Subclinical myocardial dysfunction in pituitary neuroendocrine tumor patients: a 2D speckle-tracking echocardiography study

Pituitary neuroendocrine tumors (PitNETs) cause hormonal hypersecretion, which can disrupt cardiovascular homeostasis and lead to both overt and subclinical cardiac dysfunction. This study retrospectively examined 198 hospitalized PitNET patients (n = 91 male, n = 107 female) from March 2019 to December 2022. Two-dimensional speckle-tracking echocardiography (2D-STE) was used to determine the absolute left ventricular global longitudinal strain (GLS) values. Comprehensive clinical information was gathered, including endocrine axis status, symptoms, surgery history, and demographics. Standard transthoracic echocardiography parameters were also recorded. Multiple linear regression analysis and intergroup comparisons were used to identify factors affecting GLS. Pre-planned subgroup analyses were conducted for the acromegaly cohort and by surgical status. Clinical presentations overlapped across tumor subtypes. Cranial imaging was the primary method for detecting cases with atypical symptoms. Female patients were older and sought treatment more frequently than males. The mean patient age was 43.7 years, with most diagnoses occurring between 30 and 50 years. GLS showed a significant positive correlation with EF (62.64 ± 5.04%; P = 0.00) and E/e' ratio (7.10 ± 1.96; P = 0.01), and a significant negative correlation with BMI (25.20 ± 3.92 kg/m2; P = 0.00). Significant differences in GLS were observed based on gender (t = 2.47, P = 0.01) and operator (t = 2.52, P = 0.01). Regression analysis identified baseline predictors (BMI), EF, LV function, and gender as independent predictors of GLS. A key finding from a dedicated acromegaly subgroup analysis was that disease activity (IGF-1) independently predicted impaired GLS. 2D-STE is a sensitive tool for detecting subclinical myocardial dysfunction in PitNET patients. GLS is significantly influenced by BMI, EF, left ventricular dimensions, and gender, underscoring the need to integrate metabolic and cardiac profiles into the cardiovascular assessment of this population. Furthermore, the identification of IGF-1 as a predictor in acromegaly highlights the value of disease-specific cardiac risk stratification.