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 outcomes after bariatric surgery (BS). This study assessed 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

FFM and LST were measured by DXA in 123 participants with class-II obesity or greater 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, the Li et al. 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 the Li et al. 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, the Salamat et al. equation provided the greatest accuracy before BS (mean-difference, 0.03 kg; 95% CI, -1.35 to 1.40), and the Kulkarni et al. 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 group-level agreement with DXA, their individual-level performance was inconsistent, limiting their clinical utility. Further research is warranted to develop predictive models with enhanced precision and reliability.