Validation of Predictive Equations for Estimating Lean Soft Tissue and Fat-Free Mass in Class II–V Obesity: A Multicenter Observational Study with Implications for Bariatric Surgery Monitoring

Background: Preoperative analysis of body composition is critical for anticipating metabolic changes and optimizing weight loss outcomes after bariatric surgery (BS). This study aimed to assess agreement between dual-energy X-ray absorptiometry (DXA)-derived fat-free mass (FFM) and lean soft tissue (LST) and estimates from anthropometric equations in individuals with class II obesity or greater. Methods: LST and FFM were measured via DXA in 123 participants with class II obesity or greater. Measurements were performed before and approximately 1 month after BS. Six equations were used to estimate FFM and five to estimate LST. The estimates were calculated at both time points, and changes from pre- to post-BS were compared with the DXA-derived values. Paired t-tests were used to evaluate differences between the predicted and measured values. Results: At the group level, equation for higher body mass index (BMI) values demonstrated the highest agreement with DXA-FFM before surgery (mean difference, 0.01 kg; 95% confidence interval [CI], -0.74 to 0.74), whereas equation for normal BMI values showed the greatest agreement at follow-up (mean difference, 1.71 kg; 95% CI, 1.30 to 2.13). For LST estimation, equation provided the greatest accuracy before BS (mean difference, 0.03 kg; 95% CI, -1.35 to 1.40), and equation achieved the best performance in capturing group-level changes during follow-up (mean difference, 0.48 kg; 95% CI, 0.05 to 0.92). Conclusion: Although certain predictive equations yielded acceptable agreement with DXA at the group level, their performance was inconsistent at the individual level, limiting their clinical utility. Further research is warranted to develop predictive models with enhanced precision and reliability.