To our knowledge, this is the first large-scale, multinational study to comprehensively evaluate the Lancet Commission diagnostic framework1 against the widely used BMI-based classification. Previous studies have examined individual components of the framework—such as expanded anthropometric criteria or prevalence estimates of clinical obesity—but have lacked integrated assessment or longitudinal follow-up12,13,14,15,16. This study evaluated several interrelated elements of the new diagnostic framework: the implications of using combined anthropometric criteria to confirm obesity; their effects on prevalence and risk stratification compared with BMI alone; the importance of selecting appropriate anthropometric cut-offs; and the prognostic implications of distinguishing clinical from preclinical obesity.

With regard to anthropometric classification, our findings show that BMI alone both over- and under-identifies obesity and does not reliably distinguish individuals who have an increased mortality risk. These results support the Commission framework’s use of complementary anthropometric measures alongside BMI to improve obesity classification and risk stratification. Although WC and WHtR are, like BMI, indirect measures of adiposity, increased values for these measures are (for anatomical and physiological reasons) more likely to reflect fat-mass expansion, whereas a higher BMI might also reflect greater lean mass. Their correlation with direct body-composition measurements10 supports their use as a pragmatic means of detecting excess adiposity where direct measurement is impractical. Future studies should determine whether direct measurement of fat adds meaningful precision and could ultimately replace combined anthropometry if reliable bedside technologies become widely available.

We observed substantial discordance between BMI-based classification and CAD-obesity, with BMI overestimating obesity in 1.2–3.1% and underestimating it in 5.2–8.6% of participants across cohorts. When considered against national obesity estimates from the 2025 World Obesity Atlas17, these proportions suggest that BMI misclassification affects tens of millions of individuals in countries with large populations, underscoring the potential public-health implications of seemingly modest differences in classification.

However, the effects of using a combined anthropometric definition of obesity depend, crucially, on how the Commission’s recommendations are operationalized. Unlike our findings, previous studies in US cohorts reported that applying the Lancet Commission framework substantially increased overall obesity prevalence12,13,14,15,16. These discrepancies mainly reflect methodological differences, particularly the choice of anthropometric cut-offs and incomplete adherence to the framework’s recommendations. In one study14, higher prevalence estimates were driven by the use of a single low WHtR cut-off of 0.50 across populations, potentially classifying individuals with milder increases in adiposity and associated risk as having obesity. Another study16 reported an overall obesity prevalence of 60% in the AoU cohort, compared with 48.1% in our analysis. In addition to using a single WHtR cut-off of 0.50, the study applied the cut-off across the full BMI range, including individuals with a normal BMI. This approach diverges from the Commission’s recommendation that anthropometric confirmation be used mainly in individuals with BMI values below—but near —the BMI obesity threshold1. Applying anthropometric criteria outside this context risks classifying individuals with normal body weight and localized abdominal adiposity—such as those with lipodystrophy or related conditions, which are biologically and clinically distinct—as having obesity. For this reason, the Commission retained BMI as the initial screening tool1, and we limited the assessment of obesity to participants within the conventional BMI-defined overweight and obesity ranges.

In closer alignment with the Commission’s guidance, we applied obesity-level, ethnicity-specific thresholds for WC and WHtR. In our data, the conventional WHtR cut-off of 0.50 corresponded approximately to the BMI-defined overweight threshold, whereas WHtR values corresponding to the BMI-defined obesity threshold and increasing mortality risk were generally observed around or above 0.55. This convergence between anthropometric and mortality analyses supports the use of higher WHtR thresholds for obesity classification and is consistent with the previously proposed cut-off of 0.55–0.59 (ref. 10). Further prospective evaluation will be required to refine population-specific thresholds.

The application of obesity-level thresholds for WC and WHtR further illustrates the limitations of BMI-based classification. Although these measures remain indirect proxies of body fat, the reclassified groups had distinct mortality profiles, showing that conventional BMI categories encompass individuals with heterogeneous risk and that additional measures provide clinically relevant stratification beyond BMI alone. This is particularly consequential for the overweight category. The Lancet Commission framework intentionally does not retain overweight as a separate intermediate-risk category; instead, it extends the definition of obesity to individuals with a BMI classified as overweight who meet additional anthropometric criteria indicating obesity-level adiposity. Our data support this approach: individuals with a BMI in the overweight range who met CAD-obesity criteria had a mortality risk comparable with that of individuals with a BMI in the obesity range who also met CAD-obesity criteria. By contrast, those with a BMI classified as overweight who did not meet CAD-obesity criteria had no excess risk and, in several analyses, had a lower risk than did individuals with a normal BMI. The CAD-obesity criteria therefore identify adiposity-related risk that would otherwise remain obscured within the conventional overweight range.

The implications are substantial. An estimated 1.5 billion adults worldwide are classified as overweight by BMI17, yet our findings indicate that individuals in this population should not all be regarded as being at increased risk. Our data suggest that combining BMI with WC and appropriately calibrated WHtR cut-offs, such as those evaluated here, can identify higher-risk individuals without substantially increasing overall obesity prevalence. This selective reclassification provides a clinically meaningful basis to inform individual care, health policy and resource allocation.

