Sex-based differences in imaging-derived body composition and their association with clinical malnutrition in abdominal surgery patients
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Structured Abstract
Background
Malnutrition significantly impacts surgical outcomes yet is difficult to identify preoperatively. Few studies have investigated the association between comprehensive body composition assessment and malnutrition in males and females separately. This study evaluates sex-specific associations between preoperative imaging-derived body composition features and malnutrition in abdominal surgery patients.
Methods
We retrospectively analyzed patients who underwent computed tomography (CT) scans and elective abdominal surgery at a single institution (2018-2021). Preoperative CT scans were assessed using deep learning to quantify five muscle groups and two fat depots. Malnutrition was diagnosed by registered dietitians using standardized criteria. Sex-specific associations with malnutrition were evaluated using logistic regression.
Results
Among 1,143 patients (52% female), clinical malnutrition was diagnosed in 20.2% of patients, with prevalence varying by procedure type (3.5-38.2%). Malnutrition was associated with reduced muscle volume for both sexes; in contrast, malnutrition was associated with myosteatosis in 3 of 5 muscle groups for females only. In males, malnutrition was associated with decreased psoas volume (OR 0.59 SD, p<0.01), decreased quadratus lumborum volume (OR 0.59 SD, p<0.01), and reduced erector spinae attenuation (OR 0.66 SD, p=0.048). In females, decreased psoas volume (OR 0.55 SD, p<0.001) and attenuation (OR 0.64 SD, p<0.01) were associated with malnutrition. Both sexes demonstrated increased subcutaneous fat attenuation associated with malnutrition (males: OR 1.51 SD, p<0.01; females: OR 1.73 SD, p<0.001), while increased visceral fat attenuation (OR 1.4 SD, p=0.027) was associated with malnutrition only in females.
Conclusions
Males and females differ in baseline body composition and features associated with clinical malnutrition. Comprehensive deep learning analysis of muscle and fat characteristics from cross-sectional imaging provides insight into the sex-specific relationships between body composition and malnutrition in the preoperative setting and provides an opportunity for early identification of patients with greater nutrition-related surgical risk.
