A major rethink of obesity diagnosis promises better risk prediction, but experts warn it could unintentionally restrict access to care and deepen health disparities.
Study: Defining disease or delaying care? A conceptual and clinical appraisal of the Lancet obesity framework. Image credit: Lee Charlie/Shutterstock.com
Obesity is highly prevalent today and is associated with multiple cardiometabolic risks, besides its adverse impact on neurological, musculoskeletal, and reproductive health. This prompted the Lancet Diabetes & Endocrinology Commission to explore clinically relevant approaches to defining and classifying obesity. The Endocrine Society (ES) published an evaluation of this framework in The Journal of Clinical Endocrinology & Metabolism.
Misclassification of obesity
The definition of obesity has long been the body mass index (BMI). The World Health Organization (WHO) defines obesity as a BMI of 30 kg/m2 or higher, and distinguishes three classes based on the BMI. It is widely used in clinical and epidemiological settings due to its simplicity and reproducibility.
However, it fails to distinguish lean from fat mass or localize fat distribution, or directly reflect organ dysfunction. It also does not predict organ dysfunction. Surrogates like the waist circumference (WC) and waist-to-height ratio (WHtR) may better capture central adiposity and cardiometabolic risk, and evidence supports moving beyond BMI-only assessment. But this can add complexity to the diagnostic process and may still misclassify individuals due to variability in measurement protocols and cut-offs.
Thus, individuals with high muscular mass may be misdiagnosed as obese, and people with low lean and overall mass but visceral fat deposits as normal, despite their high mortality risk. Complementary measures, such as using dual X-ray absorptiometry (DXA), may improve accuracy but are more resource-intensive and less scalable.
Clinical versus preclinical obesity
The Lancet framework is a novel effort to more precisely differentiate clinical from preclinical obesity. It defines clinical obesity using BMI, along with additional anthropometric measures, and evidence of organ dysfunction or functional limitation attributable to obesity. Without such dysfunction, excess adiposity is defined as preclinical obesity. However, such a classification may risk undertreatment or delayed care, despite the elevated risk of future cardiometabolic disease, as demonstrated by prior research.
Adding anthropometric measures may improve risk stratification
Analyses reveal that additional anthropometric criteria do not significantly change obesity prevalence compared to the BMI alone in certain populations. Most individuals identified by the Commission criteria were already captured by the BMI. Adiposity is a continuous rather than a discrete variable, making threshold-based risk predictions challenging.
The new framework did improve cardiometabolic risk prediction, increasing the estimated risk of diabetes, cardiovascular disease (CVD), and mortality, especially in those classified as having clinical obesity. These individuals were at six times the risk of CVD and type 2 diabetes (T2D), and 2.7 times the risk of premature mortality, compared to non-obese, normally functioning individuals.
Dysfunction carries strong prognostic significance independent of obesity status
Even without obesity, the risk of T2D, CVD, and death was higher when there were signs of organ dysfunction. Multiple studies also emphasize the importance of comorbidities in managing obesity.
Requiring evidence of obesity-related dysfunction may cause delays in diagnosis and treatment, especially since most primary care settings provide limited access to diagnostic testing. It may also increase costs and worsen healthcare inequities.
Since many obesity-linked conditions are multifactorial, establishing obesity as their cause may not be feasible. Current management guidelines prioritize WC and comorbidities in treating obesity, making it more difficult to justify applying new criteria on clinical grounds.
Conversely, preclinical obesity becomes a “diagnostically indeterminate” category, which may be interpreted as lower clinical urgency and potentially limit access to treatment despite elevated risk, and whose classification may shift depending on the intensity of clinical evaluation.
In theory, the Commission framework seeks to stratify risk without requiring prior diagnosis, but in managed care, the absence of certified dysfunction often acts as a barrier to treatment, even for high-risk patients. Instead, the ES recommends staging obesity based on risk and harm, commenting that “Policy should link access and treatment intensity to expected benefit.”
Other models may signal more pragmatic approaches
Other models, such as the European Association for the Study of Obesity (EASO) criteria, the Edmonton Obesity Staging System (EOSS), and the AACE Adiposity-Based Chronic Disease (ABCD) model, prioritize adiposity severity and its clinical outcomes. This offers strong predictive support and management guidelines without requiring strict causal attribution of each comorbidity to obesity, and incorporating broader dimensions such as functional status and, in some systems, mental health.
Diabetes as a manifestation of obesity
The ES also disagreed with the Commission’s explicit exclusion of T2D as evidence of clinical obesity. The reasons for disagreement include:
- strong association of T2D with obesity in prevalence and mechanistic pathways
- selective inclusion of other multifactorial conditions
- confusing metabolic criteria, clubbing, hyperlipidemia, and hyperglycemia as a single obesity marker
- potential impact on treatment access, making implementation challenging
Final recommendations
The authors point out that price and affordability, rather than eligibility definitions alone, are the primary constraints on treatment access, and irrespective of diagnostic criteria, more than 99.7 % of eligible adults will be unable to receive treatment due to affordability constraints.
The final recommendations include developing diagnostic protocols that require minimal data; standardized anthropometric measurement protocols; harmonizing clinical staging across classification systems; and validation studies to determine which systems provide the highest prognostic value. Further research into how excess adiposity relates to outcomes such as cancer and mental health across disease stages is also recommended. This may be a more realistic approach and could support more uniform clinical practices.
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