Disentangling Clinical and Preclinical Obesity: Their Distinct Impacts on All-Cause Mortality Risk in the UK Biobank Study

Highlight

• Clinical obesity, defined by obesity-related dysfunctions plus excess anthropometric measures, shows a strong association with all-cause mortality.
• Preclinical obesity without dysfunction also contributes to a modest but significant increase in mortality risk.
• Risk stratification across clinical and preclinical states suggests the need for early screening and management of obesity-related dysfunctions.
• Findings reinforce the heterogeneity of obesity phenotypes and their distinct prognostic implications over a mean 13.4-year follow-up.

Study Background

Obesity is a significant and growing public health challenge worldwide, closely linked with numerous comorbidities including cardiovascular disease, diabetes, and certain cancers. Traditionally, obesity has been defined primarily through anthropometric parameters such as body mass index (BMI). However, recent efforts have highlighted the importance of integrating obesity-related dysfunctions—such as metabolic, cardiovascular, or inflammatory disturbances—into the classification of obesity. The concept of “clinical obesity” reflects this complex phenotype, differentiating it from “preclinical obesity,” where excess adiposity exists without overt dysfunction.

Despite broad recognition of obesity as a mortality risk factor, the differential contributions of clinical versus preclinical obesity phenotypes to long-term mortality risk remain unclear. This ambiguity hampers targeted risk stratification and tailored preventive interventions.

Study Design

This study leveraged data from 232,721 participants of the UK Biobank, a large prospective cohort with detailed phenotyping and long-term follow-up. Participants were categorized into six mutually exclusive clusters based on baseline and follow-up assessments of obesity status and presence of obesity-related dysfunctions:

• Cluster 1: No obesity, no dysfunction (reference)
• Cluster 2: No obesity at baseline, dysfunctions developed during follow-up
• Cluster 3: No obesity, but baseline dysfunctions present
• Cluster 4: Preclinical obesity (excess adiposity without dysfunction) maintained during follow-up
• Cluster 5: Preclinical obesity at baseline with dysfunctions developed during follow-up
• Cluster 6: Clinical obesity at baseline (obesity with dysfunctions)

Obesity was defined using combined anthropometric measures, while dysfunctions included various metabolic and physiological abnormalities attributable to obesity. Time-dependent Cox proportional hazards regression models were used to estimate hazard ratios (HRs) for all-cause mortality over a mean 13.4-year follow-up with adjustment for key confounders.

Key Findings

During the follow-up, 19,704 deaths occurred. The main findings were:

All-Cause Mortality Risk by Cluster:
– Cluster 1 (reference): HR = 1.00
– Cluster 2: HR = 1.99 (95% CI: 1.88–2.10)
– Cluster 3: HR = 2.02 (95% CI: 1.85–2.20)
– Cluster 4: HR = 1.15 (95% CI: 1.09–1.22)
– Cluster 5: HR = 1.97 (95% CI: 1.87–2.07)
– Cluster 6: HR = 2.30 (95% CI: 2.16–2.44)

The highest mortality risk was observed in Cluster 6—participants with baseline clinical obesity, indicating that the presence of dysfunctions alongside excess adiposity substantially elevates mortality risk. Participants with obesity but without dysfunction progression during follow-up (Cluster 4) had a smaller but statistically significant increased risk.

Temporal Dynamics:
Mortality risks varied when stratified by follow-up duration. Within five years, elevated risk was attenuated or non-significant in some clusters, highlighting that mortality risk associated with clinical and preclinical obesity becomes more pronounced with longer follow-up (up to 10 years and beyond).

Pairwise Comparisons:
Using Cluster 4 (preclinical obesity without dysfunction progression) as a reference, Cluster 5 (preclinical obesity with dysfunction progression) and Cluster 6 had significantly higher mortality risks (HRs 1.66 and 2.03 respectively). Cluster 3 (non-obesity with baseline dysfunctions) showed mortality risk comparable to Cluster 6, underlining the critical impact of dysfunction irrespective of obesity status.

Survival Analysis:
Kaplan–Meier curves demonstrated Cluster 6 having the lowest survival probability throughout follow-up, with an early and sustained decline. By contrast, Cluster 1 participants had the highest survival with only gradual decline over time.

Subgroup Findings:
Elevated mortality risk in Cluster 6 persisted across demographic subgroups and was notably higher in participants with higher educational attainment, indicating complex socio-biological interactions.

Expert Commentary

This landmark cohort study clarifies the nuanced relationship between obesity phenotypes and long-term mortality risk. By integrating dysfunction measures, the authors go beyond traditional BMI-based definitions, addressing the heterogeneity of obesity-related health outcomes. Their findings highlight that clinical obesity—defined by excess adiposity plus organ/system dysfunction—confers the greatest mortality risk.

These results underscore inadequacies of BMI alone as a prognostic tool and raise compelling arguments for incorporating metabolic and functional assessments into routine obesity evaluation. The modest but significant risk associated with preclinical obesity, even without dysfunction, suggests a window of opportunity for early intervention to prevent progression.

Some limitations include potential residual confounding inherent to observational cohort studies, and lack of granular data on specific dysfunction types that might differentially contribute to mortality. Nonetheless, the extensive size, rigorous methodology, and long follow-up strengthen the validity and generalizability of the findings.

Conclusion

The UK Biobank analysis delineates that clinical obesity, characterized by obesity-related dysfunction, poses the highest risk of all-cause mortality. Preclinical obesity also contributes meaningfully to mortality, though to a lesser extent. These data advocate for concerted efforts towards early screening, prevention, and management of dysfunctions among individuals with obesity to mitigate mortality risk. Integrating functional assessments with anthropometric screening can enhance risk stratification and guide more individualized clinical decision-making.

Funding and ClinicalTrials.gov

The study was supported by institutional funding. No clinical trial registration was applicable as this was an observational cohort analysis.

References

Xu M, Li M, Zhang Y, Li L, Shen Y, Hu G. Contributions of Clinical Obesity and Preclinical Obesity to the All-Cause Mortality Risk: Findings From the UK Biobank Cohort. Diabetes Metab Res Rev. 2025 Oct;41(7):e70095. doi: 10.1002/dmrr.70095 IF: 6.0 Q1 B3. PMID: 41100428 IF: 6.0 Q1 B3; PMCID: PMC12530463 IF: 6.0 Q1 B3.

Additional background references:
1. Hruby A, Hu FB. The Epidemiology of Obesity: A Big Picture. Pharmacoeconomics. 2015 Jul;33(7):673-89.
2. Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019 May;15(5):288-298.
3. Lavie CJ, De Schutter A, Milani RV. Healthy obesity: the obesity paradox. Nat Rev Endocrinol. 2015 Jan;11(1):55-62.