmacist	18	1.9%
Pulmonologist	11	1.1%
Medication (Liraglutide-Saxenda)	353	36.8%

Males and individuals aged ≥ 40 years were statistically significantly more likely to have higher EOSS stages (p < 0.001 for both). Lifestyle-related risk factors—including smoking, consuming a low-calorie diet, and engaging in physical activity (≥ 150 min per week)—did not differ statistically significantly across EOSS stages, with p-values of 0.370, 0.659, and 0.804, respectively.

Anthropometric parameters, namely height, weight, BMI, and waist circumference, were statistically significantly higher among patients with advanced EOSS stages (p < 0.001, < 0.001, 0.007, and < 0.001, respectively). Both systolic and diastolic blood pressure were markedly higher in patients with higher EOSS stages (p < 0.001 for each). HbA1c levels were statistically significantly elevated in those with advanced EOSS stages (p < 0.001) (Table 5).

Table 5.

Relationship between Edmonton obesity staging system (EOSS) and demographic, Lifestyle, Anthropometric, and clinical characteristics of the patients (N = 960)

Characteristic	EOSS stage 0 N (%) Mean ± SD	EOSS stage 1 N (%) Mean ± SD	EOSS stage 2 N (%) Mean ± SD	P value
Age (40 and above)	81 (35.7%)	283 (50.2%)	101 (59.8%)	< 0.001
Sex
Male	42 (18.5%)	164 (29.1%)	76 (45.0%)	< 0.001
Female	185 (81.5%)	400 (70.9%)	93 (55.0%)
Currently Smokers	9 (4.0%)	34 (6.0%)	12 (7.1%)	0.370
On Low-Caloric Diet	35 (15.4%)	93 (16.5%)	23 (13.6%)	0.659
Physical Activity (≥ 150 min/week)	29 (12.8%)	81 (14.4%)	22 (13.0%)	0.804
Height (cm)	160 ± 7	161 ± 9	163 ± 10	< 0.001
Weight (Kg)	90 ± 13	93 ± 14	96 ± 14	< 0.001
Body Mass Index (Kg/m²)	35 ± 4	36 ± 4	36 ± 4	0.007
Waist Circumference (cm)	103 ± 11	106 ± 11	108 ± 9	< 0.001
Systolic Blood Pressure (mm Hg)	120 ± 11	121 ± 12	136 ± 14	< 0.001
Diastolic Blood Pressure (mm Hg)	76 ± 7	75 ± 7	86 ± 10	< 0.001
HbA1c (%)	5.1 ± 0.2	5.8 ± 0.5	5.9 ± 0.2	< 0.001

SD: standard deviations, HbA1c: glycated hemoglobin

Discussion

This study presents the applicability of EOSS at a primary care-based lifestyle medicine clinic as an approach to enhance the risk stratification for obesity. The integration of the medical, functunal, and psychological elements into the EOSS provide a comprehensive approach to determine management direction for patients with obesity. The obesity related comorbidities and their consequences on functional health have a significantly a broad range of impact on patient’s quality of life that traditional BMI classifications fail to capture [10].

The EOSS has the potential to be an improvement tool in modifying management strategies regarding obesity [14–16]. It shifts from weight-centric models to health models of care for people living with obesity [14]. Inserting patients into EOSS stages allows the clinician to apply targeted, risk-oriented interventions. In addition, identifying those in stage 3 and above who are most in need of more intensive management. This has been found to increase patient engagement and improve long-term weight management by focusing on total health rather than weight, which removes much of the stigma attached to having obesity [16, 18].

In this study, Older age and male sex correlated with higher EOSS stages, a finding already documented in the literature. Men develop metabolic derangements at lower BMI thresholds compared to women due to different fat distribution between the sexes. Men deposit more visceral fat, which is the main supplier to metabolic syndrome [18, 19].

Patients in this study with advanced EOSS stages presented with significantly higher anthropometric measurements. However, this relationship was not consistent across the literature [20]. A cohort study found that BMI has the lowest concordance in identifying obesity as defined by EOSS when compared with the waist-to-height ratio [10]. In addition, patients with EOSS stage 2–3 often have BMIs only 7–8 kg/m² greater than normal-weight individuals, but they present with significant comorbidities [19, 21]. Other studies found that higher EOSS stages, such as stage 3, may match BMIs similar to those of the lower stages but with significantly poorer health outcomes [19, 22].

This study revealed that blood pressure and HbA1c levels are markedly elevated in patients with higher EOSS stages. This supports previous findings that indicate patients with EOSS stages 1 and 2 have a much greater risk of getting cardiovascular disease and metabolic syndrome than those in stage 0 [13, 14].

This study showed presence of policy in the Lifestyle Medicine Clinic leading to only a small proportion (7.4%) were classified as having class 3 obesity. Beside this, none of the visitors were classified as stage 3 or 4 by the EOSS, had type 2 diabetes, were pregnant, or were under 14 years or over 65 years of age. Therefore, establishing a clear and comprehensive policies and enforcing them in healthcare settings can enhance the overall model of care, and patient safety practices in health systems [23].

The EOSS does provide a systematic way of stratifying risk related to obesity, but challenges remain with its general application in the primary healthcare setting. Advantages of accurate staging can be lost due to variations in provider interpretation criteria, especially those involving mental and functional status assessments [15, 16]. This can be addressed through structured training and decision-support tools that would aid in ensuring some degree of uniformity among different healthcare settings. Another drawback is that the EOSS does not presently include socioeconomic considerations that may affect health risks associated with obesity [2, 16]. These barriers must be addressed for EOSS to work optimally.

This study has its limitations. Being conducted at one clinic from a single center, the findings may not be generalizable across diverse healthcare settings. It used secondary data from electronic medical records, and since these records were not primarily created for research purposes, there was some missing variables, such as data about anxiety, and inconsistencies in clinical documentation. Important patient groups, such as those with type 2 diabetes and people aged below 14 or above 65 years, were excluded from the study, that affect the application of EOSS. This study does not evaluate the long-term outcomes of EOSS-guided interventions. Furthermore, there was no control group in the study to make a comparative analysis between EOSS-based management of obesity and traditional methods that use BMI for classification.

Future research at the primary care level that includes all obesity-related comorbidities are suggested for better understanding the applicability of EOSS. Furthermore, future multicenter research is needed for better generalizability. Longitudinal research will enable the evaluation of the long-term clinical significance of EOSS, its impacts on health outcomes, and the use of healthcare resources. A research with control groups will enable the comparison of the alternative obesity classification system with EOSS in terms of patient adherence to the management recommendations, improving metabolic control, reducing cardiovascular events, and improving quality of life [14, 16].

Conclusion

Integrating the EOSS into primary care appears feasible strategy that strengthens risk stratification in obesity management. In this study, higher EOSS stages were strongly linked with older age, male sex, anthropometric measures, blood pressure, and HbA1c levels. To build on these findings, future prospective researches with larger and more diverse populations are needed to confirm the long-term value of EOSS in routine practice.

Author Contributions

Conceptualization, D.A. and G.G.; Methodology, D.A., G.G., and A.K.; Software, D.A., G.G., and S.A.; Validation, D.A. and G.G.; Formal analysis, M.K., and L.A.; Investigation, D.A. and G.G.; Resources, A.K., D.D., G.G., L.A., and S.A.; Data curation, D.A. and G.G.; Writing—original draft, D.A., G.G., and L.A.; Writing—review & editing, D.A. and G.G.; Visualization, A.K, and S.A.; Supervision, A.K., and M.K.; Project administration.

Data Availability

The data cannot be shared openly to protect the privacy of study participants. However, the original data of this research are available upon request from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.