In this study, we aimed to investigate the relationship between CT scan-derived muscle morphomics and DEXA-derived LBW in a small cohort of patients with obesity to address the gap in understanding the interchangeability of these two imaging modalities for body composition assessments. We hypothesized that integrating CT morphomics, particularly SMA, with existing body composition equations would improve the precision of LBW estimations compared with relying solely on traditional methods like height and weight. The results confirmed our hypothesis, showing that SMA at L3 improves the prediction of DEXA-derived LBW. Furthermore, incorporating CT-derived SMA into the existing LBW2005 equation improved R2 values for SMA at L3 and T12. This 5–10% improvement in R2 values, compared with the original LBW2005 equation, may be perceived to be small but points to the potential for further improvement with future studies that include larger study sample sizes [31]. Importantly, the use of opportunistic CT data may have some selective advantages over DEXA for LBW measurement. It provides three-dimensional visualization, allowing for precise volume calculations that DEXA cannot match [37]. CT also permits detailed regional analysis of body composition, which is particularly useful for assessing LBW distribution. Additionally, the cross-sectional images of CT enable more accurate distinction between various tissue types (e.g., muscle), especially in complex anatomical regions [38]. These combined features of CT contribute to a more comprehensive and potentially more accurate assessment of LBW compared with DEXA alone. This approach may offer a more refined and personalized method of estimating LBW, surpassing the limitations of methods such as bioimpedance analysis [39].

Incorporating body composition metrics into clinical practice has the potential to refine dosing strategies, reduce the risk of under- or overdosing in patients with obesity. However, the specific body size descriptor is not always clear and the approach is likely to be a lot more nuanced and drug specific [10]. For drugs where LBW is the clear choice, shifting the current paradigm from conventional weight-based dosing to a more personalized and physiologically accurate methodology may be of value. This is particularly relevant because commonly used ABSD are not interchangeable with LBW. Our results demonstrated that both DW and AdjBW exhibited relatively lower R2 values than IBW and the LBW2005 (Table 3).

As noted, LBW has been used as a scalar for drug dosing in patients with obesity. This dosing scalar has been shown to be optimal for drugs like fentanyl, sufentanil, remifentanil, and induction agents such as propofol and thiopental [40]. However, in contrast imaging agents such as iopamidol, LBW may not offer a clear advantage over TBW. This was demonstrated in the trial by Zanardo et al., where dosing iopamidol for abdominal CT based on LBW did not yield more consistent liver contrast enhancement compared with TBW-based dosing [41]. However, as highlighted in a systematic review and meta-analysis by Gulizia et al., LBW-based dosing of contrast agents can lead to a significantly lower contrast volume while maintaining equivalent image quality [42]. These findings suggest that while LBW-based dosing may not always demonstrate superiority over TBW-based dosing, it could offer advantages in optimizing dosing strategies for critical drugs, reducing contrast agent usage, and mitigating associated economic burdens. This warrants further investigation in specific clinical contexts to refine its clinical applicability.

Among several equations developed for estimating LBW, the LBW2005 equation is widely recognized for its stability across different body sizes, including standard, and individuals with mild to severe obesity. McLeay et al. applied the LBW2005 equation in a pharmacokinetic model for propofol and adjusted doses to achieve the recommended amount for a male patient with a body mass index (BMI) of 21.6 kg/m2, successfully predicting plasma propofol levels [43]. In another study by Friesen, lean-scaled weight (LSW) is introduced as an effective weight scalar for dosing in patients with obesity [44]. This approach involves multiplying the LBW2005 equation by a scale factor so that the LSW for a patient with a BMI of 22 equals that patient’s total body weight. LSW remains proportional to LBW and is adjusted to scale accurately across various weights and heights. The scale factors applied to the male and female LBW equations are 1.2332 and 1.5262, respectively.

