A major overhaul of obesity diagnosis promises to better predict risk, but experts warn it could unintentionally limit access to care and widen health disparities.
Research: Defining the disease or delaying treatment? A conceptual and clinical evaluation of the Lancet Obesity Framework. Image credit: Lee Charlie/Shutterstock.com
Obesity is currently highly prevalent and is associated with multiple cardiometabolic risks in addition to negative neurological, musculoskeletal, and reproductive health effects. This led the Lancet Diabetes and Endocrinology Committee to consider clinically relevant approaches to defining and classifying obesity. The Endocrine Society (ES) published an evaluation of this framework in the following paper: Journal of Clinical Endocrinology and Metabolism.
Misclassification of obesity
The definition of obesity has long been body mass index (BMI). The World Health Organization (WHO) defines obesity as a BMI of 30 kg/m2 or higher and divides obesity into three classes based on BMI. Due to its simplicity and reproducibility, it is widely used in clinical and epidemiological settings.
However, it cannot distinguish between lean mass and fat mass, localize fat distribution, or directly reflect organ dysfunction. It also does not predict organ dysfunction. Proxies such as waist circumference (WC) and waist-to-height ratio (WHtR) may better capture central obesity and cardiometabolic risk. And the evidence supports more than an assessment of BMI alone. However, this can complicate the diagnostic process and may also lead to misclassification of individuals due to variations in measurement protocols and cutoffs.
Therefore, people with high muscle mass may be misdiagnosed as obese, and people with low lean body mass or overall body weight but normal visceral fat accumulation may be misdiagnosed as obese despite their increased risk of death. Complementary measures, such as the use of dual X-ray absorptiometry (DXA), may improve accuracy but are more resource intensive and less scalable.
Clinical obesity and preclinical obesity
The Lancet framework is a new effort to more accurately distinguish between clinical and preclinical obesity. It defines clinical obesity using BMI, additional anthropometric measurements, and evidence of organ dysfunction or functional limitations due to obesity. In the absence of such dysfunction, excess fat accumulation is defined as preclinical obesity. However, as demonstrated in previous studies, such classification may lead to undertreatment or treatment delay, despite the increased risk of future cardiometabolic disease.
Adding anthropometric measurements may improve risk stratification
Analysis revealed that additional anthropometric measures do not significantly change obesity prevalence compared to BMI alone in a given population. Most individuals identified by the Commission’s criteria are already captured by BMI. Obesity is a continuous rather than a discrete variable, making threshold-based risk prediction difficult.
The new framework has improved cardiometabolic risk prediction and increased estimated risks of diabetes, cardiovascular disease (CVD), and mortality, particularly for people classified as clinically obese. These people had a six times higher risk of CVD and type 2 diabetes (T2D) and a 2.7 times higher risk of premature death compared to non-obese, normally functioning people.
Dysfunction has strong prognostic importance, independent of obesity status
Even in the absence of obesity, signs of organ dysfunction increase the risk of T2D, CVD, and death. Studies have also highlighted the importance of comorbidities in the management of obesity.
When evidence of obesity-related dysfunction is required, diagnosis and treatment can be delayed due to limited access to diagnostic tests, especially in primary care settings. It could also increase costs and exacerbate health care disparities.
Many of the symptoms associated with obesity are multifactorial, so establishing obesity as the cause may not be realistic. Current management guidelines prioritize toileting and comorbidities in the treatment of obesity, making it more difficult to justify the application of new criteria on clinical evidence.
Conversely, preclinical obesity may become a “diagnostically indeterminate” category, interpreted as having less clinical urgency, potentially limiting access to treatment despite high risk, and its classification may change depending on the intensity of clinical evaluation.
In theory, the committee’s framework seeks to stratify risk without requiring a prior diagnosis, but in managed care, the lack of recognized impairment is often a barrier to treatment, even in high-risk patients. Instead, the ES recommends grading obesity based on risk and harm, commenting that “policies need to link access and treatment intensity to expected benefits.”
Other models may represent a more realistic approach
Other models, such as the European Association for the Study of Obesity (EASO) criteria, the Edmonton Obesity Staging System (EOSS), and the AACE fat depot-based chronic disease (ABCD) model, prioritize the severity of obesity and its clinical outcomes. This provides strong predictive support and management guidelines without requiring a strict causal relationship of each comorbidity to obesity and without incorporating broader aspects such as functional status and, in some systems, mental health.
Diabetes as a symptom of obesity
The ES also disagreed with the committee’s explicit exclusion of T2D as evidence of clinical obesity. Reasons for disagreement include:
- Strong association between T2D and obesity in prevalence and mechanistic pathways
- Selectively include other multifactorial conditions
- Confounding metabolic criteria, clubbing, hyperlipidemia, and hyperglycemia as a single obesity marker
- Can impact access to treatment and is difficult to implement
Final recommendations
The authors note that price and affordability, not just the definition of eligibility, are the main constraints on treatment access, with more than 99.7% of eligible adults unable to receive treatment due to affordability constraints, regardless of diagnostic criteria.
Final recommendations include the development of diagnostic protocols that require minimal data. Standardized anthropometric measurement protocols. Harmonizing clinical staging across classification systems. Validation studies to determine which system provides the highest prognostic value. Further research into how excess obesity is related to conditions such as cancer and mental health across different disease stages is also recommended. This may be a more realistic approach and may support more uniform clinical practice.
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