Anthropometric confirmation improves the identification of mortality risk associated with excess adiposity. However, because clinical obesity denotes an active disease state, its diagnosis requires a further step: establishing the presence of organ dysfunction that can plausibly be attributed to that adiposity. This requirement reflects a fundamental principle of disease definition: because similar alterations in organ function can arise from different disease processes, causality cannot be inferred from coexistence alone. For example, not all joint inflammation is caused by rheumatoid arthritis, nor is all heart failure attributable to the same underlying heart disease. In clinical practice, differential diagnosis addresses this uncertainty by considering alternative causes and, where possible, excluding them; it is therefore essential to diagnostic specificity. For this reason, organ dysfunction coexisting with obesity cannot automatically be considered obesity-related; doing so could overattribute dysfunction to obesity and substantially inflate estimates of clinical-obesity prevalence and incidence. Most previous applications of the framework classified all coexisting organ dysfunction as obesity-related without accounting for alternative causes13,14,15,16, and might therefore have overestimated the prevalence of clinical obesity.

To mitigate this problem, we applied the AF method at the population level to estimate the proportion of organ dysfunction that is statistically attributable to CAD-obesity, and adjusted the prevalence and incidence estimates accordingly. Although this adjustment cannot establish causality in individual participants, it mitigates the risk of attributing organ dysfunction to obesity when other causes could be responsible, thereby reducing systematic overattribution and improving population-level estimates of the prevalence and incidence of clinical obesity. Notably, an earlier US study12 using a different adjustment approach estimated the clinical-obesity prevalence as 9.7%, close to the 10.9% that was observed in the AoU cohort after AF adjustment. This concordance between studies using different population-level methods to account for plausible attribution provides some reassurance about the robustness of our estimates.

Our longitudinal and mortality analyses provide evidence supporting the distinction between clinical and preclinical obesity. Despite variation across cohorts, 70–90% of individuals with preclinical obesity did not progress to clinical obesity over 10 years. Clinical obesity was also associated with substantially higher all-cause and obesity-related mortality, whereas preclinical obesity showed only a modest excess mortality risk. The observed patterns of progression and differences in mortality risk show that these health states differ not only in their defining clinical characteristics but also in their prognostic implications. The distinct clinical and prognostic profiles of clinical and preclinical obesity provide a foundation for personalized, proportionate care. Preclinical obesity warrants a mostly preventive approach aimed at reducing future risk, with the nature and intensity of intervention guided by the individual’s risk of progression and the presence of relevant comorbidities. By contrast, established functional impairment and higher mortality risk in clinical obesity justify greater urgency and, where appropriate, more intensive treatment and prioritized access. The distinction can therefore guide the purpose, timing and intensity of individual care and support evidence-based decisions on treatment prioritization, coverage and resource allocation.

Notably, using this distinction to guide proportionate care does not imply withholding treatment from people with preclinical obesity. One criticism of the Lancet Commission framework is that distinguishing preclinical from clinical obesity could encourage ‘watchful waiting’ or restrict access to treatment18,19,20. However, this concern seems to conflate diagnostic classification with treatment eligibility. Like any diagnosis, the distinction between clinical and preclinical obesity provides a snapshot of an individual’s current health status; it does not prescribe inaction or determine eligibility for treatment by itself. Analogous distinctions—such as those between prediabetes and diabetes or adenomatous polyps and carcinoma—guide the purpose, urgency and intensity of intervention without precluding care for prediabetes or adenomatous polyps21. Accordingly, preclinical obesity might warrant active preventive treatment, including lifestyle, pharmacological or, in selected cases, surgical interventions, depending on progression risk and comorbidities that could benefit from weight reduction. Treatment decisions should therefore be informed by diagnosis but not reduced to diagnostic category alone.

Several limitations should be acknowledged. The retrospective design of our study and its reliance on electronic health record (EHR) data could limit diagnostic precision and introduce inter-cohort variability. Organ dysfunction was identified using administrative codes rather than standardized clinical assessments, and not all 18 dysfunction indicators were available in every cohort, potentially leading to the underestimation or misclassification of specific organ disease subtypes. This limitation is highlighted by the Kailuan cohort, in which the higher observed incidence of clinical obesity might reflect more frequent (biennial) clinical evaluations. However, sensitivity analyses showed that the additional indicators contributed minimally to classification, supporting the conclusion that differences in the number of available organ-dysfunction indicators are unlikely to be a major driver of the inter-cohort variability observed in the primary analyses. Finally, the AF method cannot determine attribution at the individual level, and future prospective studies using clinically adjudicated diagnoses of clinical and preclinical obesity will be essential to refine estimates of disease burden and progression. Nonetheless, the consistency of findings across geographically and demographically diverse cohorts strengthens the robustness, generalizability and clinical relevance of the proposed diagnostic framework.

In summary, BMI alone misclassifies obesity in a substantial proportion of individuals, both overestimating and underestimating the clinical relevance of excess adiposity, and does not meaningfully discriminate higher mortality risk. Integrating other anthropometric measures (such as WHtR and WC) and clinical criteria reflecting organ function into a multi-criteria framework improves obesity detection and aligns diagnosis more closely with true disease status. Clinical and preclinical obesity represent distinct and prevalent subgroups, with markedly different mortality risks. This study demonstrates the clinical utility and prognostic importance of distinguishing between clinical and preclinical obesity, and supports the global applicability of this classification scheme for guiding care and informing health policy.