Given that LSW is a mathematically modified version of LBW2005, we also correlated DEXA-derived LBW with LSW to determine whether this could further improve LBW estimation. Although LSW was identified as a significant variable, the univariate regression analysis showed a lower correlation between DEXA-derived LBW and LSW (R-squared = 0.60) than LBW2005 (R-squared = 0.78). After incorporating SMA into the model, the correlation improved dramatically, achieving values comparable to those obtained with the LBW2005 equation (R-squared = 0.83 at L3 and 0.85 at T12). These findings once again underscore the robustness of the LBW2005 equation in estimating LBW and highlight the additional benefit of integrating CT morphomics for improved precision in LBW estimation.

Furthermore, assessing and monitoring LBW holds significant value, as it can offer pivotal opportunities to identify critically ill patients at high nutritional risk early on and guide metabolic support following ICU admission [45]. Current equations often overestimate LBW in critically ill patients [46]. Recently, the use of advanced imaging technologies such as CT scans to achieve a more precise LBW has gained traction and wider acceptance in the ICU setting. For instance, in a prospective observational study at Massachusetts General Hospital involving 231 surgical ICU patients who underwent a CT scan within 5 days post-extubation, LBW was linked to pneumonia, unfavorable discharge disposition, and 30-day mortality [47]. Notably, CT scans taken during the ICU stay clearly highlighted the negative consequence of the ICU stay on LBW, which must be considered alongside the patient’s pre-ICU condition. Additionally, in a multicenter study conducted at 12 ICUs in North America, Moisey et al. revealed a significant discrepancy between the calculation of LBW using CT imaging and four conventional predictive equations, such as the LBW2005 equation [46]. Therefore, using these equations is discouraged in ICU patients due to overestimating LBW and underestimating nutrition risk. An ICU-specific LBW equation could offer a valuable tool for more accurate identification of patients in need of targeted interventions and enable more precise dosing tailored to their unique needs. The growing interest in utilizing CT scans for estimating LBW in the ICU is reflected in our model, which integrates the CT modality with the established LBW2005 equation. This combination indicates that our model may warrant further evaluation for potential application in critical care settings as well.

To summarize, traditional dosing approaches may often fail to capture the variability in muscle and fat distribution, especially in older or clinically diverse populations. The opportunistic utilization of routinely acquired CT scans for body composition analysis shows promise for improving personalized dosing strategies in clinical practice, particularly for patients with obesity, where accurate LBW estimation is crucial. By leveraging DEXA-derived LBW, which directly reflects the metabolically active portion of body weight, through the LBW2005 equation and CT morphomics we offer a more precise and personalized measure that could improve the precision of body composition estimations and optimize drug dosing. Future research aimed at validating our findings in larger populations, particularly those that benefit more from weight-based dosing, such as elderly patients with obesity, critically ill patients, and others, are acknowledged.

This study has several limitations. First, the sample size is small (n = 63), and these findings require validation in larger target cohorts. This is partly due to the limited availability of patients with both DEXA and CT images and introduces selection biases based on those who have both scans. Second, the cross-sectional design precludes any inference of causality. Third, the study is single-institutional, introducing hospital-based selection bias (Berkson’s bias), which may limit the generalizability of the results to other populations. Next, measurements were available for only four vertebral levels in our dataset, potentially overlooking the significance of measurements at other levels, which may vary by patient characteristics. Additionally, the cohort primarily consisted of female participants with obesity, which may affect the generalizability of the results to broader populations, including males or individuals with different body composition and lower BMI. Furthermore, due to the relatively small sample size, we were unable to perform subgroup analyses stratified by sex, BMI category, or age. Consequently, we could not determine which specific subpopulations may derive the greatest benefit from the improved LBW estimation method.

Despite these limitations, this study provides evidence supporting the incremental value of CT imaging in improving LBW estimation, with potential applications in weight-based medication dosing. Future research should explore the integration of CT-derived morphomic measurements into clinical workflows, particularly for improving body composition assessments in more diverse cohorts with obesity.