Levothyroxine (T4) therapy is essential for patients following total thyroidectomy to achieve target thyrotropin (TSH) levels. It remains a significant challenge, often requiring multiple dose adjustments.1–3 Although conventional LT4 dosing strategies rely primarily on body weight or body mass index (BMI), their predictive accuracy remains suboptimal.3–5 We explored the utility of body fat percentage (BF%) estimated via Universidad Clínica de Navarra-Body Adiposity Estimator (CUN-BAE)6 as a potential alternative parameter to predict LT4 dose requirements in postoperatve hypothyroidism.

From an initial cohort of 484 thyroid surgical patients (235 benign, 249 malignant), a total of 143 thyroidectomized patients were carefully selected. The remaining 341 were excluded as they were lost to follow-up (98; 29%), due to lobectomy (81; 24%), missing data (147; 43%), or failure to reach TSH target (15; 4%). The approval of the corresponding institutional ethics committee was obtained, and current regulations on patient data confidentiality and usage were duly followed. All patients had a preoperative TSH measurement and a follow-up period>6 months. Poisson regression was used to develop and evaluate 2 dosing models: CUN-BAE Model #1 and CUN-BAE Model #2. These models, modified versions of Zaborek's formula with BF% replacing BMI, were assessed for accuracy based on the proportion of correct dose predictions. A prediction was deemed correct when the estimated LT4 dose was within 12.5μg of the patient's actual euthyroid or TSH suppression dose. The model with the highest proportion of correct predictions was considered the most accurate (see Table 1 for details). These models were compared against the widely used weight-based model and the Poisson regression formula proposed by Zaborek.7 The primary endpoint was the proportion of patients whose predicted LT4 dose fell within 12.5μg of their actual therapeutic requirement.

CUN-BAE Model #2 achieved the highest accuracy (62.9%; p=0.038) in predicting LT4 doses within 12.5μg of the therapeutic target, outperforming CUN-BAE Model #1 (61.5%; p=0.057), Zaborek's formula (60.1%) and the weight-based model (55.9%). This superior performance can be attributed to its integration of BF% as a key variable, which more accurately reflects individual variations in body composition and metabolic rate vs BMI or body weight alone. Additionally, incorporating preoperative TSH levels further enhanced the model predictive precision, underscoring its multifaceted approach to optimizing T4 dosing.

Incorporating BF% as a measure of body composition rather than BMI resulted in a statistically significant improvement, underscoring its relevance in refining dosing models. The mean time to reach the therapeutic goal was 24.4 months. This period was notably shorter for malignant cases (15.7 months) vs benign cases (41.2 months).

While BMI has been a cornerstone in obesity-related calculations, BF% offers a more nuanced measure of body composition, directly influencing T4 metabolism and requirements. Specifically, BF% impacts the distribution and metabolism of thyroid hormones by modulating the overall tissue reservoir for hormone storage and altering metabolic demands. Adipose tissue, being less metabolically active than lean mass, affects the basal metabolic rate and subsequently influences T4 dosing needs. Our findings underscore the clinical relevance of BF% over BMI in the context of LT4 therapy, especially given the heterogeneity of body composition across individuals.

The retrospective design and single-center design of our study may limit generalizability. Furthermore, the homogeneity of our cohort may not capture the full spectrum of metabolic and demographic variability present in broader populations. Prospective validation of the CUN-BAE models in diverse cohorts is essential to confirm their clinical utility and explore the potential impact of factors such as ethnicity, age distribution, and comorbidities on model performance.

Replacing BMI with BF% in T4 dose calculation formulas significantly improves dosing accuracy and reduces the adjustment period post-thyroidectomy. Incorporating preoperative TSH levels further enhances model precision, paving the way for more individualized and effective T4 therapy.