Frontiers in Nutrition (Oct 2024)

Subcutaneous adipose tissue measured by computed tomography could be an independent predictor for early outcomes of patients with severe COVID-19

  • Weijian Zhou,
  • Weijian Zhou,
  • Wenqi Shen,
  • Jiajing Ni,
  • Kaiwei Xu,
  • Liu Xu,
  • Chunqu Chen,
  • Ruoyu Wu,
  • Guotian Hu,
  • Jianhua Wang

DOI
https://doi.org/10.3389/fnut.2024.1432251
Journal volume & issue
Vol. 11

Abstract

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BackgroundPatients with severe Coronavirus Disease 2019 (COVID-19) can experience protein loss due to the inflammatory response and energy consumption, impairing immune function. The presence of excessive visceral and heart fat leads to chronic long-term inflammation that can adversely affect immune function and, thus, outcomes for these patients. We aimed to explore the roles of prognostic nutrition index (PNI) and quantitative fat assessment based on computed tomography (CT) scans in predicting the outcomes of patients with severe COVID-19.MethodsA total of 130 patients with severe COVID-19 who were treated between December 1, 2022, and February 28, 2023, were retrospectively enrolled. The patients were divided into survival and death groups. Data on chest CT examinations following admission were collected to measure cardiac adipose tissue (CAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) and to analyze the CT score of pulmonary lesions. Clinical information and laboratory examination data were collected. Univariate and multivariate logistic regression analyses were used to explore the risk factors associated with death, and several multivariate logistic regression models were established.ResultsOf the 130 patients included in the study (median age, 80.5 years; males, 32%), 68 patients died and 62 patients survived. PNI showed a strong association with the outcome of severe COVID-19 (p < 0.001). Among each part of the fat volume obtained based on a CT scan, SAT showed a significant association with the mortality of severe COVID-19 patients (p = 0.007). However, VAT and CAT were not significantly correlated with the death of patients. In the multivariate models, SAT had a higher predictive value than PNI; the area under the curve (AUC) of SAT was 0.844, which was higher than that of PNI (AUC = 0.833), but in the model of the combination of the two indexes, the prediction did not improve (AUC = 0.830), and SAT lost its significance (p = 0.069).ConclusionSubcutaneous adipose tissue measured by computed tomography and PNI were found to be independent predictors of death in patients with severe COVID-19.

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